The oldest post I can find for this blog is “From FermiLab Today: Tevatron is Done” at the End of 2011 (but I am not sure if that is the first post, just the oldest I could find.
But the origin goes back to 1985, Timothy Ferris Creation of the Universe PBS, November 20, 1985, available in different videos on YouTube; The Atom Smashers, PBS Frontline November 25, 2008, centered at Fermilab, not available on Youtube; and The Big Bang Machine, with Sir Brian Cox of U Manchester and the ATLAS project at the LHC at CERN.
In 1993, our idiot Congress pulled the plug on The Superconducting Super Collider, a particle accelerator complex under construction in the vicinity of Waxahachie, Texas. Its planned ring circumference was 87.1 kilometers (54.1 mi) with an energy of 20 Tev per proton and was set to be the world’s largest and most energetic. It would have greatly surpassed the current record held by the Large Hadron Collider, which has ring circumference 27 km (17 mi) and energy of 13 TeV per proton. Nobel Laureate Dr. Steven Weinberg, U Texas, has never forgiven the idiots.
If this project had been built, most probably the Higgs Boson would have been found there, not in Europe, to which the USA had ceded High Energy Physics.
The project’s director was Roy Schwitters, a physicist at the University of Texas at Austin. Dr. Louis Ianniello served as its first Project Director for 15 months. The project was cancelled in 1993 due to budget problems, cited as having no immediate economic value.
Somewhere I learned that fully 30% of the scientists working at CERN were U.S. citizens. The ATLAS project had 600 people at Brookhaven Lab. The CMS project had 1,000 people at Fermilab. There were many scientists which had “gigs” at both sites.
I started digging around in CERN web sites and found Quantum Diaries, a “blog” from before there were blogs, where different scientists could post articles. I commented on a few and my dismay about the lack of U.S recognition in the press.
Those guys at Quantum Diaries, gave me access to the Greybook, the list of every institution in the world in several tiers processing data for CERN. I collected all of their social media and was off to the races for CERN and other great basic and applied science.
Since then I have expanded the list of sites that I cover from all over the world. I build html templates for each institution I cover and plop their articles, complete with all attributions and graphics into the template and post it to the blog. I am not a scientist and I am not qualified to write anything or answer scientific questions. The only thing I might add is graphics where the origin graphics are weak. I have a monster graphics library. Any science questions are referred back to the writer who is told to seek his answer from the real scientists in the project.
The blog has to date 1200 followers on the blog, its Facebook Fan page and Twitter.I get my material from email lists and RSS feeds. I do not use Facebook or Twitter, which are both loaded with garbage in the physical sciences.
That is my Origin Story
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You can see all of the subjects and institutions that I cover below in the Categories list, the first list. and most of them in the Links list, so no reason to repeat them. In the Categories, the larger the print, the more the coverage.
richardmitnick
12:15 pm on July 11, 2019 Permalink
| Reply Tags: “Until now there’s been no way to accurately and directly measure drift between building stories” said David McCallen, DDPS leverages a promising new alternative for directly measuring building interstory drift that combines laser beams with optical sensors., Discrete Diode Position Sensor (DDPS) will be deployed for the first time this summer in a multi-story building at Berkeley Lab, Earthquake science, LBNL ( 119 ), Scientists and engineers at Berkeley Lab; Lawrence Livermore National Laboratory; and the University of Nevada-Reno designed an optical method of measuring interstory drift within buildings., This building sits adjacent to the Hayward Fault considered one of the most dangerous faults in the United States.
Berkeley Lab technology could reduce time needed to declare buildings affected by earthquakes safe and sound.
(Credit: iStockphoto)
Last week’s massive southern California earthquakes shut down Ridgecrest Regional Hospital throughout the July 4 holiday weekend while the tiny town of Ridgecrest assessed the damages. A new optical sensor developed at Lawrence Berkeley National Laboratory (Berkeley Lab) could speed up the time it takes to evaluate whether critical buildings like these are safe to occupy shortly after a major earthquake.
The technology – which autonomously captures and transmits data depicting the relative displacement between two adjacent stories of a shaking building – is able to provide reliable information about building damage immediately following an earthquake, and could expedite efforts to safely assess, repair, and reoccupy buildings post-quake.
Scientists and engineers at Berkeley Lab, Lawrence Livermore National Laboratory, and the University of Nevada-Reno began working to design an optical method of measuring interstory drift within buildings in 2015. After four years of extensive peer-reviewed research and simulative testing at the University of Nevada’s Earthquake Engineering Laboratory, the Discrete Diode Position Sensor (DDPS) will be deployed for the first time this summer in a multi-story building at Berkeley Lab – which sits adjacent to the Hayward Fault, considered one of the most dangerous faults in the United States.
“Until now, there’s been no way to accurately and directly measure drift between building stories, which is a key parameter forassessing earthquake demand in a building,” said David McCallen, a senior scientist in the Energy Geosciences Division at Berkeley Lab and faculty member at the University of Nevada, who leads the research collaboration.
The debut of DDPS comes as governments at every level make post-earthquake building inspection and reoccupation a central focus of response planning, and as the highly anticipated next generation of remote connectivity – 5G – becomes reality for rapid data transmission. “We are excited that this sensor technology is now ready for field trials, at a time when post-earthquake response strategies have evolved to prioritize safe, continued building functionality and re-occupancy in addition to ‘life safety,’” McCallen said.
DDPS is a small device that will be positioned between building stories to detect interstory drift and transmit data about building damages to response planners. Its debut comes as governments at every level make post-earthquake building inspection and reoccupation a central focus of response planning, and as the highly anticipated next generation of remote connectivity–5G–becomes reality. (Credit Diana Swantek/Berkeley Lab)
Optics makes a difference in monitoring seismic structural health
Measuring building interstory drift has been a factor in assessing buildings for post-earthquake damage for some time, yet finding a reliable method to do so has been fraught with challenges. Traditionally, engineers mounted strong motion earthquake accelerometers at select elevations to secure data on the back-and-forth and side-to-side force imposed on a shaking building. But processing the acceleration data from these instruments to obtain building drift displacements is very challenging due to the frequency limitations of the sensors, especially when buildings have sustained permanent displacements associated with damage. Even more difficult is receiving data quickly enough to inform decision-making on continuity of operations and occupant safety. In addition, because typical building accelerometer-based instrumentation can be quite costly, systems tend to be very sparse with accelerometers on relatively few buildings.
DDPS leverages a promising new alternative for directly measuring building interstory drift that combines laser beams with optical sensors. This technique centers around projecting laser light across a story height to sense the position at which the light strikes a detector located on the adjacent building floor to directly measure structural drift. The tool developed at Berkeley Lab relies on utilizing a laser source and position sensitive detector. Making use of a geometric array of small, inexpensive light-sensitive photodiodes, the sensor is able to instantly track the position of an impinging laser beam.
A new sensor developed at Lawrence Berkeley National Laboratory combines laser beams with a position sensitive detector to directly measure drift between building stories, an essential part of assessing earthquake damages in a building and deeming them safe to reoccupy. (Credit Diana Swantek/Berkeley Lab)
“Previous generations of DDPS were quite a bit larger than the system we are now able to deploy,” says McCallen. “Based on design advancements and lessons learned, the sensor is a quarter of the size of our original sensor design, but features 92 diodes staggered in a rectangular array so that the laser beam is always on one or more diodes.”
So far, DDPS has held up to three rounds of rigorous experimental shake table testing.
“The rigorous testing the DDPS has undergone indicates how the drift displacements measured on the three testbeds compared to representative drifts that could be achieved on an actual full-scale building undergoing strong shaking from an earthquake,” McCallen said.
Why DDPS is smart for cities
The most populous town affected by the earthquakes in southern California earlier this month was Ridgecrest itself, a city of 29,000 which sits at the epicenter of a magnitude 7.1 earthquake which took place on July 5. Even though this is a small population center, the building damage estimates are still in the $100-million range.
If an earthquake of that magnitude were to hit Los Angeles 150 miles to the south of tiny Ridgecrest, or San Francisco, nearly 400 miles north, literally hundreds to thousands of buildings would be at stake for damage. In that scenario, the ability to measure and display key interstory drift information immediately after an earthquake would provide critical new data for making informed decisions on building occupancy – giving first responders information to help guide their efforts to evacuate a building, and municipalities the potential to maintain functional use of important facilities such as hospitals.
In addition, understanding a building’s drift profile would allow a quick determination of building damage potential, letting building inspectors know where to look for potential damage. This will be an important capability in moving beyond time-consuming and challenging manual inspections of hundreds of buildings after the next major urban earthquake.
McCallen noted, “The major earthquakes that struck in southern California this past week serve as a reminder of the risks associated with seismic activity across many regions of the United States. These events put an exclamation point on the need for continued societal focus on earthquake readiness and resilience, including an ability to provide the sensors and data analysis that can rapidly measure infrastructure health and inform the most effective response after the next major quake.”
This research was funded by the U.S. Department of Energy’s (DOE) Nuclear Safety Research and Development (NSR&D) Program managed by the Office of Nuclear Safety within the DOE Office of Environment, Health, Safety and Security. An objective of the NSR&D program is to establish an enduring Departmental commitment and capability to utilize NSR&D in preventing and/or reducing high consequence-low probability hazards and risks posed by DOE and NNSA nuclear facilities, operations, nuclear explosives, and environmental restoration activities.
In the world of science, Lawrence Berkeley National Laboratory (Berkeley Lab) is synonymous with “excellence.” Thirteen Nobel prizes are associated with Berkeley Lab. Seventy Lab scientists are members of the National Academy of Sciences (NAS), one of the highest honors for a scientist in the United States. Thirteen of our scientists have won the National Medal of Science, our nation’s highest award for lifetime achievement in fields of scientific research. Eighteen of our engineers have been elected to the National Academy of Engineering, and three of our scientists have been elected into the Institute of Medicine. In addition, Berkeley Lab has trained thousands of university science and engineering students who are advancing technological innovations across the nation and around the world.
Berkeley Lab is a member of the national laboratory system supported by the U.S. Department of Energy through its Office of Science. It is managed by the University of California (UC) and is charged with conducting unclassified research across a wide range of scientific disciplines. Located on a 202-acre site in the hills above the UC Berkeley campus that offers spectacular views of the San Francisco Bay, Berkeley Lab employs approximately 3,232 scientists, engineers and support staff. The Lab’s total costs for FY 2014 were $785 million. A recent study estimates the Laboratory’s overall economic impact through direct, indirect and induced spending on the nine counties that make up the San Francisco Bay Area to be nearly $700 million annually. The Lab was also responsible for creating 5,600 jobs locally and 12,000 nationally. The overall economic impact on the national economy is estimated at $1.6 billion a year. Technologies developed at Berkeley Lab have generated billions of dollars in revenues, and thousands of jobs. Savings as a result of Berkeley Lab developments in lighting and windows, and other energy-efficient technologies, have also been in the billions of dollars.
Berkeley Lab was founded in 1931 by Ernest Orlando Lawrence, a UC Berkeley physicist who won the 1939 Nobel Prize in physics for his invention of the cyclotron, a circular particle accelerator that opened the door to high-energy physics. It was Lawrence’s belief that scientific research is best done through teams of individuals with different fields of expertise, working together. His teamwork concept is a Berkeley Lab legacy that continues today.
Written by Cynthia Eller
Contact:
Deborah Williams-Hedges
(626) 395-3227 debwms@caltech.edu
A portion of the Golden State Freeway in Gavin Canyon that collapsed during the 1994 Northridge earthquake. Credit: FEMA
Current Earthquake Research at Caltech
Since the magnitude 6.7 Northridge earthquake 20 years ago (January 17, 1994), researchers at the California Institute of Technology (Caltech) have learned much more about where earthquakes are likely to happen, and how danger to human life and damage to property might be mitigated when they do occur.
“The Northridge quake really heralded the beginning of a new era in earthquake research, not only in southern California, but worldwide,” says Michael Gurnis, John E. and Hazel S. Smits Professor of Geophysics, and director of the Seismological Laboratory at Caltech.
In the years just prior to the Northridge earthquake, Caltech launched a program called TERRAscope supported by the Whittier foundations, which placed high-quality seismic sensors near where earthquakes occur. The Northridge earthquake was, in effect, the first test of TERRAscope in which Caltech scientists could infer the distribution of an earthquake rupture on subsurface faults and directly measure the associated motion of the ground with greater accuracy. “With a modern digital seismic network, the potential of measuring ground shaking in real time presented itself,” says Gurnis. “The real time view also gave first responders detailed maps of ground shaking so that they could respond to those in need immediately after a quake,” adds Egill Hauksson, senior research associate at Caltech.
To give us this new view of earthquakes, Caltech collaborated with the U.S. Geological Survey (USGS) and the California Geological Survey to form TriNet, through which a vastly expanded network of instrumentation was put in place across southern California. Concurrently, a new network of continuously operated GPS stations was permanently deployed by a group of geophysicists under the auspices of the Southern California Earthquake Center, funded by the USGS, NASA, NSF, and the Keck Foundation. GPS data are used to measure displacements as small as 1 millimeter per year between stations at any two locations, making it possible to track motions during, between, and after earthquakes. Similar and even larger networks of seismometers and GPS sensors have now been deployed across the United States, especially EarthScope, supported by the NSF, and in countries around the world by various respective national agencies like the networks deployed by the Japanese government.
Initially, says Gurnis, there were not many large earthquakes to track with the new dense network of broadband seismic instruments and GPS devices. That all changed in December 2004 with the magnitude 9.3 earthquake and resulting tsunami that struck the Indian Ocean off the west coast of Sumatra, Indonesia. Quite abruptly, Caltech scientists had an enormous amount of information coming in from the instrumentation in Indonesia previously deployed by the Caltech Techtonics Observatory with support from the Gordon and Betty Moore Foundation. By the time the magnitude 9.0 Tohoku-Oki earthquake hit northern Japan in 2011, the Seismological Laboratory at Caltech had developed greatly expanded computing power capable of ingesting massive amounts of seismic and geodetic data. Within weeks of the disaster, a team led by Caltech professor of geophysics Mark Simons using data from GPS systems installed by the Japanese had produced extensive measurements of ground motion, as well as earthquake models constrained by this data, that provided new insight into the mechanics of plate tectonics and fault ruptures.
The Tohoku-Oki earthquake was unprecedented: scientists estimate that over 50 meters of slip on the subsurface fault occurred during the devastating earthquake. Currently, scientists at Caltech and the Jet Propulsion Laboratory are prototyping new automated systems for exploiting the wealth of GPS and satellite imaging data to rapidly provide disaster assessment and situational awareness as events occur around the globe. “We are now at a juncture in time where new observational capabilities and available computational power will allow us to provide critical information with unprecedented speed and resolution,” says Simons.
Earthquakes are notable—and, for many, particularly upsetting—because they have always come without warning. Earthquakes do in fact happen quickly and unpredictably, but not so much so that early-warning systems are impossible. In a Moore Foundation-supported collaboration with UC Berkeley, the University of Washington, and the USGS, Caltech is developing a prototype early-warning system that may provide seconds to tens of seconds of warning to people in areas about to experience ground shaking, and minutes of warning to people potentially in the path of a tsunami. Japan invested heavily in an earthquake early-warning system after the magnitude 6.9 Kobe earthquake that occurred January 17, 1995, on the one-year anniversary of the Northridge earthquake, and the system performed well during the Tohoku-Oki earthquake. “It was a major scientific and technological accomplishment,” says Gurnis. “High-speed rail trains slowed and stopped as earthquake warnings came in, and there were no derailments as a result of the quake.”
Closer to home, Caltech professor of geophysics Robert Clayton has aided local earthquake detection by distributing wallet-sized seismometers to residents of the greater Pasadena area to keep in their homes. The seismometers are attached to a USB drive on each resident’s computer, which is to remain on at all times. The data from these seismometers serve two functions: they record seismic activity on a detailed block-by-block scale, and, in the event of a large earthquake, they can help identify areas that are hardest hit. One lesson learned in the Northridge earthquake was that serious damage can occur far from the epicenter of an earthquake. The presence of many seismometers could help first responders to find the worst-affected areas more quickly after an earthquake strikes.
Caltech scientists have also been playing a leading role in the large multi-institutional Salton Seismic Imaging Project. The project is mapping the San Andreas fault and discovering additional faults by setting off underground explosions and underwater bursts of compressed air and then measuring the transmission of the resulting sound waves and vibrations through sediment. According to Joann Stock, professor of geology and geophysics at Caltech, knowing the geometry of faults and the composition of nearby sediments informs our understanding of the types of earthquakes that will occur in the future, and the reaction of the local sediment to ground shaking.
In addition, Caltech scientists learned much through simulating—via both computer modeling and physical modeling techniques—how earthquakes occur and what they leave in their aftermath.
Computer simulations of how buildings respond during earthquakes recently allowed Caltech professors Thomas Heaton, professor of engineering seismology, and John Hall, professor of civil engineering, to estimate the decrease in building safety caused by the existence of defective welds in steel-frame structures, a problem identified after the Northridge earthquake. Researchers simulated the behavior of different 6- and 20-story building models in a variety of potential earthquake scenarios created by the Southern California Earthquake Center for the Los Angeles and San Francisco areas. The study showed that defective welds make a building significantly more susceptible to collapse and irreparable damage, and also found that stiffer, higher-strength buildings perform better than more flexible, lower-strength designs.
Caltech professor of mechanical engineering and geophysics Nadia Lapusta recently used computer simulations of numerous earthquakes to determine what role “creeping” fault slip might play in earthquake events. It has been known for some time that, in addition to the rapid displacements that trigger earthquakes, land also slips very slowly along fault lines, a process that was thought to stop incoming earthquake rupture. Instead, Lapusta’s models show that these “stable segments” may become seismically active in an earthquake, accelerating and even strengthening its motions. Lapusta hypothesizes that this was one factor behind the severity of the 2011 Tohoku-Oki earthquake. Taking advantage of advances in computer modeling, Lapusta and her colleague Jean-Philippe Avouac, Earle C. Anthony Professor of Geology at Caltech, have created a comprehensive model of a fault zone, including both its earthquake activity and its behavior in seismically quiet times.
Physical modeling of earthquakes is carried out at Caltech via collaborative efforts between the Divisions of Geological and Planetary Sciences and of Engineering and Applied Science. A series of experiments conducted by Ares Rosakis, the Theodore von Kármán Professor of Aeronautics and Mechanical Engineering, and collaborators including Lapusta and Hiroo Kanamori, the John E. and Hazel S. Smits Professor of Geophysics, Emeritus, used polymer plates to simulate land masses. Stresses were then created at various angles to the fault lines between the plates to set off earthquake-like activity. The motion in the polymer plates was measured by laser vibrometers while a high-speed camera recorded the movements in detail, yielding unprecedented data on the propagation of seismic waves. Researchers learned that strike-slip faults like the San Andreas may rupture in more than one direction (it was previously believed that these faults had a preferred direction), and that in addition to sliding along a fault, ruptures may occur in a “self-healing” pulselike manner in which a seismic wave “crawls” down a fault line. A third study drew conclusions about how faults will behave—in either a classic cracklike sliding rupture or in a pulselike rupture—depending on the angle at which compression forces strike the fault.
“Northridge was a devastating earthquake for Los Angeles, and there was a massive amount of damage,” Gurnis says, “But in some sense, we stepped up to the plate after Northridge to determine what we could do better. And as a result we have ushered in an era of dense, high-fidelity geophysical networks on top of hazardous faults. We’ve exploited these networks to better understand how earthquakes occur, and we’ve pushed the limits such that we are now at the dawn of a new era of earthquake early warning in the United States. That’s because of Northridge.”
Earthquake Network projectEarthquake Network is a research project which aims at developing and maintaining a crowdsourced smartphone-based earthquake warning system at a global level. Smartphones made available by the population are used to detect the earthquake waves using the on-board accelerometers. When an earthquake is detected, an earthquake warning is issued in order to alert the population not yet reached by the damaging waves of the earthquake.
The project started on January 1, 2013 with the release of the homonymous Android application Earthquake Network. The author of the research project and developer of the smartphone application is Francesco Finazzi of the University of Bergamo, Italy.
Get the app in the Google Play store.
Smartphone network spatial distribution (green and red dots) on December 4, 2015
The Quake-Catcher Network is a collaborative initiative for developing the world’s largest, low-cost strong-motion seismic network by utilizing sensors in and attached to internet-connected computers. With your help, the Quake-Catcher Network can provide better understanding of earthquakes, give early warning to schools, emergency response systems, and others. The Quake-Catcher Network also provides educational software designed to help teach about earthquakes and earthquake hazards.
After almost eight years at Stanford, and a year at CalTech, the QCN project is moving to the University of Southern California Dept. of Earth Sciences. QCN will be sponsored by the Incorporated Research Institutions for Seismology (IRIS) and the Southern California Earthquake Center (SCEC).
The Quake-Catcher Network is a distributed computing network that links volunteer hosted computers into a real-time motion sensing network. QCN is one of many scientific computing projects that runs on the world-renowned distributed computing platform Berkeley Open Infrastructure for Network Computing (BOINC).
The volunteer computers monitor vibrational sensors called MEMS accelerometers, and digitally transmit “triggers” to QCN’s servers whenever strong new motions are observed. QCN’s servers sift through these signals, and determine which ones represent earthquakes, and which ones represent cultural noise (like doors slamming, or trucks driving by).
There are two categories of sensors used by QCN: 1) internal mobile device sensors, and 2) external USB sensors.
Mobile Devices: MEMS sensors are often included in laptops, games, cell phones, and other electronic devices for hardware protection, navigation, and game control. When these devices are still and connected to QCN, QCN software monitors the internal accelerometer for strong new shaking. Unfortunately, these devices are rarely secured to the floor, so they may bounce around when a large earthquake occurs. While this is less than ideal for characterizing the regional ground shaking, many such sensors can still provide useful information about earthquake locations and magnitudes.
USB Sensors: MEMS sensors can be mounted to the floor and connected to a desktop computer via a USB cable. These sensors have several advantages over mobile device sensors. 1) By mounting them to the floor, they measure more reliable shaking than mobile devices. 2) These sensors typically have lower noise and better resolution of 3D motion. 3) Desktops are often left on and do not move. 4) The USB sensor is physically removed from the game, phone, or laptop, so human interaction with the device doesn’t reduce the sensors’ performance. 5) USB sensors can be aligned to North, so we know what direction the horizontal “X” and “Y” axes correspond to.
If you are a science teacher at a K-12 school, please apply for a free USB sensor and accompanying QCN software. QCN has been able to purchase sensors to donate to schools in need. If you are interested in donating to the program or requesting a sensor, click here.
Earthquake safety is a responsibility shared by billions worldwide. The Quake-Catcher Network (QCN) provides software so that individuals can join together to improve earthquake monitoring, earthquake awareness, and the science of earthquakes. The Quake-Catcher Network (QCN) links existing networked laptops and desktops in hopes to form the worlds largest strong-motion seismic network.
Below, the QCN Quake Catcher Network map
ShakeAlert: An Earthquake Early Warning System for the West Coast of the United States
The U. S. Geological Survey (USGS) along with a coalition of State and university partners is developing and testing an earthquake early warning (EEW) system called ShakeAlert for the west coast of the United States. Long term funding must be secured before the system can begin sending general public notifications, however, some limited pilot projects are active and more are being developed. The USGS has set the goal of beginning limited public notifications in 2018.
Watch a video describing how ShakeAlert works in English or Spanish.
Earthquakes pose a national challenge because more than 143 million Americans live in areas of significant seismic risk across 39 states. Most of our Nation’s earthquake risk is concentrated on the West Coast of the United States. The Federal Emergency Management Agency (FEMA) has estimated the average annualized loss from earthquakes, nationwide, to be $5.3 billion, with 77 percent of that figure ($4.1 billion) coming from California, Washington, and Oregon, and 66 percent ($3.5 billion) from California alone. In the next 30 years, California has a 99.7 percent chance of a magnitude 6.7 or larger earthquake and the Pacific Northwest has a 10 percent chance of a magnitude 8 to 9 megathrust earthquake on the Cascadia subduction zone.
Part of the Solution
Today, the technology exists to detect earthquakes, so quickly, that an alert can reach some areas before strong shaking arrives. The purpose of the ShakeAlert system is to identify and characterize an earthquake a few seconds after it begins, calculate the likely intensity of ground shaking that will result, and deliver warnings to people and infrastructure in harm’s way. This can be done by detecting the first energy to radiate from an earthquake, the P-wave energy, which rarely causes damage. Using P-wave information, we first estimate the location and the magnitude of the earthquake. Then, the anticipated ground shaking across the region to be affected is estimated and a warning is provided to local populations. The method can provide warning before the S-wave arrives, bringing the strong shaking that usually causes most of the damage.
Studies of earthquake early warning methods in California have shown that the warning time would range from a few seconds to a few tens of seconds. ShakeAlert can give enough time to slow trains and taxiing planes, to prevent cars from entering bridges and tunnels, to move away from dangerous machines or chemicals in work environments and to take cover under a desk, or to automatically shut down and isolate industrial systems. Taking such actions before shaking starts can reduce damage and casualties during an earthquake. It can also prevent cascading failures in the aftermath of an event. For example, isolating utilities before shaking starts can reduce the number of fire initiations.
System Goal
The USGS will issue public warnings of potentially damaging earthquakes and provide warning parameter data to government agencies and private users on a region-by-region basis, as soon as the ShakeAlert system, its products, and its parametric data meet minimum quality and reliability standards in those geographic regions. The USGS has set the goal of beginning limited public notifications in 2018. Product availability will expand geographically via ANSS regional seismic networks, such that ShakeAlert products and warnings become available for all regions with dense seismic instrumentation.
Current Status
The West Coast ShakeAlert system is being developed by expanding and upgrading the infrastructure of regional seismic networks that are part of the Advanced National Seismic System (ANSS); the California Integrated Seismic Network (CISN) is made up of the Southern California Seismic Network, SCSN) and the Northern California Seismic System, NCSS and the Pacific Northwest Seismic Network (PNSN). This enables the USGS and ANSS to leverage their substantial investment in sensor networks, data telemetry systems, data processing centers, and software for earthquake monitoring activities residing in these network centers. The ShakeAlert system has been sending live alerts to “beta” users in California since January of 2012 and in the Pacific Northwest since February of 2015.
In February of 2016 the USGS, along with its partners, rolled-out the next-generation ShakeAlert early warning test system in California joined by Oregon and Washington in April 2017. This West Coast-wide “production prototype” has been designed for redundant, reliable operations. The system includes geographically distributed servers, and allows for automatic fail-over if connection is lost.
This next-generation system will not yet support public warnings but does allow selected early adopters to develop and deploy pilot implementations that take protective actions triggered by the ShakeAlert notifications in areas with sufficient sensor coverage.
Authorities
The USGS will develop and operate the ShakeAlert system, and issue public notifications under collaborative authorities with FEMA, as part of the National Earthquake Hazard Reduction Program, as enacted by the Earthquake Hazards Reduction Act of 1977, 42 U.S.C. §§ 7704 SEC. 2.
For More Information
Robert de Groot, ShakeAlert National Coordinator for Communication, Education, and Outreach rdegroot@usgs.gov
626-583-7225
The California Institute of Technology (commonly referred to as Caltech) is a private research university located in Pasadena, California, United States. Caltech has six academic divisions with strong emphases on science and engineering. Its 124-acre (50 ha) primary campus is located approximately 11 mi (18 km) northeast of downtown Los Angeles. “The mission of the California Institute of Technology is to expand human knowledge and benefit society through research integrated with education. We investigate the most challenging, fundamental problems in science and technology in a singularly collegial, interdisciplinary atmosphere, while educating outstanding students to become creative members of society.”
Countless dollars and entire scientific careers have been dedicated to predicting where and when the next big earthquake will strike. But unlike weather forecasting, which has significantly improved with the use of better satellites and more powerful mathematical models, earthquake prediction has been marred by repeated failure.
Some of the world’s most destructive earthquakes — China in 2008, Haiti in 2010 and Japan in 2011, among them — occurred in areas that seismic hazard maps had deemed relatively safe. The last large earthquake to strike Los Angeles, Northridge in 1994, occurred on a fault that did not appear on seismic maps.
Now, with the help of artificial intelligence, a growing number of scientists say changes in the way they can analyze massive amounts of seismic data can help them better understand earthquakes, anticipate how they will behave, and provide quicker and more accurate early warnings.
“I am actually hopeful for the first time in my career that we will make progress on this problem,” said Paul Johnson, a fellow at the Los Alamos National Laboratory who is among those at the forefront of this research.
Well aware of past earthquake prediction failures, scientists are cautious when asked how much progress they have made using A.I. Some in the field refer to prediction as “the P word,” because they do not even want to imply it is possible. But one important goal, they say, is to be able to provide reliable forecasts.
The earthquake probabilities that are provided on seismic hazard maps, for example, have crucial consequences, most notably in instructing engineers how they should construct buildings. Critics say these maps are remarkably inexact.
A map of Los Angeles lists the probability of an earthquake producing strong shaking within a given period of time — usually 50 years. That is based on a complex formula that takes into account, among other things, the distance from a fault, how fast one side of a fault is moving past the other, and the recurrence of earthquakes in the area.
A study led by Katherine M. Scharer, a geologist with the United States Geological Survey, estimated dates for nine previous earthquakes along the Southern California portion of the San Andreas fault dating back to the eighth century. The last big earthquake on the San Andreas was in 1857.
Since the average interval between these big earthquakes was 135 years, a common interpretation is that Southern California is due for a big earthquake. Yet the intervals between earthquakes are so varied — ranging from 44 years to 305 years — that taking the average is not a very useful prediction tool. A big earthquake could come tomorrow, or it could come in a century and a half or more.
This is one of the criticisms of Philip Stark, an associate dean at the University of California, Berkeley, at the Division of Mathematical and Physical Sciences. Dr. Stark describes the overall system of earthquake probabilities as “somewhere between meaningless and misleading” and has called for it to be scrapped.
The new A.I.-related earthquake research is leaning on neural networks, the same technology that has accelerated the progress of everything from talking digital assistants to driverless cars. Loosely modeled on the web of neurons in the human brain, a neural network is a complex mathematical system that can learn tasks on its own.
Scientists say seismic data is remarkably similar to the audio data that companies like Google and Amazon use in training neural networks to recognize spoken commands on coffee-table digital assistants like Alexa. When studying earthquakes, it is the computer looking for patterns in mountains of data rather than relying on the weary eyes of a scientist.
“Rather than a sequence of words, we have a sequence of ground-motion measurements,” said Zachary Ross, a researcher in the California Institute of Technology’s Seismological Laboratory who is exploring these A.I. techniques. “We are looking for the same kinds of patterns in this data.”
Brendan Meade, a professor of earth and planetary sciences at Harvard, began exploring these techniques after spending a sabbatical at Google, a company at the forefront of A.I. research.
His first project showed that, at the very least, these machine-learning methods could significantly accelerate his experiments. He and his graduate students used a neural network to run an earthquake analysis 500 times faster than they could in the past. What once took days now took minutes.
Dr. Meade also found that these A.I. techniques could lead to new insights. In the fall, with other researchers from Google and Harvard, he published a paper showing how neural networks can forecast earthquake aftershocks. This kind of project, he believes, represents an enormous shift in the way earthquake science is done. Similar work is underway at places like Caltech and Stanford University.
“We are at a point where the technology can do as well as — or better than — human experts,” Dr. Ross said.
Driving that guarded optimism is the belief that as sensors get smaller and cheaper, scientists will be able to gather larger amounts of seismic data. With help from neural networks and similar A.I. techniques, they hope to glean new insights from all this data.
Dr. Ross and other Caltech researchers are using these techniques to build systems that can more accurately recognize earthquakes as they are happening and anticipate where the epicenter is and where the shaking will spread.
Japan and Mexico have early warning systems, and California just rolled out its own. But scientists say artificial intelligence could greatly improve their accuracy, helping predict the direction and intensity of a rupture in the earth’s crust and providing earlier warnings to hospitals and other institutions that could benefit from a few extra seconds of preparation.
“The more detail you have, the better your forecasts will be,” Dr. Ross said.
Scientists working on these projects said neural networks have their limits. Though they are good at finding familiar signals in data, they are not necessarily suited to finding new kinds of signals — like the sounds tectonic plates make as they grind together.
But at Los Alamos, Dr. Johnson and his colleagues have shown that a machine-learning technique called “random forests” can identify previously unknown signals in a simulated fault created inside a lab. In one case, their system showed that a particular sound made by the fault, which scientists previously thought was meaningless, was actually an indication of when an earthquake would arrive.
Some scientists, like Robert Geller, a seismologist at the University of Tokyo, are unconvinced that A.I. will improve earthquake forecasts. He questions the very premise that past earthquakes can predict future ones. And ultimately, he said, we would only know the effectiveness of A.I. forecasting when earthquakes can be predicted beyond random chance.
“There are no shortcuts,” Dr. Geller said. “If you cannot predict the future, then your hypothesis is wrong.”
Earthquake Network projectEarthquake Network is a research project which aims at developing and maintaining a crowdsourced smartphone-based earthquake warning system at a global level. Smartphones made available by the population are used to detect the earthquake waves using the on-board accelerometers. When an earthquake is detected, an earthquake warning is issued in order to alert the population not yet reached by the damaging waves of the earthquake.
The project started on January 1, 2013 with the release of the homonymous Android application Earthquake Network. The author of the research project and developer of the smartphone application is Francesco Finazzi of the University of Bergamo, Italy.
Get the app in the Google Play store.
Smartphone network spatial distribution (green and red dots) on December 4, 2015
The Quake-Catcher Network is a collaborative initiative for developing the world’s largest, low-cost strong-motion seismic network by utilizing sensors in and attached to internet-connected computers. With your help, the Quake-Catcher Network can provide better understanding of earthquakes, give early warning to schools, emergency response systems, and others. The Quake-Catcher Network also provides educational software designed to help teach about earthquakes and earthquake hazards.
After almost eight years at Stanford, and a year at CalTech, the QCN project is moving to the University of Southern California Dept. of Earth Sciences. QCN will be sponsored by the Incorporated Research Institutions for Seismology (IRIS) and the Southern California Earthquake Center (SCEC).
The Quake-Catcher Network is a distributed computing network that links volunteer hosted computers into a real-time motion sensing network. QCN is one of many scientific computing projects that runs on the world-renowned distributed computing platform Berkeley Open Infrastructure for Network Computing (BOINC).
The volunteer computers monitor vibrational sensors called MEMS accelerometers, and digitally transmit “triggers” to QCN’s servers whenever strong new motions are observed. QCN’s servers sift through these signals, and determine which ones represent earthquakes, and which ones represent cultural noise (like doors slamming, or trucks driving by).
There are two categories of sensors used by QCN: 1) internal mobile device sensors, and 2) external USB sensors.
Mobile Devices: MEMS sensors are often included in laptops, games, cell phones, and other electronic devices for hardware protection, navigation, and game control. When these devices are still and connected to QCN, QCN software monitors the internal accelerometer for strong new shaking. Unfortunately, these devices are rarely secured to the floor, so they may bounce around when a large earthquake occurs. While this is less than ideal for characterizing the regional ground shaking, many such sensors can still provide useful information about earthquake locations and magnitudes.
USB Sensors: MEMS sensors can be mounted to the floor and connected to a desktop computer via a USB cable. These sensors have several advantages over mobile device sensors. 1) By mounting them to the floor, they measure more reliable shaking than mobile devices. 2) These sensors typically have lower noise and better resolution of 3D motion. 3) Desktops are often left on and do not move. 4) The USB sensor is physically removed from the game, phone, or laptop, so human interaction with the device doesn’t reduce the sensors’ performance. 5) USB sensors can be aligned to North, so we know what direction the horizontal “X” and “Y” axes correspond to.
If you are a science teacher at a K-12 school, please apply for a free USB sensor and accompanying QCN software. QCN has been able to purchase sensors to donate to schools in need. If you are interested in donating to the program or requesting a sensor, click here.
Earthquake safety is a responsibility shared by billions worldwide. The Quake-Catcher Network (QCN) provides software so that individuals can join together to improve earthquake monitoring, earthquake awareness, and the science of earthquakes. The Quake-Catcher Network (QCN) links existing networked laptops and desktops in hopes to form the worlds largest strong-motion seismic network.
Below, the QCN Quake Catcher Network map
ShakeAlert: An Earthquake Early Warning System for the West Coast of the United States
The U. S. Geological Survey (USGS) along with a coalition of State and university partners is developing and testing an earthquake early warning (EEW) system called ShakeAlert for the west coast of the United States. Long term funding must be secured before the system can begin sending general public notifications, however, some limited pilot projects are active and more are being developed. The USGS has set the goal of beginning limited public notifications in 2018.
Watch a video describing how ShakeAlert works in English or Spanish.
Earthquakes pose a national challenge because more than 143 million Americans live in areas of significant seismic risk across 39 states. Most of our Nation’s earthquake risk is concentrated on the West Coast of the United States. The Federal Emergency Management Agency (FEMA) has estimated the average annualized loss from earthquakes, nationwide, to be $5.3 billion, with 77 percent of that figure ($4.1 billion) coming from California, Washington, and Oregon, and 66 percent ($3.5 billion) from California alone. In the next 30 years, California has a 99.7 percent chance of a magnitude 6.7 or larger earthquake and the Pacific Northwest has a 10 percent chance of a magnitude 8 to 9 megathrust earthquake on the Cascadia subduction zone.
Part of the Solution
Today, the technology exists to detect earthquakes, so quickly, that an alert can reach some areas before strong shaking arrives. The purpose of the ShakeAlert system is to identify and characterize an earthquake a few seconds after it begins, calculate the likely intensity of ground shaking that will result, and deliver warnings to people and infrastructure in harm’s way. This can be done by detecting the first energy to radiate from an earthquake, the P-wave energy, which rarely causes damage. Using P-wave information, we first estimate the location and the magnitude of the earthquake. Then, the anticipated ground shaking across the region to be affected is estimated and a warning is provided to local populations. The method can provide warning before the S-wave arrives, bringing the strong shaking that usually causes most of the damage.
Studies of earthquake early warning methods in California have shown that the warning time would range from a few seconds to a few tens of seconds. ShakeAlert can give enough time to slow trains and taxiing planes, to prevent cars from entering bridges and tunnels, to move away from dangerous machines or chemicals in work environments and to take cover under a desk, or to automatically shut down and isolate industrial systems. Taking such actions before shaking starts can reduce damage and casualties during an earthquake. It can also prevent cascading failures in the aftermath of an event. For example, isolating utilities before shaking starts can reduce the number of fire initiations.
System Goal
The USGS will issue public warnings of potentially damaging earthquakes and provide warning parameter data to government agencies and private users on a region-by-region basis, as soon as the ShakeAlert system, its products, and its parametric data meet minimum quality and reliability standards in those geographic regions. The USGS has set the goal of beginning limited public notifications in 2018. Product availability will expand geographically via ANSS regional seismic networks, such that ShakeAlert products and warnings become available for all regions with dense seismic instrumentation.
Current Status
The West Coast ShakeAlert system is being developed by expanding and upgrading the infrastructure of regional seismic networks that are part of the Advanced National Seismic System (ANSS); the California Integrated Seismic Network (CISN) is made up of the Southern California Seismic Network, SCSN) and the Northern California Seismic System, NCSS and the Pacific Northwest Seismic Network (PNSN). This enables the USGS and ANSS to leverage their substantial investment in sensor networks, data telemetry systems, data processing centers, and software for earthquake monitoring activities residing in these network centers. The ShakeAlert system has been sending live alerts to “beta” users in California since January of 2012 and in the Pacific Northwest since February of 2015.
In February of 2016 the USGS, along with its partners, rolled-out the next-generation ShakeAlert early warning test system in California joined by Oregon and Washington in April 2017. This West Coast-wide “production prototype” has been designed for redundant, reliable operations. The system includes geographically distributed servers, and allows for automatic fail-over if connection is lost.
This next-generation system will not yet support public warnings but does allow selected early adopters to develop and deploy pilot implementations that take protective actions triggered by the ShakeAlert notifications in areas with sufficient sensor coverage.
Authorities
The USGS will develop and operate the ShakeAlert system, and issue public notifications under collaborative authorities with FEMA, as part of the National Earthquake Hazard Reduction Program, as enacted by the Earthquake Hazards Reduction Act of 1977, 42 U.S.C. §§ 7704 SEC. 2.
For More Information
Robert de Groot, ShakeAlert National Coordinator for Communication, Education, and Outreach rdegroot@usgs.gov
626-583-7225
Jonathan Stewart, a professor in the UCLA Department of Civil and Environmental Engineering, at a Los Angeles Department of Water and Power facility.
It’s a scene of post-mayhem disaster. In front of the Acacia residential building on the west end of the UCLA campus. Victims are everywhere, bleeding, confused, in and out of consciousness. A small boy in a baseball hat and shorts is laid out on a red tarp. “Very low pulse,” says one of the people who helped carry him over, before rushing back to the search and rescue. It’s hard to tell if anyone hears her, given the commotion. Nearby, a woman sits upright, a drop of blood rolling out of her ear and down her cheek, and another woman props her bloodied leg inside a makeshift cardboard splint.
A few dozen first responders move victims onto colorcoded tarps — green for the most stable, yellow for those in need of a medic and red for the most critical. One of the vested first responders kneels beside the boy to check his pulse, and quickly stands up again. “We have a dead over here,” she calls out. But there’s no time to stop.
This is the aftermath of a 6.8 magnitude earthquake centered on the Santa Monica Fault just south of campus. It’s the “big one” that Southern Californians had known could one day happen. That day is today.
Except it’s not. The “victims” are all actors, the injuries painted on and the small boy alive and well. The first responders are volunteers from the Community Emergency Response Team, running a drill to test emergency response procedures on campus.
While this 6.8 quake didn’t actually happen, through the work of researchers and scientists across UCLA, we know with certainty the probable impact of such a temblor, how to warn those who would feel its shaking, how to plan around its destructive power and even how to ensure that buildings like the Acacia dorms don’t fall. From the deepest motions of our planet’s structure to the foundations of our buildings to the crucial urban systems underpinning modern society, UCLA research is increasing our understanding of how the land beneath us moves and how to survive a major quake.
It’s estimated that up to 3,000 people died in San Francisco in 1906 as a result of the 7.9 magnitude quake, and more than 140,000 died in the 1923 Great Kanto earthquake in Japan. Fortunately, in more recent years, particularly in the United States, earthquake-caused deaths have been relatively rare. Unlike in the past, when buildings crumbled and crushed the people inside, we now know how to construct buildings that can withstand quakes.
We learned from buildings that fell. In 1994, a 6.7 magnitude earthquake that struck in the San Fernando Valley destroyed or significantly damaged an estimated 90,000 buildings. Of the approximately 60 people killed, 33 were in buildings that fell. The most common were small apartment buildings perched over space left largely empty for parking. With enough shaking, the apartments come crashing down on the mostly hollow space below.
Scott Brandenberg, a professor of civil and environmental engineering at the UCLA Henry Samueli School of Engineering and Applied Science, studies the impact of earthquakes on the built environment. He lives in a soft story building.“It’s hard to find buildings in the area I can afford,” he says. Soft story buildings were not designed to resist earthquake forces specified in the current building code and should be evaluated for retrofit. A number of these buildings collapsed during the 1994 Northridge earthquake.
Today, Brandenberg’s building, as well as thousands of others across the region, have been retrofitted through mandatory retrofit ordinances.
Learning from the past is key to UCLA’s earthquake research across multiple fields. Brandenberg, for example, is creating an international database on liquefaction, the phenomenon sometimes observed during earthquakes in which soil flows like a liquid, causing land to slide and foundations of buildings to slip away. He and his colleagues are collecting case studies globally that shed light on the consequences of liquefaction. “We’ve never really had a database that was available to the whole community,” says Brandenberg. He hopes broad access to the data will help standardize the science behind liquefaction.
Researchers can’t wait around for earthquakes to strike; the stakes are too high. Jonathan Stewart, a professor in the Department of Civil and Environmental Engineering, has been collecting global data on earthquake impacts on levees and their associated drinking water systems. His major area: a 1,100-mile network of levees in California that directs water into the State Water Project’s drinking and agricultural water conveyances and prevents salt water intrusion from the San Francisco Bay.
“A good 40 percent of the water in Southern California is coming through this system,” he says. “So the stability and viability of this system is really a big deal. For the system to work, the whole thing has to work. You can’t just analyze individual sections. So we’ve developed methods to do that.”
Based on previous seismic activity near levee systems in places like Japan, Stewart and his colleagues can determine the dynamic properties of the peat that makes up much of the structure of the foundation beneath the levees in the Delta, learning how much levees can settle, which can lead to overtopping and cause erosion. They also determine how much soil to keep in reserve to patch breaches that occur. Add in computer modeling, and they can predict worst-case scenarios for disruptions to the system and plan how to respond.
This type of systemic, model-based thinking is new for earthquake research, a field that has been largely based on observations of specific events. “[Research] was being done on a small-time basis: individual faculty and their grad students working on something, producing a paper, other people doing the same thing, and we get all these disparate documents out there,” Stewart explains. “And then somebody has to figure out what to do with it all. We’re trying to change the paradigm by which this research is done.”
Practitioners outside the university who are applying this information to the real world say UCLA’s work is making a difference. Ronald T. Eguchi is president and CEO of Long Beach-based ImageCat, which creates earthquake maps and hazard exposure models for buildings and infrastructure. The company serves clients like NASA and FEMA, as well as private insurance companies. Eguchi says the data coming out of UCLA has helped make these maps more accurate.
“Without [that UCLA] research, I don’t think we’d be able to come up with these quantitative assessments,” he says. “We use that information to [learn] what the extent of displacement or ground failure would be.”
Useful data can come from surprising sources. Engineering Professor Ertugrul Taciroglu, who studies earthquake effects on urban infrastructure — ports, bridges, power lines — has developed a way to use the abundant images available from Google to visually analyze infrastructures and develop predictive simulation models to quantify their seismic risks.
“My students and I developed computer codes that will locate each bridge and examine it through Google Street from multiple angles. Our algorithms extract key measurements, such as column heights and cross-sectional dimension. We use those measurements to create a structural analysis model. We intend to do that for all 25,000 bridges in California,” he says. These images are remarkably accurate. Taciroglu says he has checked his models using Google’s images against Caltrans’ original bridge blueprints, and the measurements match up at the sub-inch level.
Google Earth also has been a rich source of data for power lines and other lifeline transmission corridors that provide electricity across the state. “I can create structural analysis models of power distribution networks by going around with my preprogrammed robot inside Google Earth and extracting where the transmission towers are, the length of the cables, the sag of the cables,” Taciroglu adds. “Because I know where they are, I know what kind of an earthquake shaking we can expect in the future for each structure.”
Knowing how transmission lines may fail in a big earthquake can show, for example, what hospitals should be better equipped with backup power. Modeling which bridges could fail will help us understand how to prevent parts of cities from being cut off from essential services. Taciroglu says a dream project would be to integrate all this information into one massive model that encompasses the full complexity of an entire urban region and all its interrelated risks. Such a tool would be immensely valuable to government agencies, facility operators and insurance agencies.
This kind of metropolitan-wide thinking may not be far off. A task force of UCLA earthquake researchers is developing plans to better integrate systems thinking and earthquake consciousness into the operations of city and county entities, such as utilities. “Lifeline infrastructure can be impacted by big earthquakes,” says Ken Hudnut, a geophysicist for Risk Reduction at the U.S. Geological Survey and a lecturer in UCLA’s Department of Civil and Environmental Engineering, who advises the L.A. Mayor’s Office of Resilience.
Earthquake Network projectEarthquake Network is a research project which aims at developing and maintaining a crowdsourced smartphone-based earthquake warning system at a global level. Smartphones made available by the population are used to detect the earthquake waves using the on-board accelerometers. When an earthquake is detected, an earthquake warning is issued in order to alert the population not yet reached by the damaging waves of the earthquake.
The project started on January 1, 2013 with the release of the homonymous Android application Earthquake Network. The author of the research project and developer of the smartphone application is Francesco Finazzi of the University of Bergamo, Italy.
Get the app in the Google Play store.
Smartphone network spatial distribution (green and red dots) on December 4, 2015
The Quake-Catcher Network is a collaborative initiative for developing the world’s largest, low-cost strong-motion seismic network by utilizing sensors in and attached to internet-connected computers. With your help, the Quake-Catcher Network can provide better understanding of earthquakes, give early warning to schools, emergency response systems, and others. The Quake-Catcher Network also provides educational software designed to help teach about earthquakes and earthquake hazards.
After almost eight years at Stanford, and a year at CalTech, the QCN project is moving to the University of Southern California Dept. of Earth Sciences. QCN will be sponsored by the Incorporated Research Institutions for Seismology (IRIS) and the Southern California Earthquake Center (SCEC).
The Quake-Catcher Network is a distributed computing network that links volunteer hosted computers into a real-time motion sensing network. QCN is one of many scientific computing projects that runs on the world-renowned distributed computing platform Berkeley Open Infrastructure for Network Computing (BOINC).
The volunteer computers monitor vibrational sensors called MEMS accelerometers, and digitally transmit “triggers” to QCN’s servers whenever strong new motions are observed. QCN’s servers sift through these signals, and determine which ones represent earthquakes, and which ones represent cultural noise (like doors slamming, or trucks driving by).
There are two categories of sensors used by QCN: 1) internal mobile device sensors, and 2) external USB sensors.
Mobile Devices: MEMS sensors are often included in laptops, games, cell phones, and other electronic devices for hardware protection, navigation, and game control. When these devices are still and connected to QCN, QCN software monitors the internal accelerometer for strong new shaking. Unfortunately, these devices are rarely secured to the floor, so they may bounce around when a large earthquake occurs. While this is less than ideal for characterizing the regional ground shaking, many such sensors can still provide useful information about earthquake locations and magnitudes.
USB Sensors: MEMS sensors can be mounted to the floor and connected to a desktop computer via a USB cable. These sensors have several advantages over mobile device sensors. 1) By mounting them to the floor, they measure more reliable shaking than mobile devices. 2) These sensors typically have lower noise and better resolution of 3D motion. 3) Desktops are often left on and do not move. 4) The USB sensor is physically removed from the game, phone, or laptop, so human interaction with the device doesn’t reduce the sensors’ performance. 5) USB sensors can be aligned to North, so we know what direction the horizontal “X” and “Y” axes correspond to.
If you are a science teacher at a K-12 school, please apply for a free USB sensor and accompanying QCN software. QCN has been able to purchase sensors to donate to schools in need. If you are interested in donating to the program or requesting a sensor, click here.
Earthquake safety is a responsibility shared by billions worldwide. The Quake-Catcher Network (QCN) provides software so that individuals can join together to improve earthquake monitoring, earthquake awareness, and the science of earthquakes. The Quake-Catcher Network (QCN) links existing networked laptops and desktops in hopes to form the worlds largest strong-motion seismic network.
Below, the QCN Quake Catcher Network map
ShakeAlert: An Earthquake Early Warning System for the West Coast of the United States
The U. S. Geological Survey (USGS) along with a coalition of State and university partners is developing and testing an earthquake early warning (EEW) system called ShakeAlert for the west coast of the United States. Long term funding must be secured before the system can begin sending general public notifications, however, some limited pilot projects are active and more are being developed. The USGS has set the goal of beginning limited public notifications in 2018.
Watch a video describing how ShakeAlert works in English or Spanish.
Earthquakes pose a national challenge because more than 143 million Americans live in areas of significant seismic risk across 39 states. Most of our Nation’s earthquake risk is concentrated on the West Coast of the United States. The Federal Emergency Management Agency (FEMA) has estimated the average annualized loss from earthquakes, nationwide, to be $5.3 billion, with 77 percent of that figure ($4.1 billion) coming from California, Washington, and Oregon, and 66 percent ($3.5 billion) from California alone. In the next 30 years, California has a 99.7 percent chance of a magnitude 6.7 or larger earthquake and the Pacific Northwest has a 10 percent chance of a magnitude 8 to 9 megathrust earthquake on the Cascadia subduction zone.
Part of the Solution
Today, the technology exists to detect earthquakes, so quickly, that an alert can reach some areas before strong shaking arrives. The purpose of the ShakeAlert system is to identify and characterize an earthquake a few seconds after it begins, calculate the likely intensity of ground shaking that will result, and deliver warnings to people and infrastructure in harm’s way. This can be done by detecting the first energy to radiate from an earthquake, the P-wave energy, which rarely causes damage. Using P-wave information, we first estimate the location and the magnitude of the earthquake. Then, the anticipated ground shaking across the region to be affected is estimated and a warning is provided to local populations. The method can provide warning before the S-wave arrives, bringing the strong shaking that usually causes most of the damage.
Studies of earthquake early warning methods in California have shown that the warning time would range from a few seconds to a few tens of seconds. ShakeAlert can give enough time to slow trains and taxiing planes, to prevent cars from entering bridges and tunnels, to move away from dangerous machines or chemicals in work environments and to take cover under a desk, or to automatically shut down and isolate industrial systems. Taking such actions before shaking starts can reduce damage and casualties during an earthquake. It can also prevent cascading failures in the aftermath of an event. For example, isolating utilities before shaking starts can reduce the number of fire initiations.
System Goal
The USGS will issue public warnings of potentially damaging earthquakes and provide warning parameter data to government agencies and private users on a region-by-region basis, as soon as the ShakeAlert system, its products, and its parametric data meet minimum quality and reliability standards in those geographic regions. The USGS has set the goal of beginning limited public notifications in 2018. Product availability will expand geographically via ANSS regional seismic networks, such that ShakeAlert products and warnings become available for all regions with dense seismic instrumentation.
Current Status
The West Coast ShakeAlert system is being developed by expanding and upgrading the infrastructure of regional seismic networks that are part of the Advanced National Seismic System (ANSS); the California Integrated Seismic Network (CISN) is made up of the Southern California Seismic Network, SCSN) and the Northern California Seismic System, NCSS and the Pacific Northwest Seismic Network (PNSN). This enables the USGS and ANSS to leverage their substantial investment in sensor networks, data telemetry systems, data processing centers, and software for earthquake monitoring activities residing in these network centers. The ShakeAlert system has been sending live alerts to “beta” users in California since January of 2012 and in the Pacific Northwest since February of 2015.
In February of 2016 the USGS, along with its partners, rolled-out the next-generation ShakeAlert early warning test system in California joined by Oregon and Washington in April 2017. This West Coast-wide “production prototype” has been designed for redundant, reliable operations. The system includes geographically distributed servers, and allows for automatic fail-over if connection is lost.
This next-generation system will not yet support public warnings but does allow selected early adopters to develop and deploy pilot implementations that take protective actions triggered by the ShakeAlert notifications in areas with sufficient sensor coverage.
Authorities
The USGS will develop and operate the ShakeAlert system, and issue public notifications under collaborative authorities with FEMA, as part of the National Earthquake Hazard Reduction Program, as enacted by the Earthquake Hazards Reduction Act of 1977, 42 U.S.C. §§ 7704 SEC. 2.
For More Information
Robert de Groot, ShakeAlert National Coordinator for Communication, Education, and Outreach rdegroot@usgs.gov
626-583-7225
For nearly 100 years, UCLA has been a pioneer, persevering through impossibility, turning the futile into the attainable.
We doubt the critics, reject the status quo and see opportunity in dissatisfaction. Our campus, faculty and students are driven by optimism. It is not naïve; it is essential. And it has fueled every accomplishment, allowing us to redefine what’s possible, time after time.
This can-do perspective has brought us 12 Nobel Prizes, 12 Rhodes Scholarships, more NCAA titles than any university and more Olympic medals than most nations. Our faculty and alumni helped create the Internet and pioneered reverse osmosis. And more than 100 companies have been created based on technology developed at UCLA.
May 5, 2018
Jason R. Patton, Ph.D.
Ross Stein, Ph.D.
Volkan Sevilgen, M.Sc.
At 12:46 p.m. HST, a column of robust, reddish-brown ash plume occurred after a magnitude 6.9 South Flank of Kïlauea earthquake shook the Big Island of Hawai‘i. (USGS HVO)
Hawai’i Earthquakes and Eruptions
Over the past week there has been a flurry of earthquake activity on the Big Island of Hawai’i. These earthquakes are related to the volcanic activity associated with Kïlauea magmatism. As magma rises and moves within the magma chamber, we can infer the motion direction and velocity as earthquakes respond to these changes in magma pressure. At the time we write this, there have been over 900 shallow depth earthquakes reported on the U.S. Geological Survey earthquake website.
Hawai’i as seen in Google Earth, 3X vertical exaggeration. One week of earthquakes from USGS (orange dots)
Below is a map that shows seismicity from the past week. Blue circles are located relative to the Pu’u ‘Ō’ō-Kupaianaha Volcano April 30 activity and the May 3 and May 4 fissure eruptions near the Leilani Estates (a residential subdivision near Pāhoa, Hawai’i). This area was evacuated and nobody was harmed. Several buildings were destroyed by fire. The seismicity also initially followed this eastward trend in motion. Initially, earthquakes were located to the west, but migrated to the east prior to the fissure eruptions. In addition, the lava lake formed in late April dropped in elevation prior to the fissure eruption (possibly due to the migration of magma from west to east). However, later seismicity migrated back to the west. This may be due to the changes in pressure associated with magma movement.
Temblor map showing earthquakes, faults, and shaded topography.
Hawai’ian Hotspot Volcanism
The Hawai’ian Islands are part of a chain of volcanoes and seamounts that are formed as the oceanic Pacific plate moves over a magmatic hotspot. This hotspot is a region where there exists a plume of upwelling magma that erupts through the Pacific plate to form volcanic eruptions. Over time, as the plate moves, the older volcanoes get further away from the hotspot. The most recent and currently volcanically active part of the Hawai’ian Islands is located on the Big Island of Hawai’i, where the Kïlauea volcano is located. Below is a visualization of how the magma chamber below Kïlauea may be oriented. Note how the magma plume rises to the Kïlauea Caldera, then spreads laterally to feed additional volcanic centers along the rift zones.
There are three main sources of earthquakes in Hawai’i: magmatic, volcanic edifice, and deep tectonic (IRIS). Magmatic earthquakes occur when magma rises or moves within the crust. As the magma rises beneath the volcanoes it can break up the crust. Changes in pressure and volume in the magma and volcano can increase the stress on faults in the region causing earthquakes.
There are faults within the volcanic edifice (the cone shaped structure that forms the shape of the volcano), as well as faults that exist beneath the volcano, between the volcano and the underlying Pacific plate. These faults can be sources of earthquakes independent of volcanism. Earthquakes in the volcanic edifice are extensional and caused by gravitational collapse of the volcanic rocks that form the edifice. These earthquakes tend to be small, with maximum magnitudes in the M 5 range. These extensional earthquakes may trigger earthquakes on the fault formed beneath the volcano. Earthquakes along this fault system can be much larger, including a M 7.9 Ka’u earthquake in 1868. A more recent example is the November 29, 1975 M 7.1 earthquake that happened near the current seismic and volcanic activity.
Earthquakes can occur within both the upper brittle mantle and oceanic crust as changes in pressure and temperature are exerted by the overlying volcano. The October 15, 2006 Kiholo Bay earthquake is an example of this type of earthquake. These are deeper than the other earthquakes, are further away from people and cause lesser shaking, for the same magnitude, than for shallower magmatic and volcanic edifice earthquakes.
The major fault systems on the southern part of the Big Island include rift zones and normal faults formed by extension either from gravitational collapse or extension related to the rift zones. The East Rift Zone and the Hilina fault appear to be the likely fault systems associated with this ongoing seismic activity. The 1975 earthquake may be a good analog to the current seismicity because it was also associated with magma injection.
Map showing the major volcanic centers, rift zones, and fault systems in Hawai’I (USGS, 2010).
The current sequence of earthquakes began near the Pu’u ‘Ō’ō-Kupaianaha Volcano, where there is a crater formed from prior eruptions. This crater was filled with lava and the lava level reached the rim of the crater and overflowed the crater on 4/30/2018.
Tsunami
The 1975 M 7.1 earthquake generated a tsunami observed by tide gages located in Maui, Kauai, Hawai’i, and Oahu. Wave heights were up to several feet in Hilo and several inches high in Oahu. This tsunami was too small to have an impact elsewhere. The M 6.9 earthquake also generated a tsunami, but it was smaller than the 1975 tsunami. The Hilo tide gage shows a wave height of less than a foot (amplitude = 0.399 meter).
Water surface elevation data from Hilo, Hawai’i from IOC.
What is Next?
Using the 1975 earthquake as an analogy, the M 6.9 earthquake is possibly the main shock in this sequence. However, our historic record is only about 200 years long and we may not have enough knowledge to fully understand the entire range of possible outcomes. In terms of volcanism, this part of Hawai’i has eruptions on an almost ongoing basis. Below is a figure that shows the volcanic activity since 1780. Note that the USGS considers that we are currently in a period of continuous activity.
Graph summarizing the eruptions of Mauna Loa and Kïlauea Volcanoes during the past 200 years (USGS, 2010).
Here is another great map showing the relative volcanic hazard for the areas around the Big Island of Hawai’i. Severity of volcanic hazard is represented by color. The gray areas show regions where lava flows have happened in the past ~200 years. Note that the rift zones of Kïlauea are considered a region of increased volcanic hazard. So, if one resides or visits to regions of increasing severity of hazard, be prepared to respond to volcanic and seismic activity. Be prepared and know your hazard!
Map of Island of Hawai‘i showing the volcanic hazards from lava flows (USGS, 2010).
Earthquake Network is a research project which aims at developing and maintaining a crowdsourced smartphone-based earthquake warning system at a global level. Smartphones made available by the population are used to detect the earthquake waves using the on-board accelerometers. When an earthquake is detected, an earthquake warning is issued in order to alert the population not yet reached by the damaging waves of the earthquake.
The project started on January 1, 2013 with the release of the homonymous Android application Earthquake Network. The author of the research project and developer of the smartphone application is Francesco Finazzi of the University of Bergamo, Italy.
Get the app in the Google Play store.
Smartphone network spatial distribution (green and red dots) on December 4, 2015
The Quake-Catcher Network is a collaborative initiative for developing the world’s largest, low-cost strong-motion seismic network by utilizing sensors in and attached to internet-connected computers. With your help, the Quake-Catcher Network can provide better understanding of earthquakes, give early warning to schools, emergency response systems, and others. The Quake-Catcher Network also provides educational software designed to help teach about earthquakes and earthquake hazards.
After almost eight years at Stanford, and a year at CalTech, the QCN project is moving to the University of Southern California Dept. of Earth Sciences. QCN will be sponsored by the Incorporated Research Institutions for Seismology (IRIS) and the Southern California Earthquake Center (SCEC).
The Quake-Catcher Network is a distributed computing network that links volunteer hosted computers into a real-time motion sensing network. QCN is one of many scientific computing projects that runs on the world-renowned distributed computing platform Berkeley Open Infrastructure for Network Computing (BOINC).
The volunteer computers monitor vibrational sensors called MEMS accelerometers, and digitally transmit “triggers” to QCN’s servers whenever strong new motions are observed. QCN’s servers sift through these signals, and determine which ones represent earthquakes, and which ones represent cultural noise (like doors slamming, or trucks driving by).
There are two categories of sensors used by QCN: 1) internal mobile device sensors, and 2) external USB sensors.
Mobile Devices: MEMS sensors are often included in laptops, games, cell phones, and other electronic devices for hardware protection, navigation, and game control. When these devices are still and connected to QCN, QCN software monitors the internal accelerometer for strong new shaking. Unfortunately, these devices are rarely secured to the floor, so they may bounce around when a large earthquake occurs. While this is less than ideal for characterizing the regional ground shaking, many such sensors can still provide useful information about earthquake locations and magnitudes.
USB Sensors: MEMS sensors can be mounted to the floor and connected to a desktop computer via a USB cable. These sensors have several advantages over mobile device sensors. 1) By mounting them to the floor, they measure more reliable shaking than mobile devices. 2) These sensors typically have lower noise and better resolution of 3D motion. 3) Desktops are often left on and do not move. 4) The USB sensor is physically removed from the game, phone, or laptop, so human interaction with the device doesn’t reduce the sensors’ performance. 5) USB sensors can be aligned to North, so we know what direction the horizontal “X” and “Y” axes correspond to.
If you are a science teacher at a K-12 school, please apply for a free USB sensor and accompanying QCN software. QCN has been able to purchase sensors to donate to schools in need. If you are interested in donating to the program or requesting a sensor, click here.
Earthquake safety is a responsibility shared by billions worldwide. The Quake-Catcher Network (QCN) provides software so that individuals can join together to improve earthquake monitoring, earthquake awareness, and the science of earthquakes. The Quake-Catcher Network (QCN) links existing networked laptops and desktops in hopes to form the worlds largest strong-motion seismic network.
Below, the QCN Quake Catcher Network map
ShakeAlert: An Earthquake Early Warning System for the West Coast of the United States
The U. S. Geological Survey (USGS) along with a coalition of State and university partners is developing and testing an earthquake early warning (EEW) system called ShakeAlert for the west coast of the United States. Long term funding must be secured before the system can begin sending general public notifications, however, some limited pilot projects are active and more are being developed. The USGS has set the goal of beginning limited public notifications in 2018.
Watch a video describing how ShakeAlert works in English or Spanish.
Earthquakes pose a national challenge because more than 143 million Americans live in areas of significant seismic risk across 39 states. Most of our Nation’s earthquake risk is concentrated on the West Coast of the United States. The Federal Emergency Management Agency (FEMA) has estimated the average annualized loss from earthquakes, nationwide, to be $5.3 billion, with 77 percent of that figure ($4.1 billion) coming from California, Washington, and Oregon, and 66 percent ($3.5 billion) from California alone. In the next 30 years, California has a 99.7 percent chance of a magnitude 6.7 or larger earthquake and the Pacific Northwest has a 10 percent chance of a magnitude 8 to 9 megathrust earthquake on the Cascadia subduction zone.
Part of the Solution
Today, the technology exists to detect earthquakes, so quickly, that an alert can reach some areas before strong shaking arrives. The purpose of the ShakeAlert system is to identify and characterize an earthquake a few seconds after it begins, calculate the likely intensity of ground shaking that will result, and deliver warnings to people and infrastructure in harm’s way. This can be done by detecting the first energy to radiate from an earthquake, the P-wave energy, which rarely causes damage. Using P-wave information, we first estimate the location and the magnitude of the earthquake. Then, the anticipated ground shaking across the region to be affected is estimated and a warning is provided to local populations. The method can provide warning before the S-wave arrives, bringing the strong shaking that usually causes most of the damage.
Studies of earthquake early warning methods in California have shown that the warning time would range from a few seconds to a few tens of seconds. ShakeAlert can give enough time to slow trains and taxiing planes, to prevent cars from entering bridges and tunnels, to move away from dangerous machines or chemicals in work environments and to take cover under a desk, or to automatically shut down and isolate industrial systems. Taking such actions before shaking starts can reduce damage and casualties during an earthquake. It can also prevent cascading failures in the aftermath of an event. For example, isolating utilities before shaking starts can reduce the number of fire initiations.
System Goal
The USGS will issue public warnings of potentially damaging earthquakes and provide warning parameter data to government agencies and private users on a region-by-region basis, as soon as the ShakeAlert system, its products, and its parametric data meet minimum quality and reliability standards in those geographic regions. The USGS has set the goal of beginning limited public notifications in 2018. Product availability will expand geographically via ANSS regional seismic networks, such that ShakeAlert products and warnings become available for all regions with dense seismic instrumentation.
Current Status
The West Coast ShakeAlert system is being developed by expanding and upgrading the infrastructure of regional seismic networks that are part of the Advanced National Seismic System (ANSS); the California Integrated Seismic Network (CISN) is made up of the Southern California Seismic Network, SCSN) and the Northern California Seismic System, NCSS and the Pacific Northwest Seismic Network (PNSN). This enables the USGS and ANSS to leverage their substantial investment in sensor networks, data telemetry systems, data processing centers, and software for earthquake monitoring activities residing in these network centers. The ShakeAlert system has been sending live alerts to “beta” users in California since January of 2012 and in the Pacific Northwest since February of 2015.
In February of 2016 the USGS, along with its partners, rolled-out the next-generation ShakeAlert early warning test system in California joined by Oregon and Washington in April 2017. This West Coast-wide “production prototype” has been designed for redundant, reliable operations. The system includes geographically distributed servers, and allows for automatic fail-over if connection is lost.
This next-generation system will not yet support public warnings but does allow selected early adopters to develop and deploy pilot implementations that take protective actions triggered by the ShakeAlert notifications in areas with sufficient sensor coverage.
Authorities
The USGS will develop and operate the ShakeAlert system, and issue public notifications under collaborative authorities with FEMA, as part of the National Earthquake Hazard Reduction Program, as enacted by the Earthquake Hazards Reduction Act of 1977, 42 U.S.C. §§ 7704 SEC. 2.
For More Information
Robert de Groot, ShakeAlert National Coordinator for Communication, Education, and Outreach rdegroot@usgs.gov
626-583-7225
Southern California defines cool. The perfect climes of San Diego, the glitz of Hollywood, the magic of Disneyland. The geology is pretty spectacular, as well.
“Southern California is a prime natural laboratory to study active earthquake processes,” says Tom Jordan, a professor in the Department of Earth Sciences at the University of Southern California (USC). “The desert allows you to observe the fault system very nicely.”
The fault system to which he is referring is the San Andreas, among the more famous fault systems in the world. With roots deep in Mexico, it scars California from the Salton Sea in the south to Cape Mendocino in the north, where it then takes a westerly dive into the Pacific.
Situated as it is at the heart of the San Andreas Fault System, Southern California does make an ideal location to study earthquakes. That it is home to nearly 24-million people makes for a more urgent reason to study them.
San Andreas Fault System. Aerial photo of San Andreas Fault looking northwest onto the Carrizo Plain with Soda Lake visible at the upper left. John Wiley User:Jw4nvc – Santa Barbara, California
Jordan and a team from the Southern California Earthquake Center (SCEC) are using the supercomputing resources of the Argonne Leadership Computing Facility (ALCF), a U.S. Department of Energy Office of Science User Facility, to advance modeling for the study of earthquake risk and how to reduce it.
Headquartered at USC, the center is one of the largest collaborations in geoscience, engaging over 70 research institutions and 1,000 investigators from around the world.
The team relies on a century’s worth of data from instrumental records as well as regional and seismic national hazard models to develop new tools for understanding earthquake hazards. Working with the ALCF, they have used this information to improve their earthquake rupture simulator, RSQSim.
RSQ is a reference to rate- and state-dependent friction in earthquakes — a friction law that can be used to study the nucleation, or initiation, of earthquakes. RSQSim models both nucleation and rupture processes to understand how earthquakes transfer stress to other faults.
ALCF staff were instrumental in adapting the code to Mira, the facility’s 10-petaflops supercomputer, to allow for the larger simulations required to model earthquake behaviors in very complex fault systems, like San Andreas, and which led to the team’s biggest discovery.
Shake, rattle, and code
The SCEC, in partnership with the U.S. Geological Survey, had already developed the Uniform California Earthquake Rupture Forecast (UCERF), an empirically based model that integrates theory, geologic information, and geodetic data, like GPS displacements, to determine spatial relationships between faults and slippage rates of the tectonic plates that created those faults.
Though more traditional, the newest version, UCERF3, is considered the best representation of California earthquake ruptures, but the picture it portrays is still not as accurate as researchers would hope.
“We know a lot about how big earthquakes can be, how frequently they occur, and where they occur, but we cannot predict them precisely in time,” notes Jordan.
The team turned to Mira to run RSQSim to determine whether they could achieve more accurate results more quickly. A physics-based code, RSQSim produces long-term synthetic earthquake catalogs that comprise dates, times, locations, and magnitudes for predicted events.
Using simulation, researchers impose stresses upon some representation of a fault system, which changes the stress throughout much of the system and thus changes the way future earthquakes occur. Trying to model these powerful stress-mediated interactions is particularly difficult with complex systems and faults like San Andreas.
“We just let the system evolve and create earthquake catalogs for a hundred thousand or a million years. It’s like throwing a grain of sand in a set of cogs to see what happens,” explains Christine Goulet, a team member and executive science director for special projects with SCEC.
The end result is a more detailed picture of the possible hazard, which forecasts a sequence of earthquakes of various magnitudes expected to occur on the San Andreas Fault over a given time range.
The group tried to calibrate RSQSim’s numerous parameters to replicate UCERF3, but eventually decided to run the code with its default parameters. While the initial intent was to evaluate the magnitude of differences between the models, they discovered, instead, that both models agreed closely on their forecasts of future seismologic activity.
“So it was an a-ha moment. Eureka,” recalls Goulet. “The results were a surprise because the group had thought carefully about optimizing the parameters. The decision not to change them from their default values made for very nice results.”
The researchers noted that the mutual validation of the two approaches could prove extremely productive in further assessing seismic hazard estimates and their uncertainties.
Information derived from the simulations will help the team compute the strong ground motions generated by faulting that occurs at the surface — the characteristic shaking that is synonymous with earthquakes. To do this, the team couples the earthquake rupture forecasts, UCERF and RSQSim, with different models that represent the way waves propagate through the system. Called ground motion prediction equations, these are standard equations used by engineers to calculate the shaking levels from earthquakes of different sizes and locations.
One of those models is the dynamic rupture and wave propagation code Waveqlab3D (Finite Difference Quake and Wave Laboratory 3D), which is the focus of the SCEC team’s current ALCF allocation.
“These experiments show that the physics-based model RSQSim can replicate the seismic hazard estimates derived from the empirical model UCERF3, but with far fewer statistical assumptions,” notes Jordan. “The agreement gives us more confidence that the seismic hazard models for California are consistent with what we know about earthquake physics. We can now begin to use these physics to improve the hazard models.”
This project was awarded computing time and resources at the ALCF through DOE’s Innovative and Novel Computational Impact on Theory and Experiment (INCITE) program. The team’s research is also supported by the National Science Foundation, the U.S. Geological Survey, and the W.M. Keck Foundation.
ANL ALCF Cetus IBM supercomputer
ANL ALCF Theta Cray supercomputer
ANL ALCF Cray Aurora supercomputer
ANL ALCF MIRA IBM Blue Gene Q supercomputer at the Argonne Leadership Computing Facility
Earthquake Network projectEarthquake Network is a research project which aims at developing and maintaining a crowdsourced smartphone-based earthquake warning system at a global level. Smartphones made available by the population are used to detect the earthquake waves using the on-board accelerometers. When an earthquake is detected, an earthquake warning is issued in order to alert the population not yet reached by the damaging waves of the earthquake.
The project started on January 1, 2013 with the release of the homonymous Android application Earthquake Network. The author of the research project and developer of the smartphone application is Francesco Finazzi of the University of Bergamo, Italy.
Get the app in the Google Play store.
Smartphone network spatial distribution (green and red dots) on December 4, 2015
The Quake-Catcher Network is a collaborative initiative for developing the world’s largest, low-cost strong-motion seismic network by utilizing sensors in and attached to internet-connected computers. With your help, the Quake-Catcher Network can provide better understanding of earthquakes, give early warning to schools, emergency response systems, and others. The Quake-Catcher Network also provides educational software designed to help teach about earthquakes and earthquake hazards.
After almost eight years at Stanford, and a year at CalTech, the QCN project is moving to the University of Southern California Dept. of Earth Sciences. QCN will be sponsored by the Incorporated Research Institutions for Seismology (IRIS) and the Southern California Earthquake Center (SCEC).
The Quake-Catcher Network is a distributed computing network that links volunteer hosted computers into a real-time motion sensing network. QCN is one of many scientific computing projects that runs on the world-renowned distributed computing platform Berkeley Open Infrastructure for Network Computing (BOINC).
The volunteer computers monitor vibrational sensors called MEMS accelerometers, and digitally transmit “triggers” to QCN’s servers whenever strong new motions are observed. QCN’s servers sift through these signals, and determine which ones represent earthquakes, and which ones represent cultural noise (like doors slamming, or trucks driving by).
There are two categories of sensors used by QCN: 1) internal mobile device sensors, and 2) external USB sensors.
Mobile Devices: MEMS sensors are often included in laptops, games, cell phones, and other electronic devices for hardware protection, navigation, and game control. When these devices are still and connected to QCN, QCN software monitors the internal accelerometer for strong new shaking. Unfortunately, these devices are rarely secured to the floor, so they may bounce around when a large earthquake occurs. While this is less than ideal for characterizing the regional ground shaking, many such sensors can still provide useful information about earthquake locations and magnitudes.
USB Sensors: MEMS sensors can be mounted to the floor and connected to a desktop computer via a USB cable. These sensors have several advantages over mobile device sensors. 1) By mounting them to the floor, they measure more reliable shaking than mobile devices. 2) These sensors typically have lower noise and better resolution of 3D motion. 3) Desktops are often left on and do not move. 4) The USB sensor is physically removed from the game, phone, or laptop, so human interaction with the device doesn’t reduce the sensors’ performance. 5) USB sensors can be aligned to North, so we know what direction the horizontal “X” and “Y” axes correspond to.
If you are a science teacher at a K-12 school, please apply for a free USB sensor and accompanying QCN software. QCN has been able to purchase sensors to donate to schools in need. If you are interested in donating to the program or requesting a sensor, click here.
Earthquake safety is a responsibility shared by billions worldwide. The Quake-Catcher Network (QCN) provides software so that individuals can join together to improve earthquake monitoring, earthquake awareness, and the science of earthquakes. The Quake-Catcher Network (QCN) links existing networked laptops and desktops in hopes to form the worlds largest strong-motion seismic network.
Below, the QCN Quake Catcher Network map
ShakeAlert: An Earthquake Early Warning System for the West Coast of the United States
The U. S. Geological Survey (USGS) along with a coalition of State and university partners is developing and testing an earthquake early warning (EEW) system called ShakeAlert for the west coast of the United States. Long term funding must be secured before the system can begin sending general public notifications, however, some limited pilot projects are active and more are being developed. The USGS has set the goal of beginning limited public notifications in 2018.
Watch a video describing how ShakeAlert works in English or Spanish.
Earthquakes pose a national challenge because more than 143 million Americans live in areas of significant seismic risk across 39 states. Most of our Nation’s earthquake risk is concentrated on the West Coast of the United States. The Federal Emergency Management Agency (FEMA) has estimated the average annualized loss from earthquakes, nationwide, to be $5.3 billion, with 77 percent of that figure ($4.1 billion) coming from California, Washington, and Oregon, and 66 percent ($3.5 billion) from California alone. In the next 30 years, California has a 99.7 percent chance of a magnitude 6.7 or larger earthquake and the Pacific Northwest has a 10 percent chance of a magnitude 8 to 9 megathrust earthquake on the Cascadia subduction zone.
Part of the Solution
Today, the technology exists to detect earthquakes, so quickly, that an alert can reach some areas before strong shaking arrives. The purpose of the ShakeAlert system is to identify and characterize an earthquake a few seconds after it begins, calculate the likely intensity of ground shaking that will result, and deliver warnings to people and infrastructure in harm’s way. This can be done by detecting the first energy to radiate from an earthquake, the P-wave energy, which rarely causes damage. Using P-wave information, we first estimate the location and the magnitude of the earthquake. Then, the anticipated ground shaking across the region to be affected is estimated and a warning is provided to local populations. The method can provide warning before the S-wave arrives, bringing the strong shaking that usually causes most of the damage.
Studies of earthquake early warning methods in California have shown that the warning time would range from a few seconds to a few tens of seconds. ShakeAlert can give enough time to slow trains and taxiing planes, to prevent cars from entering bridges and tunnels, to move away from dangerous machines or chemicals in work environments and to take cover under a desk, or to automatically shut down and isolate industrial systems. Taking such actions before shaking starts can reduce damage and casualties during an earthquake. It can also prevent cascading failures in the aftermath of an event. For example, isolating utilities before shaking starts can reduce the number of fire initiations.
System Goal
The USGS will issue public warnings of potentially damaging earthquakes and provide warning parameter data to government agencies and private users on a region-by-region basis, as soon as the ShakeAlert system, its products, and its parametric data meet minimum quality and reliability standards in those geographic regions. The USGS has set the goal of beginning limited public notifications in 2018. Product availability will expand geographically via ANSS regional seismic networks, such that ShakeAlert products and warnings become available for all regions with dense seismic instrumentation.
Current Status
The West Coast ShakeAlert system is being developed by expanding and upgrading the infrastructure of regional seismic networks that are part of the Advanced National Seismic System (ANSS); the California Integrated Seismic Network (CISN) is made up of the Southern California Seismic Network, SCSN) and the Northern California Seismic System, NCSS and the Pacific Northwest Seismic Network (PNSN). This enables the USGS and ANSS to leverage their substantial investment in sensor networks, data telemetry systems, data processing centers, and software for earthquake monitoring activities residing in these network centers. The ShakeAlert system has been sending live alerts to “beta” users in California since January of 2012 and in the Pacific Northwest since February of 2015.
In February of 2016 the USGS, along with its partners, rolled-out the next-generation ShakeAlert early warning test system in California joined by Oregon and Washington in April 2017. This West Coast-wide “production prototype” has been designed for redundant, reliable operations. The system includes geographically distributed servers, and allows for automatic fail-over if connection is lost.
This next-generation system will not yet support public warnings but does allow selected early adopters to develop and deploy pilot implementations that take protective actions triggered by the ShakeAlert notifications in areas with sufficient sensor coverage.
Authorities
The USGS will develop and operate the ShakeAlert system, and issue public notifications under collaborative authorities with FEMA, as part of the National Earthquake Hazard Reduction Program, as enacted by the Earthquake Hazards Reduction Act of 1977, 42 U.S.C. §§ 7704 SEC. 2.
For More Information
Robert de Groot, ShakeAlert National Coordinator for Communication, Education, and Outreach rdegroot@usgs.gov
626-583-7225
Argonne National Laboratory seeks solutions to pressing national problems in science and technology. The nation’s first national laboratory, Argonne conducts leading-edge basic and applied scientific research in virtually every scientific discipline. Argonne researchers work closely with researchers from hundreds of companies, universities, and federal, state and municipal agencies to help them solve their specific problems, advance America’s scientific leadership and prepare the nation for a better future. With employees from more than 60 nations, Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science. For more visit http://www.anl.gov.
About ALCF
The Argonne Leadership Computing Facility’s (ALCF) mission is to accelerate major scientific discoveries and engineering breakthroughs for humanity by designing and providing world-leading computing facilities in partnership with the computational science community.
We help researchers solve some of the world’s largest and most complex problems with our unique combination of supercomputing resources and expertise.
ALCF projects cover many scientific disciplines, ranging from chemistry and biology to physics and materials science. Examples include modeling and simulation efforts to:
Discover new materials for batteries
Predict the impacts of global climate change
Unravel the origins of the universe
Develop renewable energy technologies
14 March 2018
David Marsan
Greg Beroza
Joan Gomberg
Earthquakes: Nucleation, Triggering, Rupture, and Relationships to Aseismic Processes; Cargèse, Corsica, France, 2–6 October 2017.
This fault scarp in Italy’s Apennine Mountains, an example of surface-rupturing normal faulting, formed during the complex 2016 Amatrice-Norcia earthquake sequence. Attendees at a school in Cargèse, on the French island of Corsica, discussed current topics of interest in earthquake behavior and new developments in understanding complex earthquake sequences. Photo Credit: Maxime Godano.
The second Cargèse school on earthquakes, held last October, covered important and persistently challenging topics in earthquake behavior, including what factors control earthquake nucleation, how static or dynamic stresses and fluid injection trigger earthquakes, and how recent progress in measuring aseismic deformation might inform our understanding.
The 79 participants representing 21 nationalities, mostly Ph.D. students and postdocs, and the 20 lecturers addressed these questions from a range of disciplines and over a range of spatial and temporal scales.
Throughout this school, a recurring topic of discussion was what new insights have been gained since the first school in 2014. Here are some of the new developments presented at the 2017 school.
Presentations on new observations of the complexity of earthquake rupture—perhaps most notably in the 2016 Kaikoura, New Zealand, earthquake—emphasized the critical role that geometric complexity must play in earthquake physics. With some notable exceptions, earthquake scientists have confronted this complexity only intermittently in the past. However, recent developments in sensor technology, such as nodal-style seismic instruments, remote sensing using interferometric synthetic aperture radar (InSAR), and high-performance computing, increasingly allow scientists to discern complexity and explore its role in earthquake behavior.
Another new development presented at the school arises from multiple studies of large subduction zone earthquakes. These studies point to a preparation phase that manifests as foreshocks and possibly slow slip before some large events, sometimes originating at relatively shallow depths where the fault friction is thought to be high. The question of whether this preparation phase is the manifestation of a cascading failure process or is driven by an underlying aseismic process of unknown origin remains at issue.
Another important contributor to progress, discussed at the school, is the continuing development and application of new signal processing approaches to discern small earthquakes and weak deformation transients. This development is especially significant because the mechanical processes at work in weak deformation transients are poorly known. Laboratory exploration of established and proposed friction laws, of the slip rate–dependent and slip-dependent types, will be essential to elucidate those processes. Lab experiments and numerical simulations are making steady progress toward more realistic physical models that account for such factors as fluids, roughness, and damage zones. These models also provide new insight into earthquake processes.
Induced seismicity, which was also discussed at the school, provides an opportunity to accelerate progress in understanding the role of fluids in faulting. It also fills the spatial gap between laboratory experiments and naturally occurring tectonic earthquakes. Greater access to data relevant to induced seismicity would help realize its potential for furthering earthquake science in general.
At the end of the school, there were rumblings about the next one. What important current trends might we anticipate? Machine learning and data mining applied to earthquake science are emerging as an important area. Other examples include continued new insights from studies of induced seismicity and potentially even a controlled earthquake experiment. Finally, new observational capabilities—the ramping up of InSAR satellites, lidar surveys, dense seismometer arrays, and novel and highly ambitious deployments like S-net, which spans the seafloor from the Japanese coast to beyond the Japan Trench—are certain to provide new insights and will help ensure that future earthquakes teach us more than has been possible previously.
More information about the school can be found on its website.
David Marsan, ISTerre, Université Savoie Mont Blanc, Le Bourget du Lac, France; Greg Beroza (email: beroza@stanford.edu), Department of Geophysics, Stanford University, Calif.; and Joan Gomberg, U.S. Geological Survey, Seattle, Wash.
Earthquake Network projectEarthquake Network is a research project which aims at developing and maintaining a crowdsourced smartphone-based earthquake warning system at a global level. Smartphones made available by the population are used to detect the earthquake waves using the on-board accelerometers. When an earthquake is detected, an earthquake warning is issued in order to alert the population not yet reached by the damaging waves of the earthquake.
The project started on January 1, 2013 with the release of the homonymous Android application Earthquake Network. The author of the research project and developer of the smartphone application is Francesco Finazzi of the University of Bergamo, Italy.
Get the app in the Google Play store.
Smartphone network spatial distribution (green and red dots) on December 4, 2015
The Quake-Catcher Network is a collaborative initiative for developing the world’s largest, low-cost strong-motion seismic network by utilizing sensors in and attached to internet-connected computers. With your help, the Quake-Catcher Network can provide better understanding of earthquakes, give early warning to schools, emergency response systems, and others. The Quake-Catcher Network also provides educational software designed to help teach about earthquakes and earthquake hazards.
After almost eight years at Stanford, and a year at CalTech, the QCN project is moving to the University of Southern California Dept. of Earth Sciences. QCN will be sponsored by the Incorporated Research Institutions for Seismology (IRIS) and the Southern California Earthquake Center (SCEC).
The Quake-Catcher Network is a distributed computing network that links volunteer hosted computers into a real-time motion sensing network. QCN is one of many scientific computing projects that runs on the world-renowned distributed computing platform Berkeley Open Infrastructure for Network Computing (BOINC).
The volunteer computers monitor vibrational sensors called MEMS accelerometers, and digitally transmit “triggers” to QCN’s servers whenever strong new motions are observed. QCN’s servers sift through these signals, and determine which ones represent earthquakes, and which ones represent cultural noise (like doors slamming, or trucks driving by).
There are two categories of sensors used by QCN: 1) internal mobile device sensors, and 2) external USB sensors.
Mobile Devices: MEMS sensors are often included in laptops, games, cell phones, and other electronic devices for hardware protection, navigation, and game control. When these devices are still and connected to QCN, QCN software monitors the internal accelerometer for strong new shaking. Unfortunately, these devices are rarely secured to the floor, so they may bounce around when a large earthquake occurs. While this is less than ideal for characterizing the regional ground shaking, many such sensors can still provide useful information about earthquake locations and magnitudes.
USB Sensors: MEMS sensors can be mounted to the floor and connected to a desktop computer via a USB cable. These sensors have several advantages over mobile device sensors. 1) By mounting them to the floor, they measure more reliable shaking than mobile devices. 2) These sensors typically have lower noise and better resolution of 3D motion. 3) Desktops are often left on and do not move. 4) The USB sensor is physically removed from the game, phone, or laptop, so human interaction with the device doesn’t reduce the sensors’ performance. 5) USB sensors can be aligned to North, so we know what direction the horizontal “X” and “Y” axes correspond to.
If you are a science teacher at a K-12 school, please apply for a free USB sensor and accompanying QCN software. QCN has been able to purchase sensors to donate to schools in need. If you are interested in donating to the program or requesting a sensor, click here.
Earthquake safety is a responsibility shared by billions worldwide. The Quake-Catcher Network (QCN) provides software so that individuals can join together to improve earthquake monitoring, earthquake awareness, and the science of earthquakes. The Quake-Catcher Network (QCN) links existing networked laptops and desktops in hopes to form the worlds largest strong-motion seismic network.
Below, the QCN Quake Catcher Network map
ShakeAlert: An Earthquake Early Warning System for the West Coast of the United States
The U. S. Geological Survey (USGS) along with a coalition of State and university partners is developing and testing an earthquake early warning (EEW) system called ShakeAlert for the west coast of the United States. Long term funding must be secured before the system can begin sending general public notifications, however, some limited pilot projects are active and more are being developed. The USGS has set the goal of beginning limited public notifications in 2018.
Watch a video describing how ShakeAlert works in English or Spanish.
Earthquakes pose a national challenge because more than 143 million Americans live in areas of significant seismic risk across 39 states. Most of our Nation’s earthquake risk is concentrated on the West Coast of the United States. The Federal Emergency Management Agency (FEMA) has estimated the average annualized loss from earthquakes, nationwide, to be $5.3 billion, with 77 percent of that figure ($4.1 billion) coming from California, Washington, and Oregon, and 66 percent ($3.5 billion) from California alone. In the next 30 years, California has a 99.7 percent chance of a magnitude 6.7 or larger earthquake and the Pacific Northwest has a 10 percent chance of a magnitude 8 to 9 megathrust earthquake on the Cascadia subduction zone.
Part of the Solution
Today, the technology exists to detect earthquakes, so quickly, that an alert can reach some areas before strong shaking arrives. The purpose of the ShakeAlert system is to identify and characterize an earthquake a few seconds after it begins, calculate the likely intensity of ground shaking that will result, and deliver warnings to people and infrastructure in harm’s way. This can be done by detecting the first energy to radiate from an earthquake, the P-wave energy, which rarely causes damage. Using P-wave information, we first estimate the location and the magnitude of the earthquake. Then, the anticipated ground shaking across the region to be affected is estimated and a warning is provided to local populations. The method can provide warning before the S-wave arrives, bringing the strong shaking that usually causes most of the damage.
Studies of earthquake early warning methods in California have shown that the warning time would range from a few seconds to a few tens of seconds. ShakeAlert can give enough time to slow trains and taxiing planes, to prevent cars from entering bridges and tunnels, to move away from dangerous machines or chemicals in work environments and to take cover under a desk, or to automatically shut down and isolate industrial systems. Taking such actions before shaking starts can reduce damage and casualties during an earthquake. It can also prevent cascading failures in the aftermath of an event. For example, isolating utilities before shaking starts can reduce the number of fire initiations.
System Goal
The USGS will issue public warnings of potentially damaging earthquakes and provide warning parameter data to government agencies and private users on a region-by-region basis, as soon as the ShakeAlert system, its products, and its parametric data meet minimum quality and reliability standards in those geographic regions. The USGS has set the goal of beginning limited public notifications in 2018. Product availability will expand geographically via ANSS regional seismic networks, such that ShakeAlert products and warnings become available for all regions with dense seismic instrumentation.
Current Status
The West Coast ShakeAlert system is being developed by expanding and upgrading the infrastructure of regional seismic networks that are part of the Advanced National Seismic System (ANSS); the California Integrated Seismic Network (CISN) is made up of the Southern California Seismic Network, SCSN) and the Northern California Seismic System, NCSS and the Pacific Northwest Seismic Network (PNSN). This enables the USGS and ANSS to leverage their substantial investment in sensor networks, data telemetry systems, data processing centers, and software for earthquake monitoring activities residing in these network centers. The ShakeAlert system has been sending live alerts to “beta” users in California since January of 2012 and in the Pacific Northwest since February of 2015.
In February of 2016 the USGS, along with its partners, rolled-out the next-generation ShakeAlert early warning test system in California joined by Oregon and Washington in April 2017. This West Coast-wide “production prototype” has been designed for redundant, reliable operations. The system includes geographically distributed servers, and allows for automatic fail-over if connection is lost.
This next-generation system will not yet support public warnings but does allow selected early adopters to develop and deploy pilot implementations that take protective actions triggered by the ShakeAlert notifications in areas with sufficient sensor coverage.
Authorities
The USGS will develop and operate the ShakeAlert system, and issue public notifications under collaborative authorities with FEMA, as part of the National Earthquake Hazard Reduction Program, as enacted by the Earthquake Hazards Reduction Act of 1977, 42 U.S.C. §§ 7704 SEC. 2.
For More Information
Robert de Groot, ShakeAlert National Coordinator for Communication, Education, and Outreach rdegroot@usgs.gov
626-583-7225
Eos is the leading source for trustworthy news and perspectives about the Earth and space sciences and their impact. Its namesake is Eos, the Greek goddess of the dawn, who represents the light shed on understanding our planet and its environment in space by the Earth and space sciences.
An international team led by geologist Michael Strasser has used novel methods to analyse sediment deposits in the Japan Trench in order to gain new insights into the carbon cycle.
The research vessel RV Sonne, aboard which the sediment samples in the Japan Trench were taken in 2012. (Image: RF Forschungsschiffahrt Bremen/Germany)
In a paper recently published in Nature Communications, geologist Michael Strasser presented the initial findings of a month-long research expedition off the coast of Japan. The research initiative had been organised in March 2012 by MARUM – Center for Marine Environmental Sciences. Strasser, who until 2015 was Assistant Professor for Sediment Dynamics at ETH Zürich and is now a Full Professor for Sediment Geology at the University of Innsbruck, took an international team there to study dynamic sediment remobilisation processes triggered by seismic activity.
At a depth of 7,542 metres below sea level, the team took a core sample from the Japan Trench, an 800-km-long oceanic trench in the northwestern part of the Pacific Ocean. The trench, which is seismically active, was the epicentre of the Tohoku earthquake in 2011, which made headlines when it caused the nuclear meltdown at Fukushima. Such earthquakes wash enormous amounts of organic matter from the shallows down into deeper waters. The resulting sediment layers can thus be used later to glean information about the history of earthquakes and the carbon cycle in the deep ocean.
New dating methods in the deep ocean
The current study provided the researchers with a breakthrough. They analysed the carbon-rich sediments using radiocarbon dating. This method – measuring the amount of organic carbon as well as radioactive carbon (14C) in mineralised compounds – has long been a means of determining the age of individual sediment layers. Until now, however, it has not been possible to analyse samples from deeper than 5,000 metres below the surface, because the mineralised compounds dissolve under increased water pressure.
Strasser and his team therefore had to use new methods for their analysis. One of these was what is known as the online gas radiocarbon method, developed by ETH doctoral student Rui Bao and the Biogeoscience Group at ETH Zürich. This greatly increases efficiency, since it takes just a single core sample to make more than one hundred 14C age measurements directly on the organic matter contained within the sediment.
In addition, the researchers applied the Ramped PyrOx measurement method (pyrolysis) for the first time in the dating of deep-ocean sediment layers. This was done in cooperation with the Woods Hole Oceanographic Institute (U.S.), which developed the method. The process involves burning organic matter at different temperatures. Because older organic matter contains stronger chemical bonds, it requires higher temperatures to burn. What makes this method novel is that the relative age variation of the individual temperature fractions between two samples very precisely distinguishes the age difference between sediment levels in the deep sea.
Dating earthquakes to increase forecast accuracy
Thanks to these two innovative methods, the researchers could determine the relative age of organic matter in individual sediment layers with a high degree of precision. The core sample they tested contained older organic matter in three places, as well as higher rates of carbon export to the deep ocean. These places correspond to three historically documented yet hitherto partially imprecisely dated seismic events in the Japan Trench: the Tohoku earthquake in 2011, an unnamed earthquake in 1454, and the Sanriku earthquake in 869.
At the moment, Strasser is working on a large-scale geological map of the origin and frequency of sediments in deep-ocean trenches. To do so, he is analysing multiple core samples taken during a follow-up expedition to the Japan Trench in 2016. “The identification and dating of tectonically triggered sediment deposits is also important for future forecasts about the likelihood of earthquakes,” Strasser says. “With our new methods, we can predict the recurrence of earthquakes with much more accuracy.”
Michael Strasser (right), then assistant professor at ETH Zürich, and expedition head Gerold Wefer, professor at MARUM and Bremen University, make recommendations about the core sample on board the RV Sonne. Source/Copyright: V. Diekamp, MARUM, Bremen University.
Science team:
Bao R, Strasser M, McNichol A, Haghipour N, McIntyre C Wefer G, Eglinton T.
ETH Zürich is one of the leading international universities for technology and the natural sciences. It is well known for its excellent education, ground-breaking fundamental research and for implementing its results directly into practice.
Founded in 1855, ETH Zürich today has more than 18,500 students from over 110 countries, including 4,000 doctoral students. To researchers, it offers an inspiring working environment, to students, a comprehensive education.
Twenty-one Nobel Laureates have studied, taught or conducted research at ETH Zürich, underlining the excellent reputation of the university.
richardmitnick
5:19 pm on December 29, 2017 Permalink
| Reply Tags: A Challenge by Yue et al, Dispute: Do Magnitude 7-8 mainshocks commonly trigger immediate damaging aftershocks up to 300 km (180 mi) away?, Dueling posters at the Fall Meeting of the American Geophysical Union, Earthquake science, How could these large aftershocks have been overlooked?, Implications of the Fan-Shearer hypothesis, Singing seismograms, So who is right?, temblor ( 62 )
By analyzing a series of great earthquakes off the coast of Sumatra, Yue et al. argue that the seismic signals interpreted by Fan and Shearer as aftershocks are actually water reverberations in near-shore regions. (Photo from: trekkersblog.com)
In September 2016, Wenyuan Fan and Peter Shearer, from the Scripps Institution of Oceanography at U.C. San Diego, published an astonishing discovery in Science. Fan and Shearer detected nearly 50 previously unidentified M≥5.5 aftershocks up to 300 kilometers (200 mi) away from their M=7-8 mainshocks during the brief passage of the seismic ‘surface’ waves from the mainshock, or within 3 minutes. The authors concluded that remote dynamic triggering commonly exists and fundamentally promotes aftershock occurrence throughout the globe.
This is an annotated version of Fig. 2b of Fan and Shearer (2016), for the case of a 2013 mainshock off the Japan coast. The contours give location uncertainties of the mainshock(magenta) and aftershock (turquoise). The aftershock locates between the trench and the coast, where the seafloor begins to shallow. Virtually all the discovered aftershocks occur between oceanic trenches and the coast.
How could these large aftershocks have been overlooked?
There are no large, immediate, and remote aftershocks in any seismic catalog, and so most of us had concluded that this must be exceedingly rare, or is non-existent. That’s because today, any M≥4.5 shock anywhere on Earth can be reliably detected.
But, during the first few minutes after a large mainshock, its seismic wave train could obscure aftershocks, and so during this period, large shocks could conceivably have been hidden. To overcome this obstacle, Fan and Shearer used a technique in which a continent’s worth of seismometers are retroactively trained on the site of a single large earthquake halfway around the globe, and then used to track where the seismic energy was released in time. The method, called ‘beam back-projection,’ was introduced by Miaki Ishii, Peter Shearer, Heidi Houston and John Vidale in 2005 (Ishii et al., 2005). Although remote dynamic triggering of tiny aftershocks is well known (Velasco et al., 2008; Parsons et al., 2014), there are only a few examples of M≥5.5 aftershocks (Johnson et al., 2015), the most impressive of which was the 2012 M=8.6 Indian Ocean shock, which triggered large aftershocks all over the globe (Pollitz et al., 2012). But those aftershocks struck over several days—not minutes—long after the seismic waves had vanished.
Implications of the Fan-Shearer hypothesis
If they are correct, the hazard after a large mainshock would be more widespread than understood today, and the first several minutes after a large mainshock are more dangerous than we currently assume. But there is another, equally important, implication: For historical quakes, whose magnitudes and locations are interpreted from contemporary intensity reports (descriptions of shaking), we might be overestimating their magnitudes and blurring their locations, because widespread shaking in aftershocks would be misconstrued as caused by the mainshock.
A Challenge by Yue et al.
In October 2017, Han Yue, from Beijing University, Jorge C. Castellanos, Chunquan Yu, and Lingsen Meng from UCLA and Zhongwen Zhan from Caltech published a rebuttal of the Fan-Shearer hypothesis in Geophysical Research Letters. In a nutshell, Yue et al. argue that the seismic signals interpreted by Fan and Shearer as aftershocks are actually water reverberations in near-shore regions. The reverberations are triggered by the seismic waves launched by the mainshock. Fan and Shearer had raised this possibility in their paper, but ultimately dismissed it. Yue et al. present a series of falsification tests, but I am going to focus on what I consider the two most persuasive.
This is a simplified and annotated version of Fig. 3 of Yue et al. (2017). P waves transmit through rock and water, but S waves only through rock. So, if the energy pulses northeast (landward) of the trench were indeed aftershocks, they should appear in both panels, but they do not. ‘Seismic energy’ is the beam back-projection amplitude. The ‘+’ signs refer to the pulses in time shown in the figure below.
In the figure above, energy from a M=7.2 mainshock southwest of the trench is imaged by P waves. The energy is spread over about 100 km because this is roughly the rupture area of the shock. There are also strong energy pulses landward of the trench, near the ‘10 s resonance contour.’ These are the pulses identified by Fan and Shearer as aftershocks. But Yue et al. point out that if these were aftershocks, they should also appear when using S waves. But they are absent in the right-hand panel above. If, instead, they were water reverberations, they should appear in the P wave panel but not in the S wave panel, because S waves do not transmit in water. So, this would seem to be a very strong test, which the Fan-Shearer hypothesis does not pass.
Singing seismograms
In a second falsification test, Yue et al lined up seismograms of the M=7.2 mainshock recorded throughout the hemisphere. It takes about 25 s for a M=7.2 earthquake to rupture, and in those first 20-30 seconds, one sees the somewhat chaotic signature of the rupture. But beginning at 61 s (and perhaps at 51 s) one can see a coherent pulse on all the records (the red ‘+’ signs in the figure below). This pattern repeats at least three times at 10 s intervals (green, blue, and cyan ‘+’ signs in the figure below).
This is a simplified and annotated version of Yue et al. Fig. 2. Seismograms from throughout the hemisphere show coherent reverberations every 10 s. This becomes evident 61 s after the mainshock, and lasts at least until 92 s. This rhythmic ringing is unlikely to be caused by an earthquake, whose oscillations would normally be much more irregular. Yue et al. located the source of the ringing; those ‘+’ icons are shown in the preceding figure.
Yue et al argue that earthquakes do not produce such simple and periodic pulses. When Yue et al. located the source of the pulses, they land next to the beam back-projection energy pulses that Fan and Shearer identified as aftershocks. In addition, the pulses are very close to the seafloor depth contour that would produce the observed 10 s resonance. So, it would be hard to argue that water reverberation was not occurring, and occurring right where Fan and Shearer identified aftershocks.
Dueling posters at the Fall Meeting of the American Geophysical Union
Fortunately, Wenyuan Fan (now a Post-Doctoral Scholar at Woods Hole Oceanographic Institution) and Han Yue presented side-by-side posters at AGU Meeting in New Orleans two weeks ago. This gave everyone the chance to see both sides of the story, and it also enabled me to pose questions to each author based on the arguments and rebuttals of the other.
Fan and Shearer believe that with more tuning, an aftershock energy pulse might emerge in the S wave analysis. They now concede that water reverberations are evident in the signals, but they argue that these are water reverberations from the remote aftershock, not the mainshock. So, while remote dynamic aftershocks might be less common than they originally proposed, it still occurs. Han Yue says that he cannot (yet) eliminate this possibility, and so the debate continues.
So, who’s right?
In addition to the falsification tests, two other factors lead me to believe that Yue et al. are likely correct, and that few if any of the signals are actually aftershocks. Why would different types of mainshocks (thrust, extensional, and strike-slip) all trigger aftershocks at about the same water depth between the trench and the coast? This just seems very unlikely. Beyond that, if there is a simpler, quotidian explanation for a phenomenon (water reverberation), then it should be favored over a more exotic interpretation (heretofore unseen aftershocks).
With that said, debates like this are essential to science, which only advances when bold new ideas are promulgated, and promulgated in a manner that can be unambiguously tested. And for that we can thank Fan and Shearer. All we can really do in science is falsify hypotheses; proving something right is extremely difficult.
Here is a video of seismic wave propagation through rock and water by Yue et al., 2017
The first ring-like wave launched from the 20-km deep hypocenter is the P wave, traveling at about 7 km/sec; the second is S wave, traveling at about half that speed. The video is moving at about realtime. The thick black line is the seafloor. At the site of the epicenter, the seafloor is about 5 km (3 mi) deep. The camera moves with the advancing waves toward the coast. Water reverberations become most pronounced when the seafloor shallows to about 2 km deep, at a distance of about 220 km. The P waves bounce back and forth every 10 s or so in the water.
References
[Unfortunately the writer does not simply give us the links, but you can retrieve them with the references that they do give.]
Wenyuan Fan and Peter M. Shearer (2016), Local near instantaneously dynamically triggered aftershocks of large earthquakes, Science, 353, 1133-1136, DOI: 10.1126/science.aag0013.
Miaki Ishii, Peter M. Shearer, Heidi Houston, and John E. Vidale, Extent, duration and speed of the 2004 Sumatra-Andaman earthquake imaged by the HI-Net array (2005), Nature, DOI: 10.1038/nature03675.
Christopher W. Johnson, R. Bürgmann, and F. F. Pollitz (2015), Rare dynamic triggering of remote M≥ 5.5 earthquakes from global catalog analysis, J. Geophys. Res., 120, 1748–1761, doi:10.1002/ 2014JB011788.
Tom Parsons, Margaret Segou, Warner Marzocchi (2014), The global aftershock zone, Tectonophysics, 618, 1–34, .doi.org/10.1016/j.tecto.2014.01.038
Fred F. Pollitz, Ross S. Stein, Volkan Sevilgen, and Roland Bürgmann (2012), The 11 April 2012 east Indian Ocean earthquake triggered large aftershocks worldwide, Nature, 490, 250–253, DOI:10.1038/nature11504.
Aaron A. Velasco, S. Hernandez, T. Parsons, and K. Pankow (2008), Global ubiquity of dynamic earthquake triggering, Nature Geoscience, 1, 375–379, doi:10.1038/ngeo204
Han Yue, Jorge C. Castellanos, Chunquan Yu, Lingsen Meng, and Zhongwen Zhan (2017), Localized water reverberation phases and its impact on backprojection images, Geophys. Res. Letts., DOI: 10.1002/2017GL073254.
The Quake-Catcher Network is a collaborative initiative for developing the world’s largest, low-cost strong-motion seismic network by utilizing sensors in and attached to internet-connected computers. With your help, the Quake-Catcher Network can provide better understanding of earthquakes, give early warning to schools, emergency response systems, and others. The Quake-Catcher Network also provides educational software designed to help teach about earthquakes and earthquake hazards.
After almost eight years at Stanford, and a year at CalTech, the QCN project is moving to the University of Southern California Dept. of Earth Sciences. QCN will be sponsored by the Incorporated Research Institutions for Seismology (IRIS) and the Southern California Earthquake Center (SCEC).
The Quake-Catcher Network is a distributed computing network that links volunteer hosted computers into a real-time motion sensing network. QCN is one of many scientific computing projects that runs on the world-renowned distributed computing platform Berkeley Open Infrastructure for Network Computing (BOINC).
The volunteer computers monitor vibrational sensors called MEMS accelerometers, and digitally transmit “triggers” to QCN’s servers whenever strong new motions are observed. QCN’s servers sift through these signals, and determine which ones represent earthquakes, and which ones represent cultural noise (like doors slamming, or trucks driving by).
There are two categories of sensors used by QCN: 1) internal mobile device sensors, and 2) external USB sensors.
Mobile Devices: MEMS sensors are often included in laptops, games, cell phones, and other electronic devices for hardware protection, navigation, and game control. When these devices are still and connected to QCN, QCN software monitors the internal accelerometer for strong new shaking. Unfortunately, these devices are rarely secured to the floor, so they may bounce around when a large earthquake occurs. While this is less than ideal for characterizing the regional ground shaking, many such sensors can still provide useful information about earthquake locations and magnitudes.
USB Sensors: MEMS sensors can be mounted to the floor and connected to a desktop computer via a USB cable. These sensors have several advantages over mobile device sensors. 1) By mounting them to the floor, they measure more reliable shaking than mobile devices. 2) These sensors typically have lower noise and better resolution of 3D motion. 3) Desktops are often left on and do not move. 4) The USB sensor is physically removed from the game, phone, or laptop, so human interaction with the device doesn’t reduce the sensors’ performance. 5) USB sensors can be aligned to North, so we know what direction the horizontal “X” and “Y” axes correspond to.
If you are a science teacher at a K-12 school, please apply for a free USB sensor and accompanying QCN software. QCN has been able to purchase sensors to donate to schools in need. If you are interested in donating to the program or requesting a sensor, click here.
Earthquake safety is a responsibility shared by billions worldwide. The Quake-Catcher Network (QCN) provides software so that individuals can join together to improve earthquake monitoring, earthquake awareness, and the science of earthquakes. The Quake-Catcher Network (QCN) links existing networked laptops and desktops in hopes to form the worlds largest strong-motion seismic network.
Below, the QCN Quake Catcher Network map
Earthquake country is beautiful and enticing
Almost everything we love about areas like the San Francisco bay area, the California Southland, Salt Lake City against the Wasatch range, Seattle on Puget Sound, and Portland, is brought to us by the faults. The faults have sculpted the ridges and valleys, and down-dropped the bays, and lifted the mountains which draw us to these western U.S. cities. So, we enjoy the fruits of the faults every day. That means we must learn to live with their occasional spoils: large but infrequent earthquakes. Becoming quake resilient is a small price to pay for living in such a great part of the world, and it is achievable at modest cost.
A personal solution to a global problem
Half of the world’s population lives near active faults, but most of us are unaware of this. You can learn if you are at risk and protect your home, land, and family.
Temblor enables everyone in the continental United States, and many parts of the world, to learn their seismic, landslide, tsunami, and flood hazard. We help you determine the best way to reduce the risk to your home with proactive solutions.
Earthquake maps, soil liquefaction, landslide zones, cost of earthquake damage
In our iPhone and Android and web app, Temblor estimates the likelihood of seismic shaking and home damage. We show how the damage and its costs can be decreased by buying or renting a seismically safe home or retrofitting an older home.
Please share Temblor with your friends and family to help them, and everyone, live well in earthquake country.
Temblor is free and ad-free, and is a 2017 recipient of a highly competitive Small Business Innovation Research (‘SBIR’) grant from the U.S. National Science Foundation.
ShakeAlert: An Earthquake Early Warning System for the West Coast of the United States
The U. S. Geological Survey (USGS) along with a coalition of State and university partners is developing and testing an earthquake early warning (EEW) system called ShakeAlert for the west coast of the United States. Long term funding must be secured before the system can begin sending general public notifications, however, some limited pilot projects are active and more are being developed. The USGS has set the goal of beginning limited public notifications in 2018.
Watch a video describing how ShakeAlert works in English or Spanish.
Earthquakes pose a national challenge because more than 143 million Americans live in areas of significant seismic risk across 39 states. Most of our Nation’s earthquake risk is concentrated on the West Coast of the United States. The Federal Emergency Management Agency (FEMA) has estimated the average annualized loss from earthquakes, nationwide, to be $5.3 billion, with 77 percent of that figure ($4.1 billion) coming from California, Washington, and Oregon, and 66 percent ($3.5 billion) from California alone. In the next 30 years, California has a 99.7 percent chance of a magnitude 6.7 or larger earthquake and the Pacific Northwest has a 10 percent chance of a magnitude 8 to 9 megathrust earthquake on the Cascadia subduction zone.
Part of the Solution
Today, the technology exists to detect earthquakes, so quickly, that an alert can reach some areas before strong shaking arrives. The purpose of the ShakeAlert system is to identify and characterize an earthquake a few seconds after it begins, calculate the likely intensity of ground shaking that will result, and deliver warnings to people and infrastructure in harm’s way. This can be done by detecting the first energy to radiate from an earthquake, the P-wave energy, which rarely causes damage. Using P-wave information, we first estimate the location and the magnitude of the earthquake. Then, the anticipated ground shaking across the region to be affected is estimated and a warning is provided to local populations. The method can provide warning before the S-wave arrives, bringing the strong shaking that usually causes most of the damage.
Studies of earthquake early warning methods in California have shown that the warning time would range from a few seconds to a few tens of seconds. ShakeAlert can give enough time to slow trains and taxiing planes, to prevent cars from entering bridges and tunnels, to move away from dangerous machines or chemicals in work environments and to take cover under a desk, or to automatically shut down and isolate industrial systems. Taking such actions before shaking starts can reduce damage and casualties during an earthquake. It can also prevent cascading failures in the aftermath of an event. For example, isolating utilities before shaking starts can reduce the number of fire initiations.
System Goal
The USGS will issue public warnings of potentially damaging earthquakes and provide warning parameter data to government agencies and private users on a region-by-region basis, as soon as the ShakeAlert system, its products, and its parametric data meet minimum quality and reliability standards in those geographic regions. The USGS has set the goal of beginning limited public notifications in 2018. Product availability will expand geographically via ANSS regional seismic networks, such that ShakeAlert products and warnings become available for all regions with dense seismic instrumentation.
Current Status
The West Coast ShakeAlert system is being developed by expanding and upgrading the infrastructure of regional seismic networks that are part of the Advanced National Seismic System (ANSS); the California Integrated Seismic Network (CISN) is made up of the Southern California Seismic Network, SCSN) and the Northern California Seismic System, NCSS and the Pacific Northwest Seismic Network (PNSN). This enables the USGS and ANSS to leverage their substantial investment in sensor networks, data telemetry systems, data processing centers, and software for earthquake monitoring activities residing in these network centers. The ShakeAlert system has been sending live alerts to “beta” users in California since January of 2012 and in the Pacific Northwest since February of 2015.
In February of 2016 the USGS, along with its partners, rolled-out the next-generation ShakeAlert early warning test system in California joined by Oregon and Washington in April 2017. This West Coast-wide “production prototype” has been designed for redundant, reliable operations. The system includes geographically distributed servers, and allows for automatic fail-over if connection is lost.
This next-generation system will not yet support public warnings but does allow selected early adopters to develop and deploy pilot implementations that take protective actions triggered by the ShakeAlert notifications in areas with sufficient sensor coverage.
Authorities
The USGS will develop and operate the ShakeAlert system, and issue public notifications under collaborative authorities with FEMA, as part of the National Earthquake Hazard Reduction Program, as enacted by the Earthquake Hazards Reduction Act of 1977, 42 U.S.C. §§ 7704 SEC. 2.
For More Information
Robert de Groot, ShakeAlert National Coordinator for Communication, Education, and Outreach rdegroot@usgs.gov
626-583-7225
The intensity distribution across Japan on the Japanese seven-point scale from the 680-kilometer-deep earthquake near the Ogasawara Islands. Credit: Japan Meteorological Agency
On 30 May 2015, a powerful earthquake struck west of Japan’s remote Ogasawara (Bonin) island chain, which lies more than 800 kilometers south of Tokyo. Although it caused little damage, the magnitude 7.9 quake was noteworthy for being the deepest major earthquake ever recorded—it occurred more than 100 kilometers below any previously observed seismicity along the subducting Pacific Plate—and the first earthquake felt in every Japanese prefecture since observations began in 1884.
The 680-kilometer-deep earthquake was also notable for its unusual ground motion. Instead of producing a band of high-frequency (>1 hertz) seismic waves concentrated along northern Japan’s east coast, as is typical for deep subduction-related earthquakes in this region, this event generated strong, low-frequency waves that jolted a broad area up to 2,000 kilometers from the epicenter. To explain this uncharacteristic wavefield, Furumura and Kennett [Journal of Geophysical Research] analyzed ground motion records from across the country and compared the results to observations from a much shallower, magnitude 6.8 earthquake that occurred within the Pacific slab in the same area in 2010.
The results indicated that the peculiar ground motion associated with the 2015 earthquake was due to its great source depth as well as its location outside of the subducting slab. The team found that the ultradeep event was missing high-frequency components and generated milder ground motions at regional distances, whereas the 2010 earthquake included the high-frequency components but was narrowly focused.
After contrasting three-dimensional numerical simulations of seismic wave propagation from both events, the researchers concluded that waves originating from a deep source outside of the slab can develop a distinctive, low-frequency wavefield as they interact with continental crust and the region’s subducting slabs. Because this wavefield is usually concealed by higher-frequency, slab-guided waves, the few existing examples of this phenomenon will likely provide valuable information on local crustal structure and, in the case of the 2015 Ogasawara event, the morphology of the Pacific Plate.
The Quake-Catcher Network is a collaborative initiative for developing the world’s largest, low-cost strong-motion seismic network by utilizing sensors in and attached to internet-connected computers. With your help, the Quake-Catcher Network can provide better understanding of earthquakes, give early warning to schools, emergency response systems, and others. The Quake-Catcher Network also provides educational software designed to help teach about earthquakes and earthquake hazards.
After almost eight years at Stanford, and a year at CalTech, the QCN project is moving to the University of Southern California Dept. of Earth Sciences. QCN will be sponsored by the Incorporated Research Institutions for Seismology (IRIS) and the Southern California Earthquake Center (SCEC).
The Quake-Catcher Network is a distributed computing network that links volunteer hosted computers into a real-time motion sensing network. QCN is one of many scientific computing projects that runs on the world-renowned distributed computing platform Berkeley Open Infrastructure for Network Computing (BOINC).
The volunteer computers monitor vibrational sensors called MEMS accelerometers, and digitally transmit “triggers” to QCN’s servers whenever strong new motions are observed. QCN’s servers sift through these signals, and determine which ones represent earthquakes, and which ones represent cultural noise (like doors slamming, or trucks driving by).
There are two categories of sensors used by QCN: 1) internal mobile device sensors, and 2) external USB sensors.
Mobile Devices: MEMS sensors are often included in laptops, games, cell phones, and other electronic devices for hardware protection, navigation, and game control. When these devices are still and connected to QCN, QCN software monitors the internal accelerometer for strong new shaking. Unfortunately, these devices are rarely secured to the floor, so they may bounce around when a large earthquake occurs. While this is less than ideal for characterizing the regional ground shaking, many such sensors can still provide useful information about earthquake locations and magnitudes.
USB Sensors: MEMS sensors can be mounted to the floor and connected to a desktop computer via a USB cable. These sensors have several advantages over mobile device sensors. 1) By mounting them to the floor, they measure more reliable shaking than mobile devices. 2) These sensors typically have lower noise and better resolution of 3D motion. 3) Desktops are often left on and do not move. 4) The USB sensor is physically removed from the game, phone, or laptop, so human interaction with the device doesn’t reduce the sensors’ performance. 5) USB sensors can be aligned to North, so we know what direction the horizontal “X” and “Y” axes correspond to.
If you are a science teacher at a K-12 school, please apply for a free USB sensor and accompanying QCN software. QCN has been able to purchase sensors to donate to schools in need. If you are interested in donating to the program or requesting a sensor, click here.
Earthquake safety is a responsibility shared by billions worldwide. The Quake-Catcher Network (QCN) provides software so that individuals can join together to improve earthquake monitoring, earthquake awareness, and the science of earthquakes. The Quake-Catcher Network (QCN) links existing networked laptops and desktops in hopes to form the worlds largest strong-motion seismic network.
Below, the QCN Quake Catcher Network map
ShakeAlert: An Earthquake Early Warning System for the West Coast of the United States
The U. S. Geological Survey (USGS) along with a coalition of State and university partners is developing and testing an earthquake early warning (EEW) system called ShakeAlert for the west coast of the United States. Long term funding must be secured before the system can begin sending general public notifications, however, some limited pilot projects are active and more are being developed. The USGS has set the goal of beginning limited public notifications in 2018.
Watch a video describing how ShakeAlert works in English or Spanish.
Earthquakes pose a national challenge because more than 143 million Americans live in areas of significant seismic risk across 39 states. Most of our Nation’s earthquake risk is concentrated on the West Coast of the United States. The Federal Emergency Management Agency (FEMA) has estimated the average annualized loss from earthquakes, nationwide, to be $5.3 billion, with 77 percent of that figure ($4.1 billion) coming from California, Washington, and Oregon, and 66 percent ($3.5 billion) from California alone. In the next 30 years, California has a 99.7 percent chance of a magnitude 6.7 or larger earthquake and the Pacific Northwest has a 10 percent chance of a magnitude 8 to 9 megathrust earthquake on the Cascadia subduction zone.
Part of the Solution
Today, the technology exists to detect earthquakes, so quickly, that an alert can reach some areas before strong shaking arrives. The purpose of the ShakeAlert system is to identify and characterize an earthquake a few seconds after it begins, calculate the likely intensity of ground shaking that will result, and deliver warnings to people and infrastructure in harm’s way. This can be done by detecting the first energy to radiate from an earthquake, the P-wave energy, which rarely causes damage. Using P-wave information, we first estimate the location and the magnitude of the earthquake. Then, the anticipated ground shaking across the region to be affected is estimated and a warning is provided to local populations. The method can provide warning before the S-wave arrives, bringing the strong shaking that usually causes most of the damage.
Studies of earthquake early warning methods in California have shown that the warning time would range from a few seconds to a few tens of seconds. ShakeAlert can give enough time to slow trains and taxiing planes, to prevent cars from entering bridges and tunnels, to move away from dangerous machines or chemicals in work environments and to take cover under a desk, or to automatically shut down and isolate industrial systems. Taking such actions before shaking starts can reduce damage and casualties during an earthquake. It can also prevent cascading failures in the aftermath of an event. For example, isolating utilities before shaking starts can reduce the number of fire initiations.
System Goal
The USGS will issue public warnings of potentially damaging earthquakes and provide warning parameter data to government agencies and private users on a region-by-region basis, as soon as the ShakeAlert system, its products, and its parametric data meet minimum quality and reliability standards in those geographic regions. The USGS has set the goal of beginning limited public notifications in 2018. Product availability will expand geographically via ANSS regional seismic networks, such that ShakeAlert products and warnings become available for all regions with dense seismic instrumentation.
Current Status
The West Coast ShakeAlert system is being developed by expanding and upgrading the infrastructure of regional seismic networks that are part of the Advanced National Seismic System (ANSS); the California Integrated Seismic Network (CISN) is made up of the Southern California Seismic Network, SCSN) and the Northern California Seismic System, NCSS and the Pacific Northwest Seismic Network (PNSN). This enables the USGS and ANSS to leverage their substantial investment in sensor networks, data telemetry systems, data processing centers, and software for earthquake monitoring activities residing in these network centers. The ShakeAlert system has been sending live alerts to “beta” users in California since January of 2012 and in the Pacific Northwest since February of 2015.
In February of 2016 the USGS, along with its partners, rolled-out the next-generation ShakeAlert early warning test system in California joined by Oregon and Washington in April 2017. This West Coast-wide “production prototype” has been designed for redundant, reliable operations. The system includes geographically distributed servers, and allows for automatic fail-over if connection is lost.
This next-generation system will not yet support public warnings but does allow selected early adopters to develop and deploy pilot implementations that take protective actions triggered by the ShakeAlert notifications in areas with sufficient sensor coverage.
Authorities
The USGS will develop and operate the ShakeAlert system, and issue public notifications under collaborative authorities with FEMA, as part of the National Earthquake Hazard Reduction Program, as enacted by the Earthquake Hazards Reduction Act of 1977, 42 U.S.C. §§ 7704 SEC. 2.
For More Information
Robert de Groot, ShakeAlert National Coordinator for Communication, Education, and Outreach rdegroot@usgs.gov
626-583-7225
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