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  • richardmitnick 8:33 pm on November 16, 2017 Permalink | Reply
    Tags: , , Geology, ,   

    From phys.org: “Machine learning used to predict earthquakes in a lab setting” 

    physdotorg
    phys.org

    October 23, 2017

    1
    Aerial photo of the San Andreas Fault in the Carrizo Plain, northwest of Los Angeles. Credit: Wikipedia.

    A group of researchers from the UK and the US have used machine learning techniques to successfully predict earthquakes. Although their work was performed in a laboratory setting, the experiment closely mimics real-life conditions, and the results could be used to predict the timing of a real earthquake.

    The team, from the University of Cambridge, Los Alamos National Laboratory and Boston University, identified a hidden signal leading up to earthquakes, and used this ‘fingerprint’ to train a machine learning algorithm to predict future earthquakes. Their results, which could also be applied to avalanches, landslides and more, are reported in the journal Geophysical Review Letters.

    For geoscientists, predicting the timing and magnitude of an earthquake is a fundamental goal. Generally speaking, pinpointing where an earthquake will occur is fairly straightforward: if an earthquake has struck a particular place before, the chances are it will strike there again. The questions that have challenged scientists for decades are how to pinpoint when an earthquake will occur, and how severe it will be. Over the past 15 years, advances in instrument precision have been made, but a reliable earthquake prediction technique has not yet been developed.

    As part of a project searching for ways to use machine learning techniques to make gallium nitride (GaN) LEDs more efficient, the study’s first author, Bertrand Rouet-Leduc, who was then a PhD student at Cambridge, moved to Los Alamos National Laboratory in New Mexico to start a collaboration on machine learning in materials science between Cambridge University and Los Alamos. From there the team started helping the Los Alamos Geophysics group on machine learning questions.

    The team at Los Alamos, led by Paul Johnson, studies the interactions among earthquakes, precursor quakes (often very small earth movements) and faults, with the hope of developing a method to predict earthquakes. Using a lab-based system that mimics real earthquakes, the researchers used machine learning techniques to analyse the acoustic signals coming from the ‘fault’ as it moved and search for patterns.

    The laboratory apparatus uses steel blocks to closely mimic the physical forces at work in a real earthquake, and also records the seismic signals and sounds that are emitted. Machine learning is then used to find the relationship between the acoustic signal coming from the fault and how close it is to failing.

    The machine learning algorithm was able to identify a particular pattern in the sound, previously thought to be nothing more than noise, which occurs long before an earthquake. The characteristics of this sound pattern can be used to give a precise estimate (within a few percent) of the stress on the fault (that is, how much force is it under) and to estimate the time remaining before failure, which gets more and more precise as failure approaches. The team now thinks that this sound pattern is a direct measure of the elastic energy that is in the system at a given time.

    “This is the first time that machine learning has been used to analyse acoustic data to predict when an earthquake will occur, long before it does, so that plenty of warning time can be given – it’s incredible what machine learning can do,” said co-author Professor Sir Colin Humphreys of Cambridge’s Department of Materials Science & Metallurgy, whose main area of research is energy-efficient and cost-effective LEDs. Humphreys was Rouet-Leduc’s supervisor when he was a PhD student at Cambridge.

    “Machine learning enables the analysis of datasets too large to handle manually and looks at data in an unbiased way that enables discoveries to be made,” said Rouet-Leduc.

    Although the researchers caution that there are multiple differences between a lab-based experiment and a real earthquake, they hope to progressively scale up their approach by applying it to real systems which most resemble their lab system. One such site is in California along the San Andreas Fault, where characteristic small repeating earthquakes are similar to those in the lab-based earthquake simulator. Progress is also being made on the Cascadia fault in the Pacific Northwest of the United States and British Columbia, Canada, where repeating slow earthquakes that occur over weeks or months are also very similar to laboratory earthquakes.

    “We’re at a point where huge advances in instrumentation, machine learning, faster computers and our ability to handle massive data sets could bring about huge advances in earthquake science,” said Rouet-Leduc.

    See the full article here .

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    About Phys.org in 100 Words

    Phys.org™ (formerly Physorg.com) is a leading web-based science, research and technology news service which covers a full range of topics. These include physics, earth science, medicine, nanotechnology, electronics, space, biology, chemistry, computer sciences, engineering, mathematics and other sciences and technologies. Launched in 2004, Phys.org’s readership has grown steadily to include 1.75 million scientists, researchers, and engineers every month. Phys.org publishes approximately 100 quality articles every day, offering some of the most comprehensive coverage of sci-tech developments world-wide. Quancast 2009 includes Phys.org in its list of the Global Top 2,000 Websites. Phys.org community members enjoy access to many personalized features such as social networking, a personal home page set-up, RSS/XML feeds, article comments and ranking, the ability to save favorite articles, a daily newsletter, and other options.

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  • richardmitnick 12:23 pm on November 16, 2017 Permalink | Reply
    Tags: Geology, Study reveals structure and origins of glacial polish on Yosemite's rocks,   

    From UCSC: “Study reveals structure and origins of glacial polish on Yosemite’s rocks” 

    UC Santa Cruz

    UC Santa Cruz

    November 15, 2017
    Anna Katrina Hunter
    stephens@ucsc.edu

    Geologists at UC Santa Cruz investigated glacial polish from Yosemite National Park to understand how it formed and what it can tell them about how glaciers move.

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    Glacial polish reflects sunlight at Pothole Dome in Yosemite National Park, California. The granitic bedrock here was polished by glacier sliding during the last ice age. UCSC researchers found that glacial polish forms by the accretion of a thin coating layer on top of glacially abraded surfaces. (Photo by Shalev Siman-Tov)

    The glaciers that carved Yosemite Valley left highly polished surfaces on many of the region’s rock formations. These smooth, shiny surfaces, known as glacial polish, are common in the Sierra Nevada and other glaciated landscapes.

    Geologists at UC Santa Cruz have now taken a close look at the structure and chemistry of glacial polish and found that it consists of a thin coating smeared onto the rock as the glacier moved over it. The new findings, published in the November issue of Geology, show that the polish is not simply the result of abrasion and smoothing by the glacier, as was previously thought. Instead, it is a distinct layer deposited onto the surface of the rock at the base of the glacier.

    This smooth layer coating the rock at the base of glaciers may influence how fast the glaciers slide. It also helps explain why glacial polish is so resistant to weathering long after the glaciers that created it are gone.

    According to coauthor Emily Brodsky, professor of Earth and planetary sciences at UC Santa Cruz, this ultrathin coating can help glaciologists better understand the mechanics of how glaciers move, and it provides a potential archive for dating when the material was pasted onto the rock.

    “This is incredibly important now, as we think about the stability of ice sheets,” Brodsky said. “It is pretty hard to get to the base of a glacier to see what’s going on there, but the glacial polish can tell us about the composition of the gunk on the bottoms of glaciers and when the polish was formed.”

    Lead author Shalev Siman-Tov, a postdoctoral researcher at UC Santa Cruz, had previously studied the highly polished surfaces found on some earthquake faults. To investigate glacial polish, he teamed up with Greg Stock, who earned his Ph.D. at UC Santa Cruz and is now the park geologist at Yosemite National Park.

    “I wanted to apply what we know from fault zones and earthquakes to glaciology,” Siman-Tov said. “I was not familiar with glaciated landscapes, and I was very interested to conduct a significant field study outside of my home country of Israel.”

    He and Stock hiked into Yosemite National Park to collect small samples of glacial polish from dozens of sites. They chose samples from three sites for detailed analyses. One site (Daff Dome near Tuolomne Meadows) emerged from beneath the glaciers at the end of the last ice age around 15,000 years ago. The other two sites are in Lyell Canyon near small modern glaciers that formed during the Little Ice Age around 300 years ago. Lyell Glacier is no longer active, but McClure Glacier is still moving and has an ice cave at its toe that enabled the researchers to collect fresh polish from an area of active sliding and abrasion.

    The researchers used an ion beam to slice off thin sections from the samples, and they used electron microscopy techniques to image the samples and perform elemental analyses. The results showed that the tiny fragments in the coating were a mixture of all the minerals found in granodiorite bedrock. This suggests a process in which the glacier scrapes material from the rocks and grinds it into a fine paste, then spreads it across the rock surface to form a very thin layer only a few microns thick.

    “Abrasive wear removes material and makes the surface smoother, while simultaneously producing the wear products that become the construction material for the coating layer,” the researchers wrote in the paper.

    Siman-Tov now wants to date the layer and confirm the time when the glacier eroded the rock surface. He is also conducting laboratory experiments to try to recreate the same structures observed in the coating layer. The researchers will continue to work with Stock in Yosemite to study the chemical weathering of glacial polish surfaces compared to regular, exposed granodiorite.

    In addition to Siman-Tov, Stock, and Brodsky, the coauthors of the paper include geologist Joseph White at the University of New Brunswick. This work was funded in part by the Gordon and Betty Moore Foundation and the National Science Foundation.

    See the full article here .

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    UCO Lick Shane Telescope
    UCO Lick Shane Telescope interior
    Shane Telescope at UCO Lick Observatory, UCSC

    Lick Automated Planet Finder telescope, Mount Hamilton, CA, USA

    Lick Automated Planet Finder telescope, Mount Hamilton, CA, USA

    UC Santa Cruz campus
    The University of California, Santa Cruz, opened in 1965 and grew, one college at a time, to its current (2008-09) enrollment of more than 16,000 students. Undergraduates pursue more than 60 majors supervised by divisional deans of humanities, physical & biological sciences, social sciences, and arts. Graduate students work toward graduate certificates, master’s degrees, or doctoral degrees in more than 30 academic fields under the supervision of the divisional and graduate deans. The dean of the Jack Baskin School of Engineering oversees the campus’s undergraduate and graduate engineering programs.

    UCSC is the home base for the Lick Observatory.

    Lick Observatory's Great Lick 91-centimeter (36-inch) telescope housed in the South (large) Dome of main building
    Lick Observatory’s Great Lick 91-centimeter (36-inch) telescope housed in the South (large) Dome of main building

    Search for extraterrestrial intelligence expands at Lick Observatory
    New instrument scans the sky for pulses of infrared light
    March 23, 2015
    By Hilary Lebow
    1
    The NIROSETI instrument saw first light on the Nickel 1-meter Telescope at Lick Observatory on March 15, 2015. (Photo by Laurie Hatch) UCSC Lick Nickel telescope

    Astronomers are expanding the search for extraterrestrial intelligence into a new realm with detectors tuned to infrared light at UC’s Lick Observatory. A new instrument, called NIROSETI, will soon scour the sky for messages from other worlds.

    “Infrared light would be an excellent means of interstellar communication,” said Shelley Wright, an assistant professor of physics at UC San Diego who led the development of the new instrument while at the University of Toronto’s Dunlap Institute for Astronomy & Astrophysics.

    Wright worked on an earlier SETI project at Lick Observatory as a UC Santa Cruz undergraduate, when she built an optical instrument designed by UC Berkeley researchers. The infrared project takes advantage of new technology not available for that first optical search.

    Infrared light would be a good way for extraterrestrials to get our attention here on Earth, since pulses from a powerful infrared laser could outshine a star, if only for a billionth of a second. Interstellar gas and dust is almost transparent to near infrared, so these signals can be seen from great distances. It also takes less energy to send information using infrared signals than with visible light.

    5
    UCSC alumna Shelley Wright, now an assistant professor of physics at UC San Diego, discusses the dichroic filter of the NIROSETI instrument. (Photo by Laurie Hatch)

    Frank Drake, professor emeritus of astronomy and astrophysics at UC Santa Cruz and director emeritus of the SETI Institute, said there are several additional advantages to a search in the infrared realm.

    “The signals are so strong that we only need a small telescope to receive them. Smaller telescopes can offer more observational time, and that is good because we need to search many stars for a chance of success,” said Drake.

    The only downside is that extraterrestrials would need to be transmitting their signals in our direction, Drake said, though he sees this as a positive side to that limitation. “If we get a signal from someone who’s aiming for us, it could mean there’s altruism in the universe. I like that idea. If they want to be friendly, that’s who we will find.”

    Scientists have searched the skies for radio signals for more than 50 years and expanded their search into the optical realm more than a decade ago. The idea of searching in the infrared is not a new one, but instruments capable of capturing pulses of infrared light only recently became available.

    “We had to wait,” Wright said. “I spent eight years waiting and watching as new technology emerged.”

    Now that technology has caught up, the search will extend to stars thousands of light years away, rather than just hundreds. NIROSETI, or Near-Infrared Optical Search for Extraterrestrial Intelligence, could also uncover new information about the physical universe.

    “This is the first time Earthlings have looked at the universe at infrared wavelengths with nanosecond time scales,” said Dan Werthimer, UC Berkeley SETI Project Director. “The instrument could discover new astrophysical phenomena, or perhaps answer the question of whether we are alone.”

    NIROSETI will also gather more information than previous optical detectors by recording levels of light over time so that patterns can be analyzed for potential signs of other civilizations.

    “Searching for intelligent life in the universe is both thrilling and somewhat unorthodox,” said Claire Max, director of UC Observatories and professor of astronomy and astrophysics at UC Santa Cruz. “Lick Observatory has already been the site of several previous SETI searches, so this is a very exciting addition to the current research taking place.”

    NIROSETI will be fully operational by early summer and will scan the skies several times a week on the Nickel 1-meter telescope at Lick Observatory, located on Mt. Hamilton east of San Jose.

    The NIROSETI team also includes Geoffrey Marcy and Andrew Siemion from UC Berkeley; Patrick Dorval, a Dunlap undergraduate, and Elliot Meyer, a Dunlap graduate student; and Richard Treffers of Starman Systems. Funding for the project comes from the generous support of Bill and Susan Bloomfield.

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  • richardmitnick 11:38 pm on November 12, 2017 Permalink | Reply
    Tags: , , Geology, , , ShakeAlert: Earthquake Early Warning,   

    From temblor: “Damaging Magnitude 7.3 earthquake along the Iran-Iraq border was preceded by Magnitude 4.3 foreshock” 

    1

    temblor

    November 12, 2017
    Ross S. Stein, Ph.D., Temblor

    A powerful, shallow earthquake struck along the Iraq-Iran border today. Damage is currently largely unknown, but is likely heavy because of the poor capacity of rural housing in this region to resist seismic shaking. This was tragically demonstrated when the 2003 M=6.3 Bam, Iran, earthquake took 26,000 lives due to the almost complete collapse of ancient adobe dwellings. Also, a M=5.3 aftershock hit 10 minutes after the mainshock, which is large enough to bring down a building damaged by the first event.

    The quake struck in a region of very low background seismicity

    Although both Iraq and Iran are seismically active, and even though the quake lies only 100 km (60 mi) from the compressional boundary between the Arabian and Eurasian plates, there were no M≥4.5 quakes within about 60 km (35 mi) of today’s epicenter during the past 20 years.

    1
    The completeness magnitude for this region is likely about M=4.5 since 1997, and so we use those to assess the background, or preceding seismicity, and find it to be very sparse.

    M=4.3 foreshock an hour before the mainshock

    Nevertheless, unless the EMSC catalog suffers from timing errors, there was a M=4.3 ‘foreshock’ one hour before the mainshock, located about 60 km (35 mi) to the southwest of the future mainshock. Given how low the background rate is, this occurrence might indicate that the gently-dipping thrust fault on which the mainshock struck was undergoing precursory creep. The occurrence of foreshocks is rare, and as indicators of future mainshocks or even creep, unreliable.

    2
    The foreshock struck rather far from the mainshock, but could indicate deep precursory creep.

    Is this a very rare event?

    According to the ISC-GEM seismic catalog, the closest large events since 1900 were a pair of M=6.7 and M=6.8 events in 1957-1958, some 200 km (120 mi) to the southeast of today’s mainshock.

    Broadly, the Arabia tectonic plate is being shoved against Eurasia plate along the Bitlis Suture and Zagros fold belt at a speed of 26 mm/yr (1 in/yr). This is the same slip rate as the San Andreas Fault. But because the almost no M≥5.8 quakes struck in this region for the past 40 years, and the because local strain rate is not adequately measured by GPS, the Global Earthquake Activity Rate (GEAR) model shown in Temblor expects only a M=5.5-5.8 in a typical lifetime in this area. But the political and military conflicts in the region have prevented adequate GPS monitoring.

    3
    Today’s earthquake sequence struck along two borders: political and tectonic.

    So, either this event is indeed quite rare, or the absence of GPS data has created artificial quake ‘hole’ in the GEAR model.

    References: USGS ANSS catalog, ESMC catalog, ISC-GEM catalog
    Sorry, no links.

    See the full article here .

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    You can help many citizen scientists in detecting earthquakes and getting the data to emergency services people in affected area.
    QCN bloc

    Quake-Catcher Network

    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).

    BOINCLarge

    BOINC WallPaper

    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.

    BOINC is a leader in the field(s) of Distributed Computing, Grid Computing and Citizen Cyberscience.BOINC is more properly the Berkeley Open Infrastructure for Network Computing, developed at UC Berkeley.

    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
    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: Earthquake Early Warning

    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 by 2018.

    The primary project partners include:

    United States Geological Survey
    California Governor’s Office of Emergency Services (CalOES)
    California Geological Survey
    California Institute of Technology
    University of California Berkeley
    University of Washington
    University of Oregon
    Gordon and Betty Moore Foundation

    The Earthquake Threat

    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, depending on the distance to the epicenter of the earthquake. For very large events like those expected on the San Andreas fault zone or the Cascadia subduction zone the warning time could be much longer because the affected area is much larger. ShakeAlert can give enough time to slow and stop 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 by 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” test 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. This “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

     
  • richardmitnick 1:40 pm on November 4, 2017 Permalink | Reply
    Tags: , , Geology, John Vidale comes to USC Dornsife, ,   

    From USC Dornsife: “Renowned seismologist, John Vidale, joins USC to lead Southern California Earthquake Center” 

    USC bloc

    USC Dornsife

    November 1, 2017
    Michelle Boston
    msboston@dornsife.usc.edu

    Powered by a network of more than one thousand researchers from around the world, the center is at the forefront of earthquake system science.

    1
    John Vidale, an expert in earthquake early warning systems, has been named director of the Southern California Earthquake Center at USC Dornsife. Photo by Mike Glier.

    John Vidale first took an interest in geology as a junior in college. He began as a physics major — a subject he had adored throughout high school — but was frustrated by how ethereal his coursework was in college. So, he enrolled in a geology class. The subject excited him because it was grounded in what he could see and touch.

    “I could go look at rocks,” Vidale said. “I could evaluate things that are deep under my feet. I could look at the Earth’s history through geology. That was very appealing.”

    His senior year in college, Vidale took 11 geology classes, adding geology as another major alongside physics and a minor in economics. In particular, he was fascinated by the study of seismic waves — surges of energy traveling through the Earth.

    “In geology, the tool with the highest resolution is seismology,” Vidale said. “Like taking an X-ray of the Earth, I can see exactly what’s going on and where.”

    With three decades of experience studying Earth science, Vidale’s research focuses on anything related to seismic waves, from nuclear explosions to landslides to glaciers, and of course, earthquakes.

    Beginning this October, Vidale is taking on a new role involving the study of earthquakes as the new director of the Southern California Earthquake Center (SCEC) based at USC Dornsife. He will also serve as Dean’s Professor of Earth Sciences at USC Dornsife.

    Looking for shakier ground

    Vidale was drawn to SCEC for many reasons, but perhaps above all was its location. Coming from the Pacific Northwest, where he lived prior to leading SCEC, one could experience earthquakes, volcanoes, landslides and tsunamis, but the appeal for him was to be in the thick of it where there is a high rate of earthquakes — Southern California.

    “To learn about earthquakes we have to have earthquakes,” Vidale said. “Southern California is the heart of the science of seismology.”

    Vidale, who this year was elected into the National Academy of Sciences, brings with him significant expertise. He is a member of the National Earthquake Prediction Evaluation Council and most recently served as director of the Pacific Northwest Seismic Network at the University of Washington in Seattle where he was also a professor. There, he was instrumental in developing the ShakeAlert Earthquake Early Warning System, which seeks to warn government agencies about earthquakes along the west coast of the U.S.

    Previously, he taught at UCLA, where he served as director of the Institute of Geophysics and Planetary Physics. He was also a researcher at the U.S. Geological Survey in Menlo Park, Calif., and the University of California, Santa Cruz.

    He earned his undergraduate degree from Yale University, and a Ph.D. in seismology from the California Institute of Technology.

    Vidale is a tremendous asset for everyone who lives in earthquake-prone Southern California, and a great addition to USC Dornsife’s faculty, said Stephen Bradforth, divisional dean for natural sciences and mathematics.

    “John Vidale provides deep expertise in earthquake science and leadership in earthquake early warning systems that position him to carry on the success and growth of SCEC,” he said.

    A global force on earthquakes

    Funded by the National Science Foundation and the U.S. Geological Survey (USGS), the Southern California Earthquake Center brings together a network of more than 1,000 earthquake researchers from around the world to understand how earthquakes work and to offer models for forecasting when and where large temblors might occur.

    SCEC crafted an innovative model for forecasting earthquakes called the Uniform California Earthquake Rupture Forecast (UCERF), developed in concert with USGS and the California Geological Survey. UCERF represents the most authoritative estimates of the magnitude, location and likelihood of earthquakes in California.

    SCEC also coordinates the Great ShakeOut Earthquake Drills, an annual global disaster preparedness event that helps individuals and organizations around the world get ready for the next major earthquake. The center also has a significant education component that touches all levels of learners. Undergraduates can intern with SCEC while K–12 students can join the citizen-science Quake Catcher Network, in which volunteers place earthquake-monitoring sensors in their classrooms or homes to collect seismic data.

    A solid foundation

    SCEC was most recently led by Thomas Jordan, University Professor, William M. Keck Foundation Chair in Geological Sciences and professor of Earth sciences.

    Jordan served as director of SCEC for 15 years, overseeing enormous strides in earthquake science. He established the international Collaboratory for the Study of Earthquake Predictability and, since 2006, has been the lead SCEC investigator on projects to create and improve the Uniform California Earthquake Rupture Forecast. He is a member of the California Earthquake Prediction Evaluation Council and the Board of Directors of the Seismological Society of America, and was appointed by the Italian government to chair the International Commission on Earthquake Forecasting for Civil Protection.

    His current research is focused on models of earthquake processes, earthquake forecasting, continental dynamics, full-3D waveform tomography, and seismology. He continues to play a major role in the path-breaking research taking place at SCEC.

    “Like basketball’s Michael Jordan, Tom Jordan is an iconic leader. He has pushed earthquake system science at SCEC to international prominence through his scientific acumen, creativity and leadership,” Bradforth said. “USC Dornsife continues to benefit greatly in Tom’s mentoring of the new generation of geophysics faculty coming to Earth sciences and his continuing thought-leadership in global earthquake science.”

    See the full article here .

    YOU CAN HELP CATCH EARTHQUAKES AS THEY HAPPEN RIGHT NOW

    QCN bloc

    Quake-Catcher Network

    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).

    BOINCLarge

    BOINC WallPaper

    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.

    BOINC is a leader in the field(s) of Distributed Computing, Grid Computing and Citizen Cyberscience.BOINC is more properly the Berkeley Open Infrastructure for Network Computing, developed at UC Berkeley.

    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
    QCN Quake Catcher Network map

    Please help promote STEM in your local schools.

    STEM Icon

    Stem Education Coalition

    USC campus

    The University of Southern California is one of the world’s leading private research universities. An anchor institution in Los Angeles, a global center for arts, technology and international business, USC’s diverse curricular offerings provide extensive opportunities for interdisciplinary study, and collaboration with leading researchers in highly advanced learning environments. With a strong tradition of integrating liberal and professional education, USC fosters a vibrant culture of public service and encourages students to cross academic as well as geographic boundaries in their pursuit of knowledge.

     
  • richardmitnick 1:19 pm on November 4, 2017 Permalink | Reply
    Tags: , Barberton Mountains of South Africa, Geology, Komatiite might fill in some gaps in our knowledge of Earth history, LSU-Louisiana State University   

    From LSU: “LSU Researchers Discover Minerals in Volcanic Rock that May Offer New Insights into the First 1.5 Billion Years of Earth’s Evolution” 

    Louisiana State University

    1
    Photomicrographs of fresh olivine (large green, blue and pink crystals) and glass inclusion (lower left inset). Komatiite volcanic rocks from the 3.3 billion-year-old Weltevreden Formation are the freshest yet discovered in from Earth’s early Archean. Trace elements, radiogenic and stable isotopes from these rocks and olivine separates provide key evidence for evolution of Earth’s mantle.Photo provided by Keen Kareem

    The first 1.5 billion years of Earth’s evolution is subject to considerable uncertainty due to the lack of any significant rock record prior to four billion years ago and a very limited record until about three billion years ago. Rocks of this age are usually extensively altered making comparisons to modern rock quite difficult. In new research conducted at LSU, scientists have found evidence showing that komatiites, three-billion-year old volcanic rock found within the Earth’s mantle, had a different composition than modern ones. Their discovery may offer new information about the first one billion years of Earth’s development and early origins of life.

    Results of the team’s work has been published in the October 2017 edition of NATURE Geoscience.

    The basic research came from more than three decades of LSU scientists studying and mapping the Barberton Mountains of South Africa. The research team, including LSU geology professors Gary Byerly and Huiming Bao, geology PhD graduate Keena Kareem, and LSU researcher Benjamin Byerly, conducted chemical analyses of hundreds of komatiite rocks sampled from about 10 lava flows.

    “Early workers had mapped large areas incorrectly by assuming they were correlatives to the much more famous Komati Formation in the southern part of the mountains. We recognized this error and began a detailed study of the rocks to prove our mapping-based interpretations,” said Gary Byerly.

    Within the rocks, they discovered original minerals called fresh olivine, which had been preserved in remarkable detail. Though the mineral is rarely found in rocks subjected to metamorphism and surface weathering, olivine is the major constituent of Earth’s upper mantle and controls the nature of volcanism and tectonism of the planet. Using compositions of these fresh minerals, the researchers had previously concluded that these were the hottest lavas to ever erupt on Earth’s surface with temperatures near 1600 degrees centigrade, which is roughly 400 degrees hotter than modern eruptions in Hawaii.

    “Discovering fresh unaltered olivine in these ancient lavas was a remarkable find. The field work was wonderfully productive and we were eager to return to the lab to use the chemistry of these preserved olivine crystals to reveal clues of the Archean Mantle,” said Kareem

    The researchers suggest that maybe a chunk of early-Earth magma ocean is preserved in the approximately 3.2 billion year-old minerals.

    “The modern Earth shows little or no evidence of this early magma ocean because convection of the mantle has largely homogenized the layering produced in the magma ocean. Oxygen isotopes in these fresh olivines support the existence of ancient chunks of the frozen magma ocean. Rocks like this are very rare and scientifically valuable. An obvious next step was to do oxygen isotopes,” said Byerly.

    This study grew out of work taking place in LSU’s laboratory for the study of oxygen isotopes, a world-class facility that attracts scientists from the U.S. and international institutions for collaborative work. The results of the study were so unusual that it required extra care to be certain of the results. Huiming Bao, who is also the head of LSU’s oxygen isotopes lab, said that the team triple and quadruple checked the data by running with different reference minerals and by calibrating with other independent labs.

    “We attempted to reconcile the findings with some of the conventional explanations for lavas with oxygen isotope compositions like these, but nothing could fully explain all of the observations. It became apparent that these rocks preserve signatures of processes that occurred over four billion years ago and that are still not completely understood,” said Benjamin Byerly.

    Oxygen isotopes are measured by the conversion of rock or minerals into a gas and measuring the ratios of oxygen with the different masses of 16, 17, and 18. A variety of processes fractionate oxygen on Earth and in the Solar System, including atmospheric, hydrospheric, biological, and high temperature and pressure.

    “Different planets in our solar system have different oxygen isotope ratios. On Earth this is modified by surface atmosphere and hydrosphere, so variations could be due either to heterogeneous mantle (original accumulation of planetary debris or remnants of magma ocean) or surface processes,” said Gary Byerly. “Either might be interesting to study. The latter because it would also provide information about the early surface temperature of Earth and early origins of life.”

    This work was supported by a National Science Foundation grant awarded to Gary Byerly, a NASA grant awarded to Bao, and general support from LSU.

    3
    Left to right: Benjamin Byerly (foreground) and Gary Byerly (background) examine komatiite volcanic rocks from the Barberton Mountains of South Africa, Keena Kareem and Gary Byerly (top right), and Huiming Bao standing next to the specially designed laser fluorination line used to isolate oxygen extracted from minerals

    Photos provided by Gary Byerly

    See the full article here .

    Please help promote STEM in your local schools.

    STEM Icon

    Stem Education Coalition

    Louisiana State University (officially Louisiana State University and Agricultural and Mechanical College, commonly referred to as LSU) is a public coeducational university located in Baton Rouge, Louisiana. The university was founded in 1853 in what is now known as Pineville, Louisiana, under the name Louisiana State Seminary of Learning & Military Academy. The current LSU main campus was dedicated in 1926, consists of more than 250 buildings constructed in the style of Italian Renaissance architect Andrea Palladio, and occupies a 650-acre (2.6 km²) plateau on the banks of the Mississippi River.

    LSU is the flagship institution of the Louisiana State University System. In 2017, the university enrolled over 25,000 undergraduate and over 5,000 graduate students in 14 schools and colleges. Several of LSU’s graduate schools, such as the E.J. Ourso College of Business and the Paul M. Hebert Law Center, have received national recognition in their respective fields of study. Designated as a land-grant, sea-grant and space-grant institution, LSU is also noted for its extensive research facilities, operating some 800 sponsored research projects funded by agencies such as the National Institutes of Health, the National Science Foundation, the National Endowment for the Humanities, and the National Aeronautics and Space Administration.

    LSU’s athletics department fields teams in 21 varsity sports (9 men’s, 12 women’s), and is a member of the NCAA (National Collegiate Athletic Association) and the SEC (Southeastern Conference). The university is represented by its mascot, Mike the Tiger.

     
  • richardmitnick 12:03 pm on October 20, 2017 Permalink | Reply
    Tags: , , , Geology, How to Trigger a Massive Earthquake   

    From Eos: “How to Trigger a Massive Earthquake” 

    AGU bloc

    AGU
    Eos news bloc

    Eos

    19 October 2017
    Lucas Joel

    Humans may be to blame for California’s second-largest 20th century earthquake, and a team of seismologists has now proposed how that could have happened.

    1
    A school in Kern County in California destroyed by the 1952 earthquake. A new study suggests that this earthquake could have been set off by nearby oil drilling activities, and it explains how that might have happened. Credit: NOAA National Geophysical Data Center

    A Los Angeles Times article published on 11 June 1952 tells of a successful new oil well at Wheeler Ridge in Kern County in California. The well operated for 98 days, but then, on 21 July at 4:52 a.m. local time, a 7.5-magnitude earthquake let loose beneath the well along the White Wolf fault. It was the second-largest earthquake in California in the 20th century, and it killed 12 people. A team of seismologists, reporting new research, thinks the oil drilling triggered the event. The work is the first to give a detailed explanation for how industrial activity could cause such a big earthquake, the researchers said.

    Taking oil out of the ground likely destabilized the White Wolf fault, triggering the Kern County quake, explained Susan Hough, a seismologist at the U.S. Geological Survey in Pasadena, Calif., and lead author of a study published this month in the Journal of Seismology.

    The work follows a 2016 Bulletin of the Seismological Society of America study in which Hough and a colleague suggest that oil drilling played a role in other historic southern California earthquakes, like the deadly 1933 6.4-magnitude Long Beach earthquake that killed 120 people. That study, however, lacked an explanation for how drilling could trigger such large quakes when modern experience shows that induced quakes rarely exceed a magnitude of even 5. This time, Hough and her colleagues propose a mechanism.

    Putting the Pieces Together

    Hough told Eos how she stumbled across old California state reports that give detailed accounts of oil drilling activity in southern California. The reports revealed evidence for a spatial and temporal association between oil industry activity and earthquakes. “From the industry data for the [oil] production volumes and the location of the well and the location of the [White Wolf] fault, we can show that the stress change on the fault would’ve been potentially significant,” she said.

    The stress change Hough refers to happened as the well pumped oil out of the ground. This, Hough explained, likely triggered the quake by “unclamping” the underlying fault. In this case, picture the fault as a fracture along an inclined plane where crustal blocks on opposite sides stall as they try to move past one another. “The fault is locked because there’s friction on the fault, and part of the reason for that is there’s the weight of the overlying crust on the fault plane,” said Hough. “But if you take some of that weight off, it shifts; it’s going to reduce the confining pressure…depending on the faults that are there, that could just destabilize what had been a locked fault.”

    2
    Oil wells line the Huntington Beach shoreline in southern California in 1926. In 1933, the 6.3-magnitude Long Beach earthquake struck, and according to seismologists, the temblor was likely due to oil drilling in the Huntington Beach region. Credit: Photo courtesy of Orange County Archives

    Liquids like oil, however, typically lubricate faults, making them more prone to slipping. So how could removing oil help trigger an earthquake? The answer lies in the structure of the rock layers beneath the well, which, Hough explained, prevented the oil’s lubricating effects from reaching the White Wolf fault. This means it was only a matter of removing the oily overburden that led to the fault destabilization.

    According to the team’s calculations, the amount of oil removed from above the fault generated a stress change of about 1 bar of pressure, a value that seismologists generally think of as the amount of stress change required to set an earthquake in motion, Hough explained. “After 80 days of drilling, the stress change was right at and exceeding that magic number that we think is significant,” she said.

    “They’ve developed a very plausible geologic scenario for how the Kern County earthquake could’ve been induced,” said Gillian Foulger, a geophysicist at Durham University in the United Kingdom, who was not involved in the work. “They’re really putting flesh on the bones for this particular earthquake.”

    Foulger also agrees that a modest change in the overlying weight could have been enough to set off the quake. “Earthquakes are a little bit like snow avalanches,” she said. “You can have a massive amount of snow pile up on a mountainside, and then you have a skier who skis across it and that’s just enough to trigger the disturbance that causes the whole lot to fall off.”

    Unlikely Recurrence

    Hough presents a model for initiating a large earthquake based on just one case example, although she thinks her work can apply to induced earthquakes in general: “It highlights the possibility that inducing any initial [earthquake] nucleation in proximity to a major fault could be the spark that detonates a larger rupture,” she said.

    “Nucleation” refers to the small change in stress needed to destabilize a fault—a stress change that could happen in oil-producing regions today. But the chances of producing another temblor in the manner of the Kern County earthquake are slim, according to Hough, mostly because oil fields tend not to sit above major fault lines. In addition, oil producers long ago changed to a standard practice of injecting water into the ground after oil removal, something that was not done at the Wheeler Ridge oil field and that could have restored much of the otherwise lost weight locking the fault.

    Most induced earthquakes are small—usually no bigger than a 4 magnitude—although there is no reason to suspect that humans cannot induce a big quake, explained Hough. The reason most induced quakes tend to be relatively small, she added, is that most earthquakes, in general, tend to be small. “One school of thought argues that the size distribution is the same for induced and natural earthquakes,” she said. But whether there is a maximum size limit for induced earthquakes, seismologists still do not know, she added.

    An important aspect of the new work, Foulger said, is that Hough presents a model that other scientists can test, which is a first for a large induced event like the Kern County earthquake. For Seth Stein, a geophysicist at Northwestern University in Evanston, Ill., who also had no part in the study, “the take-home is that for one of the largest earthquakes that we know of in the last hundred years, a reasonable case can be made that it was induced.”

    See the full article here .

    Please help promote STEM in your local schools.

    STEM Icon

    Stem Education Coalition

    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.

     
  • richardmitnick 11:32 am on October 20, 2017 Permalink | Reply
    Tags: , Converting the jiggles of perturbed optical fiber strands into information about the direction and magnitude of seismic events, , Fiber optic seismic observatory, Geology, , Seismometers,   

    From Stanford: “Stanford researchers build a ‘billion sensors’ earthquake observatory with optical fibers” 

    Stanford University Name
    Stanford University

    October 19, 2017
    Ker Than

    1
    Map shows location of a 3-mile, figure-8 loop of optical fibers installed beneath the Stanford campus as part of the fiber optic seismic observatory. (Image credit: Stamen Design and the Victoria and Albert Museum)

    Thousands of miles of buried optical fibers crisscross California’s San Francisco Bay Area delivering high-speed internet and HD video to homes and businesses.

    Biondo Biondi, a professor of geophysics at Stanford’s School of Earth, Energy & Environmental Sciences, dreams of turning that dense network into an inexpensive “billion sensors” observatory for continuously monitoring and studying earthquakes.

    Over the past year, Biondi’s group has shown that it’s possible to convert the jiggles of perturbed optical fiber strands into information about the direction and magnitude of seismic events.

    The researchers have been recording those seismic jiggles in a 3-mile loop of optical fiber installed beneath the Stanford University campus with instruments called laser interrogators provided by the company OptaSense, which is a co-author on publications about the research.

    “We can continuously listen to – and hear well – the Earth using preexisting optical fibers that have been deployed for telecom purposes,” Biondi said.

    Currently researchers monitor earthquakes with seismometers, which are more sensitive than the proposed telecom array, but their coverage is sparse and they can be challenging and expensive to install and maintain, especially in urban areas.

    By contrast, a seismic observatory like the one Biondi proposes would be relatively inexpensive to operate. “Every meter of optical fiber in our network acts like a sensor and costs less than a dollar to install,” Biondi said. “You will never be able to create a network using conventional seismometers with that kind of coverage, density and price.”

    Such a network would allow scientists to study earthquakes, especially smaller ones, in greater detail and pinpoint their sources more quickly than is currently possible. Greater sensor coverage would also enable higher resolution measurements of ground responses to shaking.

    “Civil engineers could take what they learn about how buildings and bridges respond to small earthquakes from the billion-sensors array and use that information to design buildings that can withstand greater shaking,” said Eileen Martin, a graduate student in Biondi’s lab.

    From backscatter to signal

    Optical fibers are thin strands of pure glass about the thickness of a human hair. They are typically bundled together to create cables that transmit data signals over long distances by converting electronic signals into light.

    2
    The fiber optic seismic observatory successfully detected the 8.2 magnitude earthquake that struck central Mexico on Sept. 8, 2017. (Image credit: Siyuan Yuan)

    Biondi is not the first to envision using optical fibers to monitor the environment. A technology known as distributed acoustic sensing (DAS) already monitors the health of pipelines and wells in the oil and gas industry.

    “How DAS works is that as the light travels along the fiber, it encounters various impurities in the glass and bounces back,” Martin said. “If the fiber were totally stationary, that ‘backscatter’ signal would always look the same. But if the fiber starts to stretch in some areas — due to vibrations or strain — the signal changes.”

    Previous implementation of this kind of acoustic sensing, however, required optical fibers to be expensively affixed to a surface or encased in cement to maximize contact with the ground and ensure the highest data quality. In contrast, Biondi’s project under Stanford — dubbed the fiber optic seismic observatory — employs the same optical fibers as telecom companies, which lie unsecured and free-floating inside hollow plastic piping.

    “People didn’t believe this would work,” Martin said. “They always assumed that an uncoupled optical fiber would generate too much signal noise to be useful.”

    But since the fiber optic seismic observatory at Stanford began operation in September 2016, it has recorded and cataloged more than 800 events, ranging from manmade events and small, barely felt local temblors to powerful, deadly catastrophes like the recent earthquakes that struck more than 2,000 miles away in Mexico. In one particularly revealing experiment, the underground array picked up signals from two small local earthquakes with magnitudes of 1.6 and 1.8.

    “As expected, both earthquakes had the same waveform, or pattern, because they originated from the same place, but the amplitude of the bigger quake was larger,” Biondi said. “This demonstrates that fiber optic seismic observatory can correctly distinguish between different magnitude quakes.”

    Crucially, the array also detected and distinguished between two different types of waves that travel through the Earth, called P and S waves. “One of our goals is to contribute to an early earthquake warning system. That will require the ability to detect P waves, which are generally less damaging that S waves but arrive much earlier,” Martin said.

    The fiber optic seismic observatory at Stanford is just the first step toward developing a Bay Area-wide seismic network, Biondi said, and there are still many hurdles to overcome, such as demonstrating that the array can operate on a city-wide scale.
    Media Contacts

    Biondo Biondi, School of Earth, Energy & Environmental Sciences: (650) 723-1319, biondo@stanford.edu

    Eileen Martin, School of Earth, Energy & Environmental Sciences: ermartin@stanford.edu

    Ker Than, School of Earth, Energy & Environmental Sciences: (650) 723-9820

    QCN bloc

    Quake-Catcher Network

    6.11.16

    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).

    BOINCLarge

    BOINC WallPaper

    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.

    BOINC is a leader in the field(s) of Distributed Computing, Grid Computing and Citizen Cyberscience.BOINC is more properly the Berkeley Open Infrastructure for Network Computing, developed at UC Berkeley.

    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
    QCN Quake Catcher Network map -39655″ />

    Please help promote STEM in your local schools.

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    See the full article here .

    Please help promote STEM in your local schools.
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    Stem Education Coalition

    Leland and Jane Stanford founded the University to “promote the public welfare by exercising an influence on behalf of humanity and civilization.” Stanford opened its doors in 1891, and more than a century later, it remains dedicated to finding solutions to the great challenges of the day and to preparing our students for leadership in today’s complex world. Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto. Since 1952, more than 54 Stanford faculty, staff, and alumni have won the Nobel Prize, including 19 current faculty members

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  • richardmitnick 8:26 am on October 17, 2017 Permalink | Reply
    Tags: , , Geology, Rosalba Bonnacorsi, , Underground Laboratories for Dark Matter Research,   

    From SETI Institute: Women in STEM -“Catch Up with SETI Institute Scientist Rosalba Bonnacorsi on her NASA Spaceward Bound Expedition to the Center of the Earth (Almost!) 

    SETI Logo new
    SETI Institute

    1
    SETI Institute Astrobiology Scientist Rosalba Bonnacorsi

    October 16, 2017

    For two weeks in October, from the 8th-20th, SETI Institute scientist Rosalba Bonnacorsi will be part of the expedition team when NASA Spaceward Bound and the U.K. Centre for Astrobiology conduct a planetary analog expedition in the Boulby Mine. Boulby is the site of the astrobiology analog research with the Mine Analog Research Program (MINAR)

    2
    The Boulby International Subsurface Astrobiology Laboratory (BISAL) is hosted by the Boulby Mine complex north of Whitby, Yorkshire, on the North East coast of England (UK).

    The Boulby Mine is a 1.1 km-deep active potash mine at the core of a 250-million-year-old, massive sequence of NaCl, KCl, and sulfates salts. The salts were formed by the evaporation of an ancient ocean – the Zechstein Sea — which covered most of present day Western Europe during the Permian geologic period. The facility comprises over 1,000 km of underground roadways through the salt deposits. BISAL is a fully air conditioned, internet connected to the surface (100 Mbps) laboratory, with an outside ‘Mars yard’ for testing rover and instrument technology. The facility is also used for studies of astrophysics – the Underground Laboratories for Dark Matter Research, and low-background radiation and other deep underground science.

    The expedition is made up of an international team of scientists, teachers, engineers, biologists, geologists and astronauts. Scientists and educators from NASA and the SETI Institute will work on a variety of science and technology projects which will address some specific scientific questions and test a variety of potential technologies and planetary exploration protocols in the mine:

    Scientific Questions:

    Does ancient salt preserve viable organisms?
    What biosignatures of life are preserved in deep salts?
    What types of organisms inhabit deep brines?
    What are the environmental conditions that support life in salt?
    What is the composition and structure of evaporite deposits?
    Where does the deep subsurface gas comes from? Is this from biology or from geology?
    How we can apply what we learn in MINAR5 to the search for past and present life on other planets?

    Testing:

    Life detection technology
    Clean sampling technologies
    Autonomous drones and rover technology for deep subsurface exploration and mapping on the Moon and Mars
    Gas detection technology
    Communication protocols with the surface to simulate cave and lava tube exploration on the Moon and Mars

    3
    Entrance to the mine (Image Credit: Boulby Mine)

    The team will explore and study a variety of ancient salt structures and briny environments. The primary objectives are to detect evidence of ancient and modern life inside the salt and to monitor the associated underground microclimate (temperature and rH). They will scout Boulby’s underworld, and test for the most efficient protocols for accepting sampling as well as in situ and laboratory analysis of collected samples. Furthermore, they will conduct technology/robotic experiments to simulate drilling missions in space conducted by Astronauts.

    Spaceward Bound is an educational program and will use the lab and mine environment to carry out science and technology in support of the subsurface exploration of the Moon and Mars, and Ocean Worlds. Exploration, hand-on activities and classroom work will be conducted during the day. A typical day will involve a 101 introductory lectures-lab, safety training sessions, and morning/evening group meetings to plan together the next day science objectives and tasks, as well as discuss on what we have learned during the day.

    Rosalba has worked as an Astrobiologist at the Carl Sagan Center of the SETI Institute since 2008 and with scientists at NASA Ames Research Center since 2005. She enjoys doing science to advance our understanding of the universe and spends much of her spare time raising public awareness about planetary analog research taking place on Earth, including associated space missions to the Solar System (such as the Mars Science Lab 2020) and those planned to reach potential life in ocean worlds (e.g., Saturn’s icy moon Enceladus). Rosalba’s goal is to gain a broad picture of where life and its signatures are most successfully distributed, concentrated, preserved, and detected. This knowledge helps us to understand how to search for life beyond Earth.

    The SETI Institute is proud to collaborate and support the NASA Spaceward Bound Expedition to Boulby Mine, this October.

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  • richardmitnick 1:06 pm on October 16, 2017 Permalink | Reply
    Tags: , , , Geology, Volcanic Unrest at Mauna Loa Earth’s Largest Active Volcano,   

    From Eos: “Volcanic Unrest at Mauna Loa, Earth’s Largest Active Volcano” 

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    Weston Albert Thelen
    Asta Miklius
    Christina Neal

    Mauna Loa is stirring—is a major eruption imminent? Comparisons with previous eruptions paint a complicated picture.

    1
    Oblique aerial view looking north-northeast toward the summit area of Mauna Loa volcano (elevation 4,169 meters) on 15 January 1976. The summit caldera (Moku’āweoweo) is 6 kilometers long and 2.5 kilometers wide. The pit crater in the foreground marks the start of the Southwest Rift Zone. Mauna Kea volcano is on the skyline in the distance. Credit: D. Peterson, USGS

    Mauna Loa is showing persistent signs of volcanic unrest. Since 2014, increased seismicity and deformation indicate that Mauna Loa, the volcano that dominates more than half of the island of Hawaiʻi, may be building toward its first eruption since 1984.

    Thousands of residents and key infrastructure are potentially at risk from lava flows, so a critical question is whether the volcano will follow patterns of previous eruptions or return to its now historically unprecedented 33-year slumber.

    Mauna Loa has erupted 33 times since 1843, an average of one eruption every 5 years [Trusdell, 2012]. Typical of shield-building Hawaiian volcanoes, Mauna Loa hosts a summit caldera and two rift zones, the Northeast Rift Zone (NERZ) and the Southwest Rift Zone (SWRZ; Figure 1, inset).

    Since the two most recent eruptions, in 1975 and 1984, monitoring by the U.S. Geological Survey’s Hawaiian Volcano Observatory has changed dramatically. Ground-based instruments continuously record signals from global navigation satellite systems (GNSS, of which GPS is one example), measuring the changing shape of the ground surface in near-real time, and interferometric synthetic aperture radar (InSAR) provides extensive spatial coverage of deformation. Seismic monitoring has also improved with the addition of more stations, increased data fidelity, and improved data analysis.

    More people live on the slopes of Mauna Loa now than in the 1970s and 1980s, so improvements in monitoring technology are of more than just academic interest.

    How does this recent period of unrest compare with the periods just before previous eruptions? How reliable are these comparisons in predicting the next eruption?

    2
    Fig. 1. The Italian satellite system COSMO-SkyMed acquired radar images of Mauna Loa on 1 January 2013 and 30 April 2017 to produce this ascending mode interferogram. Each fringe represents 1.5 centimeters of motion in the line-of-sight direction to the satellite. The butterfly pattern of fringes suggests an inflating tabular body beneath the caldera and uppermost Southwest Rift Zone (see inset map). The sizes of the white dots represent the magnitudes of earthquakes that occurred during this period. The arrow at the bottom left shows the direction of the satellite’s motion. The satellite’s interferometric synthetic aperture radar (InSAR) antenna looks to the right of the satellite track, and the radar contacts the land surface at about 35° off vertical. The inset is a digital elevation map of Mauna Loa showing lava flows since 1843 in red. The box shows the approximate extent of the interferogram image. COSMO-SkyMed data were provided by the Agenzia Spaziale Italiana via the Hawaiʻi Supersite.

    The Current Unrest

    Several periods of unrest have occurred at Mauna Loa since the 1984 eruption. The shallow magma storage complex started refilling (inflating) immediately following the eruption, but inflation soon slowed, and stopped altogether in the mid-1990s (Figure 2). A short-lived inflation episode began in 2002 [Miklius and Cervelli, 2003], and another began in 2004. By 2009, inflation had largely ceased. Unlike the current unrest, these previous two inflation episodes were not associated with significant numbers of shallow earthquakes; rather, they started with brief periods of deep seismicity approximately 45 kilometers beneath the surface [Okubo and Wolfe, 2008].

    3
    Fig. 2. Changes in distance across Moku’āweoweo, Mauna Loa’s summit caldera, and earthquakes shallower than 15 kilometers from 1973 through April 2017 in the same area as Figure 1. Because today’s sensitive instruments can detect earthquakes that previous instruments would have missed, only earthquakes greater than M1.7 are plotted. Large, abrupt extensions are associated with the formation of volcanic dikes during the 1975 and 1984 eruptions; other extensions are mostly due to accumulation of magma in shallow reservoirs. Note that this distance change is not sensitive to extension across the upper SWRZ, where most of the magma accumulation occurred between October 2015 and mid-2016. (EDM is electronic distance measuring, and MOKP and MLSP are GPS instrument sites.)

    The current unrest started in earnest in 2014 (Figure 2). Seismicity rates began to rise above background levels as early as March 2013, and by summer 2014, both seismicity and deformation rates had increased significantly. The pattern of ground deformation indicated inflation of a magma storage complex beneath the caldera and uppermost SWRZ, areas that were also the most seismically active (Figure 3).

    Beneath the caldera, seismicity consists of mostly small earthquakes (magnitude M of less than 2.5) at depths of 2–3 kilometers. These earthquakes occur in swarms lasting days to weeks, separated by months of minor activity. Event rates have been as high as 15 earthquakes per hour, with most earthquakes too small to be formally located.

    4
    Fig. 3. Blue arrows (with gray 95% confidence error ellipses) show the average horizontal velocities of GNSS stations on Mauna Loa from mid-2014 through 2016. Red arrows represent velocities predicted by a model of a horizontally opening tabular body extending from about 3 to 6 kilometers beneath the summit and upper Southwest Rift Zone and a radially expanding body at about 3 kilometers beneath the southeastern wall of the caldera. The surface projections of these magma reservoirs are indicated by the black line and black circle. The average rate of magma accumulation in these shallow reservoirs is on the order of 13 million cubic meters per year.

    The uppermost SWRZ has been the most seismically active region during the current unrest, in terms of overall energy release and number of earthquakes. These earthquakes are typically 3–4 kilometers below the surface. Another area of seismicity has been high on the west flank of the volcano, where swarms of small earthquakes (mostly less than M2.5) at an average depth of about 7 kilometers typically last days to a week.

    In addition to shallow seismicity, there have been several deep (greater than 20 kilometers), long-period earthquakes loosely scattered beneath the summit area. During previous periods of inflation, earthquakes with similar characteristics have been associated with magma ascent [Okubo and Wolfe, 2008].

    Short-term rates of seismicity and deformation have varied in magnitude, with weeklong to monthlong periods of relative quiescence interspersed within longer-term trends of heightened activity. Although there is general long-term correlation between deformation and seismicity rates, there is no obvious relationship between them in the short term.

    The spatial pattern of deformation and seismicity has also varied. In fall 2015, after several months of decreased inflation at the summit, seismicity beneath the caldera largely ceased, and inflation in the upper SWRZ increased (Figure 4). In May 2016, inflation and seismicity beneath the caldera slowly resumed, but as of mid-2017, rates are low compared with those seen prior to fall 2015.

    5
    Fig. 4. COSMO-SkyMed ascending mode interferograms show the shift in locus of inflation toward the upper Southwest Rift Zone in October 2015. Each image covers about the same length of time: (left) 18 March 2015 to 9 August 2015 and (right) 24 July 2015 to 31 December 2015. Each full-color cycle represents 1.5 centimeters of motion in the line-of-sight direction toward the satellite. Arrow shows direction of motion of the satellite. The SAR antenna looks to the right of the satellite track; the incidence angle is about 35° off vertical. COSMO-SkyMed data were provided by the Agenzia Spaziale Italiana via the Hawaii Supersite.

    Comparison with Past Eruptions

    Deformation monitoring networks in place before the 1975 and 1984 eruptions were sufficient to provide long-term indications of inflation that along with increased seismicity, led to a general forecast for the 1984 eruption [Decker et al., 1983]. However, measurements were not frequent enough to evaluate whether there were precursory changes in extension or uplift in the summit area just prior to eruption.

    Direct comparison of magma storage geometries and volumes derived from deformation patterns is also not possible because of the limited spatial and temporal extent of the early geodetic monitoring networks. Pre-1984 measurements are consistent with, but cannot confirm, the existence of a large-volume tabular storage complex (a vertical, dikelike body) beneath the summit and upper SWRZ, similar to what we currently model from GNSS and InSAR data.

    Similarly, differences in seismic network sensitivity and data processing preclude direct comparison of current seismicity rates with pre-1975 and pre-1984 rates. Patterns in the locations of earthquakes stronger than about M1.7, however, are comparable, and these patterns show a clear coincidence between the locations of seismicity during the current unrest and previous preeruption patterns (Figure 5).

    Another approach to comparing precursory seismicity is to evaluate cumulative seismic energy release, which mainly reflects energy released by larger-magnitude earthquakes (energy release increases logarithmically with respect to earthquake magnitude). Between 1 May 2013 and 30 April 2017, energy release on the west flank was equivalent to an M4.1 earthquake. For the same region, energy releases during the 4 years prior to the 1975 and 1984 eruptions were M4.2 and M4.5, respectively. In the caldera and uppermost SWRZ, the current energy release sums to M4.4, compared with M4.9 and M4.4 for the 1975 and 1984 precursory periods.

    Thus, the energy released during the current 4 or so years of unrest is approaching that released during the 4 years prior to the 1975 and 1984 eruptions. In some volcanic systems, the amount of energy release compared with previous eruptions may be an indicator of whether a period of unrest results in an eruption [Thelen et al., 2010], but this relationship has not been established on shield volcanoes such as Mauna Loa.

    One to 2 years prior to the 1975 and 1984 eruptions, swarms of small earthquakes increased in intensity. The strongest swarms included hundreds of small earthquakes per day for weeks. Bursts, as they were called, were separated by 3–6 months of relative quiet [Koyanagi et al., 1975]. Recently, swarms on the west flank have increased in number and size, but the durations of the swarms are less than pre-1975 and 1984 levels. Similarly, swarms of tiny earthquakes beneath the caldera have not occurred at rates seen in the months prior to the 1975 and 1984 eruptions.

    Interestingly, during the days to weeks prior to the past two eruptions, the number of small earthquakes fluctuated instead of building up steadily, even reaching relatively low rates for short periods prior to eruption [Koyanagi, 1987; Lockwood et al., 1987]. However, both eruptions had distinct short-term seismic precursors. The 1975 eruption was preceded by less than an hour of strong tremor in the summit caldera area [Lockwood et al., 1987]. In 1984, small (less than M0.1) earthquakes increased in frequency, shaking the ground two or three times per minute about 2.5 hours before the eruption [Koyanagi, 1987]. Harmonic tremor began about 2 hours prior to eruption, with a large increase in tremor amplitude and a swarm of earthquakes 30 minutes prior to eruption. Seven earthquakes larger than M3 occurred during a period from 30 minutes before the 1984 eruption until just over 1 hour after the onset of the eruption.

    5
    Fig. 5. Earthquake epicenters for (a) the 4 years prior to the 1975 eruption, (b) the 4 years prior to the 1984 eruption, and (c) the latest 4 years of unrest (1 May 2013 to 30 April 2017). Earthquake symbol size is based on magnitude, and color is based on depth. Only earthquakes above M1.7 are included, in an attempt to compensate for differences in network sensitivity since 1975. All earthquakes are analyst reviewed. Because the analysis of earthquakes above M1.7 is only partially complete for the current episode of unrest, event rates since 2013 may actually be slightly higher than shown here.

    Is an Eruption in Our Near Future?

    Mauna Loa’s long history of observed activity aids in forecasting another eruption, but at present, any forecast still contains a high degree of uncertainty. Some aspects of the current unrest are similar to unrest prior to eruptions in 1975 and 1984. Earthquake locations, temporal behavior, and energy release suggest that the volcano may be following a similar pattern. Other aspects, however, differ from the periods prior to the 1975 and 1984 eruptions.

    During the current unrest period, we have not observed the kind of moderate to large flank earthquakes that preceded many historical eruptions [Walter and Amelung, 2006], including the 1975 and 1984 eruptions. Also, as of fall 2017, we have not seen the high rates of small earthquakes observed about 7–14 months prior to the 1975 and 1984 eruptions, even though our ability to detect them has improved. Thus, if current unrest follows previous patterns of seismicity, we may expect that the volcano is still many months from eruption.

    We must also consider that current unrest might not follow previous patterns, and an eruption could occur without months of elevated microseismicity. It is possible that after years of intermittent inflation, shallow magma storage is exerting pressures already near the breaking point of the overlying rock.

    We can’t say for certain whether there will be a precursory months-long increase in microseismicity before the next Mauna Loa eruption. However, an eruption will likely be immediately preceded by an hours-long, dramatic increase in small earthquakes (at least one earthquake per minute), strong tremor, and the occurrence of several M3 or stronger earthquakes, similar to the lead-up to the 1975 and 1984 eruptions. Real-time deformation data from tiltmeters and GNSS stations will show large anomalies as magma moves from storage reservoirs toward the surface to the eventual eruption site in the summit area and/or along one of the rift zones or (less likely) from radial vents on the west flank.

    It is also possible that current elevated rates of seismicity and deformation may not culminate in eruption anytime soon; rather, this could be yet another episode of unrest that gradually diminishes. During the 25-year repose between the 1950 and 1975 eruptions, seismic unrest in 1962, 1967, and 1970 did not lead to eruption, although in hindsight, each is considered a long-term precursor to the 1975 eruption [Koyanagi et al., 1975].

    The high rate of volcanic activity at neighboring Kīlauea volcano complicates assessing the likelihood of a Mauna Loa eruption in the coming months or years. Klein [1982] noted that longer repose intervals at Mauna Loa were statistically correlated with eruptive activity at Kīlauea. Indeed, the current long repose time at Mauna Loa is occurring at the same time as the long-lived Puʻu ʻŌʻō eruption at Kīlauea, which began in 1983 and continues today. Even so, the most recent eruption of Mauna Loa in 1984 occurred during this eruption at Kīlauea, so the impact of nearby volcanic activity on Mauna Loa’s behavior over short timescales is unknown.

    We can make one forecast with relative certainty: On the basis of nearly 200 years of documented activity, it is highly likely that the next eruption will begin in the summit region and then, within days to years, migrate into one of the two primary rift zones [Lockwood et al., 1987].

    It is important to note that seismicity and inflation beneath the uppermost SWRZ do not imply an increased likelihood of eruption along the SWRZ. Similar patterns of seismicity prior to the 1975 and 1984 eruptions did not result in sustained activity in the SWRZ. In 1984, the eruption began at the summit and migrated to the upper SWRZ before activity focused along the NERZ, suggesting that a magma body extending into the uppermost SWRZ—similar to that inferred from current data—was also active prior to that eruption.

    Communicating the Hazards

    In response to more than a year of persistently elevated rates of seismicity and deformation, the Hawaiian Volcano Observatory (HVO) elevated the Volcano Alert Level and Aviation Color Code for Mauna Loa to advisory/yellow on 17 September 2015, indicating that the volcano was restless and that monitoring parameters were above the long-term background levels.

    Since then, HVO has continued public education efforts and engaged agency partners, including Hawaiʻi County Civil Defense and the National Park Service, to discuss preparedness and response planning. In 2016, HVO installed new web cameras and upgraded real-time gas and temperature sensors in the summit caldera. Alarms have been set to alert scientists to significant changes in several data streams, including real-time seismic amplitude (a measure of seismic energy release), ground tilt, and satellite- and ground-based thermal imagery. Revised maps showing potential inundation zones and likely lava flow paths based on topography derived from digital elevation maps have been prepared.

    As with any precursory volcanic eruption sequence, it will be challenging to choose the correct time to alert authorities and elevate public concern about a possible eruption. Once an eruption has commenced, pinpointing the exact location of the outbreak—especially at night or in cloudy conditions—may not be straightforward and may require the use of new tools such as infrasound. Vent location determines which downslope areas are at greatest risk, so addressing this capability gap is a high priority.

    As of this writing, elevated rates of seismicity and deformation continue. Improvements in monitoring networks and alarming systems since 1984 put HVO in a better position to provide early warning and, once an eruption has commenced, help guide emergency response. Additional efforts to inform and prepare the public for the eventual eruption are an important step in minimizing impacts to life and property.

    References

    Decker, R. W., et al. (1983), Seismicity and surface deformation of Mauna Loa volcano, Hawaii, Eos Trans. AGU, 64(37), 545–547, https://doi.org/10.1029/EO064i037p00545-01.

    Klein, F. W. (1982), Patterns of historical eruptions at Hawaiian volcanoes, J. Volcanol. Geotherm. Res., 12, 1–35, https://doi.org/10.1016/0377-0273(82)90002-6.

    Koyanagi, R. Y. (1987), Seismicity associated with volcanism in Hawaii: Application to the 1984 eruption of Mauna Loa volcano, U.S. Geol. Surv. Open File Rep., 87-277, 76 pp.

    Koyanagi, R. Y., E. T. Endo, and J. S. Ebisu (1975), Reawakening of Mauna Loa volcano, Hawaii: A preliminary evaluation of seismic evidence, Geophys. Res. Lett., 2(9), 405–408, https://doi.org/10.1029/GL002i009p00405.

    Lockwood, J. P., et al. (1987), Mauna Loa 1974–1984: A decade of intrusive and extrusive activity, in Volcanism in Hawaii, chap. 19, U.S. Geol. Surv. Prof. Pap., 1350, 537–570.

    Miklius, A., and P. Cervelli (2003), Interaction between Kīlauea and Mauna Loa, Nature, 421, 229, https://doi.org/10.1038/421229a.

    Okubo, P. G., and C. J. Wolfe (2008), Swarms of similar long-period earthquakes in the mantle beneath Mauna Loa volcano, J. Volcanol. Geotherm. Res., 178, 787–794, https://doi.org/10.1016/j.jvolgeores.2008.09.007.

    Thelen, W. A., S. D. Malone, and M. E. West (2010), Repose time and cumulative moment magnitude: A new tool for forecasting eruptions?, Geophys. Res. Lett., 37, L18301, https://doi.org/10.1029/2010GL044194.

    Trusdell, F. A. (2012), Mauna Loa—History, hazards, and risk of living with the world’s largest volcano, U.S. Geol. Surv. Fact Sheet, 2012-3104, 4 pp., https://pubs.usgs.gov/fs/2012/3104/.

    Walter, T. R., and F. Amelung (2006), Volcano-earthquake interaction at Mauna Loa volcano, Hawaii, J. Geophys. Res., 111, B05204, https://doi.org/10.1029/2005JB003861.

    Author Information

    Weston Albert Thelen (email: wthelen@usgs.gov), Cascade Volcano Observatory, U.S Geological Survey, Vancouver, Wash.; and Asta Miklius and Christina Neal, Hawaiian Volcano Observatory, U.S. Geological Survey, Hawaiʻi National Park, Hawaii

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  • richardmitnick 2:33 pm on October 10, 2017 Permalink | Reply
    Tags: , , Geology, Ritter Island, , Volcanic islands are the source of some of the world’s largest landslides,   

    From Eos: “An 1888 Volcanic Collapse Becomes a Benchmark for Tsunami Models” 

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    10.10.17
    Aaron Micallef
    Sebastian F. L. Watt
    Christian Berndt
    Morelia Urlaub
    Sascha Brune
    Ingo Klaucke
    Christoph Böttner
    Jens Karstens
    Judith Elger

    1
    Scientists aboard the R/V Sonne (shown here) profiled the seafloor and subsurface structures near Ritter Island, north of New Guinea, in 2016. A large portion of this volcanic island collapsed and slid into the sea in 1888, making it an ideal case study for modeling volcanic collapse landslides and the tsunamis they generate. Credit: Christian Berndt

    Early one March morning in 1888, a 4-cubic-kilometer chunk of the Ritter Island volcano collapsed into the Bismarck Sea northeast of New Guinea. This volume of land was about twice that of the Mount St. Helens landslide in 1980, and it is the largest historically recorded tsunami-causing volcanic sector collapse.

    The ensuing landslide triggered a tsunami tens of meters high. The waves were still 8 meters high when they reached parts of the island of New Guinea that are several hundreds of kilometers away, according to observers who witnessed the event [Ward and Day, 2003].

    Volcanic islands are the source of some of the world’s largest landslides. These landslides have the potential to generate large tsunamis. Scientists have debated the magnitude of these tsunamis, but much uncertainty remains over landslide dynamics and how far a tsunami can travel across an ocean basin while remaining large enough to cause damage.

    Studies of Ritter Island’s landslide and ensuing tsunami could significantly reduce that uncertainty. During a 6-week-long expedition in November and December 2016 aboard the German R/V Sonne, we mapped the Ritter Island collapse scar and deposit using hull-mounted multibeam sonar systems, which produced high-resolution bathymetry (Figure 1) and acoustic backscatter data.

    We are using data from this expedition, alongside a range of direct observations and samples, to generate a detailed interpretation of the Ritter Island landslide. With these robust field data, we set the stage for testing coupled landslide-tsunami models.

    An Ideal Study Site

    Ritter Island’s historic landslide, along with a heightened awareness of tsunami hazards following several recent devastating events, has caused some to wonder if other volcanic islands could experience flank or total collapse and, if so, how far tsunamis could reach. One hypothetical scenario that captured the attention of the popular media in 2004 involves a potential collapse of the Cumbre Vieja volcano on the southern half of the island of La Palma, one of the Canary Islands off the northwest coast of Africa.

    Such a collapse could trigger a tsunami that races across the Atlantic Ocean. However, recent tsunami models span an order of magnitude in their predictions of far-field wave heights for the La Palma collapse scenario.

    Resolving such discrepancies in our understanding of landslide and tsunami processes requires a field data set in which both phenomena can be observed to test current models. The sector collapse of Ritter Island, Papua New Guinea, in 1888 meets both these criteria.

    The landslide generated a tsunami that devastated shorelines as far as 600 kilometers away [Day et al., 2015]. An important factor is that there are eyewitness observations of the tsunami height, arrival time, and frequency at a range of locations around the Bismarck Sea [Day et al., 2015]. The event can thus be used as a benchmark for testing models of landslide-generated tsunamis if the volume, distribution, and dynamics of the landslide mass can be reconstructed.

    2
    Fig. 1. (a) Three-dimensional view of the Bismarck Sea between Umboi and Sakar islands compiled using data from the SO-252 multibeam echo sounder, bathymetric data from the General Bathymetric Chart of the Oceans (GEBCO), and altimetry from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite. The gray transparent cone represents Ritter Island before the 1888 event. Black lines and the red box indicate 2-D and 3-D seismic reflection data, respectively, acquired during the SO-252 expedition. The white arrows here and below indicate the direction of material mobilization during the 1888 event. (b) Three-dimensional reflection seismic data (from the area in the red box above) showing the Ritter Island deposit, remnant block, and parasitic volcanic cone. No image credit.

    Geological Setting

    Ritter Island is located north of Australia in the Bismarck Sea about 80 kilometers north of New Guinea and some 20 kilometers off the western end of New Britain. Situated between the islands of Umboi and Sakar (Figure 1), it forms part of the Bismarck Volcanic Arc, which results from the northward subduction of the Solomon Plate underneath the Bismarck Plate [Baldwin et al., 2012]. Today, Ritter Island is a narrow, crescent-shaped island, around 1.2 kilometers long and 200 meters wide, reaching an elevation of approximately 140 meters above sea level.

    This island is all that remains of a larger, steep-sided conical island that was around 750 meters high before it collapsed in 1888 [Day et al., 2015]. During the 19th century, Ritter Island was known among navigators in the region as a highly active volcano, characterized by frequent Strombolian activity [Johnson, 2013].

    There is evidence for several submarine eruptions since 1888 that have constructed a cone with a current summit around 200 m beneath sea level. The remnant of the island above the waterline is dominated by interbedded sequences of basaltic scoria and thin lava flows that are consistent with low-level Strombolian activity.

    3
    This arc is all that remains of the Ritter Island volcanic cone. Underwater deposits show clear evidence of the landslide triggered by the collapsing cone, and eyewitness accounts described the resulting tsunami. Credit: Christian Berndt.

    Contemporary observations of the tsunami triggered by the 1888 event suggest a single wave train, which is consistent with one main phase of landslide movement and tsunami generation [Day et al., 2015]. The landslide deposit is young enough to be preserved at the seafloor without significant overlying sedimentary cover, so it can be examined today to understand the emplacement dynamics of a large volcanic island landslide.

    Volcanic island landslides with volumes of 1 to 10 cubic kilometers, such as the Ritter Island landslide, have a global recurrence interval of 100 to 200 years [Day et al., 2015]. Therefore, a similar event is likely to occur in the next 100 years, in contrast to the extremely large ocean island collapses (e.g., Canary Islands and Lesser Antilles) that have recurrence intervals of tens of thousands of years or more.

    Collecting the Field Data Set

    During our 2016 expedition, we used a Parasound subbottom profiler with 10-centimeter resolution, as well as 2-D multichannel seismic data and P-Cable 3-D reflection seismic data acquisition systems to image the collapse deposit with 5-m vertical and horizontal resolution (Figure 1). Additional observations and samples collected across the deposit and island flanks, using towed video cameras and sediment samplers, provide ground truthing of the geophysical data and allow us to construct a detailed interpretation of landslide emplacement processes.

    The acquired data show the three-dimensional structure of the Ritter Island landslide deposit and enabled us to reconstruct the kinematics of the emplacement process. The new data set will be used to do the following:

    quantify the overall volume of the material that has been mobilized
    decipher the nature and extent of landslide disintegration
    determine the location, distribution, and size of transported blocks
    identify the nature and origin of different regions of the landslide deposit
    understand the relationship between landslides and the eruption history of Ritter Island and surrounding volcanoes

    These are key parameters for determining the landslide failure and emplacement process and the dynamics of the 1888 tsunami. An initial assessment of the data indicates that the flanks of Ritter Island below sea level expose clastic sequences similar to those in the scar above the water, with an increase in more massive lava units in the lowermost part of the edifice. The landslide cuts deeply into the island structure, and the scar exposures suggest an edifice that is dominated by loosely compacted layers of volcanic rock fragments.

    The landslide mass split and flowed around a remnant block of the island and dispersed within the channel between Umboi and Sakar (Figure 1), where it formed a deposit that is relatively flat at the margins and has irregular channelization in the central part. Parts of the landslide deposit traveled through a constriction between Umboi and Sakar and incorporated underlying seafloor sediment.

    A Framework for Future Models

    Our observations indicate that minor changes in slope gradient can strongly affect landslide dynamics. The deposition of the Ritter landslide entailed a progressive, multiphase, brittle to plastic failure that mobilized material over a considerable distance. The distal deposit, near the leading edge of the landslide, incorporates a major proportion of underlying seafloor sediment.

    Seismic profiles through the distal deposit indicate that the 1888 landslide was only the latest of a series of large-volume volcanic landslides from the surrounding islands. Some blocks piercing the seafloor are, in fact, rooted within older and much larger landslide deposits.

    How large a tsunami a volcanic collapse landslide of a given size will generate and how far the tsunami will travel before it dissipates remain open questions. The information we gathered on this expedition will provide the framework for coupled landslide-tsunami models, which are required to assess the destructive potential of sector collapse–related tsunamis.
    Acknowledgments

    This work reflects the joint effort of the SO252 expedition’s shipboard scientific party. We thank Simon Day, Eli Silver, and Russell Perembo for sharing data and helping with the survey planning. We thank the master and crew of R/V Sonne and our technicians for support during the cruise. Data collection was funded through the BMBF project Ritter Island 03G0252A. A.M. acknowledges funding from the European Research Council under the European Union’s Horizon 2020 Programme (MARCAN, grant agreement 677898).

    References

    Baldwin, S. L., P. G. Fitzgerald, and L. E. Webb (2012), Tectonics of the New Guinea region, Annu. Rev. Earth Planet. Sci., 40, 495–520, https://doi.org/10.1146/annurev-earth-040809-152540.

    Day, S., et al. (2015), Submarine landslide deposits of the historical lateral collapse of Ritter Island, Papua New Guinea, Mar. Pet. Geol., 67, 419–438, https://doi.org/10.1016/j.marpetgeo.2015.05.017.

    Johnson, R. (2013), Fire Mountains of the Islands: A History of Volcanic Eruptions and Disaster Management in Papua New Guinea and the Solomon Islands, ANU Press, Acton, Australia, https://doi.org/10.26530/OAPEN_462202.

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    Author Information

    Aaron Micallef, Marine Geology and Seafloor Surveying group, University of Malta, Msida; Sebastian F. L. Watt, School of Geography, Earth and Environmental Sciences, University of Birmingham, U.K.; Christian Berndt (email: cberndt@geomar.de) and Morelia Urlaub, GEOMAR Helmholtz Centre for Ocean Research Kiel, Germany; Sascha Brune, GFZ German Research Centre for Geosciences, Potsdam; and Ingo Klaucke, Christoph Böttner, Jens Karstens, and Judith Elger, GEOMAR Helmholtz Centre for Ocean Research Kiel, Germany
    Citation: Micallef, A., S. F. L. Watt, C. Berndt, M. Urlaub, S.Brune, I. Klaucke, C. Böttner, J. Karstens, and J. Elger (2017), An 1888 volcanic collapse becomes a benchmark for tsunami models, Eos, 98, https://doi.org/10.1029/2017EO083743. Published on 10 October 2017.

    See the full article here .

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