From The New York Times: “A.I. Is Helping Scientists Predict When and Where the Next Big Earthquake Will Be”

New York Times

From The New York Times

Oct. 26, 2018

Thomas Fuller
Cade Metz

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Jean-Francois Podevin

Countless dollars and entire scientific careers have been dedicated to predicting where and when the next big earthquake will strike. But unlike weather forecasting, which has significantly improved with the use of better satellites and more powerful mathematical models, earthquake prediction has been marred by repeated failure.

Some of the world’s most destructive earthquakes — China in 2008, Haiti in 2010 and Japan in 2011, among them — occurred in areas that seismic hazard maps had deemed relatively safe. The last large earthquake to strike Los Angeles, Northridge in 1994, occurred on a fault that did not appear on seismic maps.

Now, with the help of artificial intelligence, a growing number of scientists say changes in the way they can analyze massive amounts of seismic data can help them better understand earthquakes, anticipate how they will behave, and provide quicker and more accurate early warnings.

“I am actually hopeful for the first time in my career that we will make progress on this problem,” said Paul Johnson, a fellow at the Los Alamos National Laboratory who is among those at the forefront of this research.

Well aware of past earthquake prediction failures, scientists are cautious when asked how much progress they have made using A.I. Some in the field refer to prediction as “the P word,” because they do not even want to imply it is possible. But one important goal, they say, is to be able to provide reliable forecasts.

The earthquake probabilities that are provided on seismic hazard maps, for example, have crucial consequences, most notably in instructing engineers how they should construct buildings. Critics say these maps are remarkably inexact.

A map of Los Angeles lists the probability of an earthquake producing strong shaking within a given period of time — usually 50 years. That is based on a complex formula that takes into account, among other things, the distance from a fault, how fast one side of a fault is moving past the other, and the recurrence of earthquakes in the area.

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A study led by Katherine M. Scharer, a geologist with the United States Geological Survey, estimated dates for nine previous earthquakes along the Southern California portion of the San Andreas fault dating back to the eighth century. The last big earthquake on the San Andreas was in 1857.

Since the average interval between these big earthquakes was 135 years, a common interpretation is that Southern California is due for a big earthquake. Yet the intervals between earthquakes are so varied — ranging from 44 years to 305 years — that taking the average is not a very useful prediction tool. A big earthquake could come tomorrow, or it could come in a century and a half or more.

This is one of the criticisms of Philip Stark, an associate dean at the University of California, Berkeley, at the Division of Mathematical and Physical Sciences. Dr. Stark describes the overall system of earthquake probabilities as “somewhere between meaningless and misleading” and has called for it to be scrapped.

The new A.I.-related earthquake research is leaning on neural networks, the same technology that has accelerated the progress of everything from talking digital assistants to driverless cars. Loosely modeled on the web of neurons in the human brain, a neural network is a complex mathematical system that can learn tasks on its own.

Scientists say seismic data is remarkably similar to the audio data that companies like Google and Amazon use in training neural networks to recognize spoken commands on coffee-table digital assistants like Alexa. When studying earthquakes, it is the computer looking for patterns in mountains of data rather than relying on the weary eyes of a scientist.

“Rather than a sequence of words, we have a sequence of ground-motion measurements,” said Zachary Ross, a researcher in the California Institute of Technology’s Seismological Laboratory who is exploring these A.I. techniques. “We are looking for the same kinds of patterns in this data.”

Brendan Meade, a professor of earth and planetary sciences at Harvard, began exploring these techniques after spending a sabbatical at Google, a company at the forefront of A.I. research.

His first project showed that, at the very least, these machine-learning methods could significantly accelerate his experiments. He and his graduate students used a neural network to run an earthquake analysis 500 times faster than they could in the past. What once took days now took minutes.

Dr. Meade also found that these A.I. techniques could lead to new insights. In the fall, with other researchers from Google and Harvard, he published a paper showing how neural networks can forecast earthquake aftershocks. This kind of project, he believes, represents an enormous shift in the way earthquake science is done. Similar work is underway at places like Caltech and Stanford University.

“We are at a point where the technology can do as well as — or better than — human experts,” Dr. Ross said.

Driving that guarded optimism is the belief that as sensors get smaller and cheaper, scientists will be able to gather larger amounts of seismic data. With help from neural networks and similar A.I. techniques, they hope to glean new insights from all this data.

Dr. Ross and other Caltech researchers are using these techniques to build systems that can more accurately recognize earthquakes as they are happening and anticipate where the epicenter is and where the shaking will spread.

Japan and Mexico have early warning systems, and California just rolled out its own. But scientists say artificial intelligence could greatly improve their accuracy, helping predict the direction and intensity of a rupture in the earth’s crust and providing earlier warnings to hospitals and other institutions that could benefit from a few extra seconds of preparation.

“The more detail you have, the better your forecasts will be,” Dr. Ross said.

Scientists working on these projects said neural networks have their limits. Though they are good at finding familiar signals in data, they are not necessarily suited to finding new kinds of signals — like the sounds tectonic plates make as they grind together.

But at Los Alamos, Dr. Johnson and his colleagues have shown that a machine-learning technique called “random forests” can identify previously unknown signals in a simulated fault created inside a lab. In one case, their system showed that a particular sound made by the fault, which scientists previously thought was meaningless, was actually an indication of when an earthquake would arrive.

Some scientists, like Robert Geller, a seismologist at the University of Tokyo, are unconvinced that A.I. will improve earthquake forecasts. He questions the very premise that past earthquakes can predict future ones. And ultimately, he said, we would only know the effectiveness of A.I. forecasting when earthquakes can be predicted beyond random chance.

“There are no shortcuts,” Dr. Geller said. “If you cannot predict the future, then your hypothesis is wrong.”

See the full article here .

Earthquake Alert

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Earthquake Alert

Earthquake Network projectEarthquake Network is a research project which aims at developing and maintaining a crowdsourced smartphone-based earthquake warning system at a global level. Smartphones made available by the population are used to detect the earthquake waves using the on-board accelerometers. When an earthquake is detected, an earthquake warning is issued in order to alert the population not yet reached by the damaging waves of the earthquake.

The project started on January 1, 2013 with the release of the homonymous Android application Earthquake Network. The author of the research project and developer of the smartphone application is Francesco Finazzi of the University of Bergamo, Italy.

Get the app in the Google Play store.

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Smartphone network spatial distribution (green and red dots) on December 4, 2015

Meet The Quake-Catcher Network

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).

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

ShakeAlert: An Earthquake Early Warning System for the West Coast of the United States

The U. S. Geological Survey (USGS) along with a coalition of State and university partners is developing and testing an earthquake early warning (EEW) system called ShakeAlert for the west coast of the United States. Long term funding must be secured before the system can begin sending general public notifications, however, some limited pilot projects are active and more are being developed. The USGS has set the goal of beginning limited public notifications in 2018.

Watch a video describing how ShakeAlert works in English or Spanish.

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. ShakeAlert can give enough time to slow trains and taxiing planes, to prevent cars from entering bridges and tunnels, to move away from dangerous machines or chemicals in work environments and to take cover under a desk, or to automatically shut down and isolate industrial systems. Taking such actions before shaking starts can reduce damage and casualties during an earthquake. It can also prevent cascading failures in the aftermath of an event. For example, isolating utilities before shaking starts can reduce the number of fire initiations.

System Goal

The USGS will issue public warnings of potentially damaging earthquakes and provide warning parameter data to government agencies and private users on a region-by-region basis, as soon as the ShakeAlert system, its products, and its parametric data meet minimum quality and reliability standards in those geographic regions. The USGS has set the goal of beginning limited public notifications in 2018. Product availability will expand geographically via ANSS regional seismic networks, such that ShakeAlert products and warnings become available for all regions with dense seismic instrumentation.

Current Status

The West Coast ShakeAlert system is being developed by expanding and upgrading the infrastructure of regional seismic networks that are part of the Advanced National Seismic System (ANSS); the California Integrated Seismic Network (CISN) is made up of the Southern California Seismic Network, SCSN) and the Northern California Seismic System, NCSS and the Pacific Northwest Seismic Network (PNSN). This enables the USGS and ANSS to leverage their substantial investment in sensor networks, data telemetry systems, data processing centers, and software for earthquake monitoring activities residing in these network centers. The ShakeAlert system has been sending live alerts to “beta” users in California since January of 2012 and in the Pacific Northwest since February of 2015.

In February of 2016 the USGS, along with its partners, rolled-out the next-generation ShakeAlert early warning test system in California joined by Oregon and Washington in April 2017. This West Coast-wide “production prototype” has been designed for redundant, reliable operations. The system includes geographically distributed servers, and allows for automatic fail-over if connection is lost.

This next-generation system will not yet support public warnings but does allow selected early adopters to develop and deploy pilot implementations that take protective actions triggered by the ShakeAlert notifications in areas with sufficient sensor coverage.

Authorities

The USGS will develop and operate the ShakeAlert system, and issue public notifications under collaborative authorities with FEMA, as part of the National Earthquake Hazard Reduction Program, as enacted by the Earthquake Hazards Reduction Act of 1977, 42 U.S.C. §§ 7704 SEC. 2.

For More Information

Robert de Groot, ShakeAlert National Coordinator for Communication, Education, and Outreach
rdegroot@usgs.gov
626-583-7225

Learn more about EEW Research

ShakeAlert Fact Sheet

ShakeAlert Implementation Plan

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