From Harvard Gazette: “Learning to find ‘quiet’ earthquakes’

Harvard University
Harvard University

Harvard Gazette

March 14, 2018
Peter Reuell

Researchers create algorithm that can separate small disturbances from seismic noise.

Assistant Professor Marine Denolle is the co-author of a new study that uses computer-learning algorithms to detect tiny earthquakes hidden in seismic “noise,” like human activity, that could be used for real-time detection and early warnings. Kris Snibbe/Harvard Staff Photographer.

Imagine standing in the middle of Harvard Square and the swirling cacophony that comes with it: the thrum of passing cars, the rumbling of trucks and buses, the chattering tourists and students, and a busker or two competing for attention.


Now imagine trying to filter out all that noise and pick up a whisper from a block away, and you have some idea of the challenge facing seismologists.

Marine Denolle, Assistant professor at the Radcliffe Institute and assistant professor of earth and planetary sciences in the Harvard Faculty of Arts and Sciences.

Marine Denolle is one of several co-authors of a study that used computer-learning algorithms to identify small earthquakes buried in seismic noise. Other authors are Thibaut Perol, who has doctoral and master’s degrees from the Harvard John A. Paulson School for Engineering and Applied Sciences and the Harvard Institute for Applied Computational Science, and Michaël Gharbi, a doctoral student at Massachusetts Institute of Technology. The study was published in the journal Science Advances.

While researchers hope the algorithm may one day allow for development of a system for real-time earthquake detection, the ability to track limited “micro-seismicity” should help scientists draw a clearer picture of a number of processes in the Earth.

“We can use this data to map fluid migration, whether it’s magma or wastewater or oil,” Denolle said. “In addition, there is a redistribution of stresses after an earthquake … but it’s very difficult to understand that process because the only data points we have are the earthquake, so we have to infer our models from there. This can help give us a more complete picture.”


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.


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

Learn more about EEW Research

ShakeAlert Fact Sheet

ShakeAlert Implementation Plan


“Seismometers are incredibly sensitive,” she said. “They can pick up signals from everything from a person walking to ocean waves hitting on the shore to the movement of a tree’s roots as it sways in the wind.

Denolle said that studying the data will be easy — because it’s already being collected.

“Seismometers are incredibly sensitive,” she said. “They can pick up signals from everything from a person walking to ocean waves hitting on the shore to the movement of a tree’s roots as it sways in the wind.

“But the signals of these smaller earthquakes are buried in that background noise,” she continued. “This is really about signal detection. That’s why deep-learning techniques are useful — because you can extract features from the noise.”

To build an algorithm capable of sorting through that seismic noise, Denolle and colleagues went to Oklahoma.

There, researchers spent nearly two years collecting data on more than 2,000 recognized earthquakes. That data, along with seismic noise, was used to train a learning algorithm to pick out previously unidentified quakes hidden in the information.

“We found that in a typical month, where there might be 100 earthquakes detected, there were actually at least 3,500 events,” she said. “That’s two or three orders of magnitude larger. So it works, but what we wanted to do was not only to detect earthquakes but to identify and locate them in real time for early warning systems.”

Dots of varying color and size denote the location, depth, and intensity of seismic activity along the San Andreas fault in California. Kris Snibbe/Harvard Staff Photographer.

To provide that early warning, Denolle said, the system has to work fast, so Perol designed the algorithm at the heart of the system with efficiency in mind. Because of the massive amounts of data collected in the field — some data sets are as large as 100 terabytes — Denolle said traditional algorithms could take minutes or longer just to analyze the data from a single day.

“But with the code we developed, it works in seconds,” she said.

Denolle and her colleagues later applied the algorithm to include seismic data collected in Spain, and it was able to identify earthquakes, even though seismic stations were placed further apart and the quake waveforms were dramatically different from those used to train the system.

“We applied this code blindly, with all the optimization for Oklahoma, and it still detected most of the earthquakes,” Denolle said. “That suggests that this code is very generalizable.”

Going forward, Denolle said she hopes to refine the algorithm to improve the ability to pinpoint the location of earthquakes. She plans to conduct additional tests using larger data sets, like those collected around volcanoes.

“This is level one. We need to detect earthquakes to understand what’s going on in the Earth,” said Denolle. “Looking at these smaller events might tell us something about bigger events … so this is fundamental.”

See the full article here .

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