Machine Learning for Predicting Earthquakes


(T) Not so long ago, everyone was saying “there is an app for everything’. Maybe, we should now change that sentence by “there is a machine learning algorithm for everything”, even for earthquake predictions, believe it or not.

A 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.

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 analyze 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 fail.

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 percents) 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 analyze 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 Ph.D. student at Cambridge.

“Machine learning enables the analysis of data sets 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.

Following is the abstract of the research paper led by Bertrand Rouet-Leduc:

“We apply machine learning to data sets from shear laboratory experiments, with the goal of identifying hidden signals that precede earthquakes. Here we show that by listening to the acoustic signal emitted by a laboratory fault, machine learning can predict the time remaining before it fails with great accuracy. These predictions are based solely on the instantaneous physical characteristics of the acoustical signal and do not make use of its history. Surprisingly, machine learning identifies a signal emitted from the fault zone previously thought to be low-amplitude noise that enables failure forecasting throughout the laboratory quake cycle. We infer that this signal originates from continuous grain motions of the fault gouge as the fault blocks displace. We posit that applying this approach to continuous seismic data may lead to significant advances in identifying currently unknown signals, in providing new insights into fault physics, and in placing bounds on fault failure times.”

The full paper has been published and is available at the following link: The Geophysical Research Letters.

And, following is a video from Paul Johnson:

Note 1: The content of this article is based on a press release from the University of Cambridge.

Note 2: The picture above is an aerial photo from Wikipedia of the San Andreas Fault looking northwest onto the Carrizo Plain with Soda Lake visible at the upper left.

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Categories: Machine Learning