Machine learning predicts earthquakes

The predictable unpredictability of earthquakes has confounded experts for decades, but by applying new developments in machine learning, Los Alamos National Laboratory scientists can pinpoint when a quake may occur.

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Machine-learning earthquake prediction shows promise

Machine learning helps predict earthquakes | Los Alamos National Laboratory
By listening to the acoustic signal emitted by a lab-created earthquake, a computer science approach using machine learning can predict how long until a fault fails
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Listening to faultline’s grumbling gives countdown to future quakes


LOS ALAMOS, N.M., Aug. 30, 2017—By listening to the acoustic signal emitted by a laboratory-created earthquake, a computer science approach using machine learning can predict the time remaining before the fault fails.


“At any given instant, the noise coming from the lab fault zone provides quantitative information on when the fault will slip,” said Paul Johnson, a Los Alamos National Laboratory fellow and lead investigator on the research, which was published in Geophysical Research Letters.


“The novelty of our work is the use of machine learning to discover and understand new physics of failure, through examination of the recorded auditory signal from the experimental setup. I think the future of earthquake physics will rely heavily on machine learning to process massive amounts of raw seismic data. Our work represents an important step in this direction,” he said.


Not only does the work have potential significance to earthquake forecasting, Johnson said, but the approach is far-reaching, applicable to potentially all failure scenarios including nondestructive testing of industrial materials brittle failure of all kinds, avalanches and other events.


Machine learning is an artificial intelligence approach to allowing the computer to learn from new data, updating its own results to reflect the implications of new information.


The machine learning technique used in this project also identifies new signals, previously thought to be low-amplitude noise, that provide forecasting information throughout the earthquake cycle. “These signals resemble Earth tremor that occurs in association with slow earthquakes on tectonic faults in the lower crust,” Johnson said. “There is reason to expect such signals from Earth faults in the seismogenic zone for slowly slipping faults.”


Machine learning algorithms can predict failure times of laboratory quakes with remarkable accuracy. The acoustic emission (AE) signal, which characterizes the instantaneous physical state of the system, reliably predicts failure far into the future. This is a surprise, Johnson pointed out, as all prior work had assumed that only the catalog of large events is relevant, and that small fluctuations in the AE signal could be neglected.


To study the phenomena, the team analyzed data from a laboratory fault system that contains fault gouge, the ground-up material created by the stone blocks sliding past one another. An accelerometer recorded the acoustic emission emanating from the shearing layers.


Following a frictional failure in the labquake, the shearing block moves or displaces, while the gouge material simultaneously dilates and strengthens, as shown by measurably increasing shear stress and friction. “As the material approaches failure, it begins to show the characteristics of a critical stress regime, including many small shear failures that emit impulsive acoustic emissions,” Johnson described.


“This unstable state concludes with an actual labquake, in which the shearing block rapidly displaces, the friction and shear stress decrease precipitously, and the gouge layers simultaneously compact,” he said. Under a broad range of conditions, the apparatus slide-slips fairly regularly for hundreds of stress cycles during a single experiment. And importantly, the signal (due to the gouge grinding and creaking that ultimately leads to the impulsive precursors) allows prediction in the laboratory, and we hope will lead to advances in prediction in Earth, Johnson said.


The paper: "Machine learning predicts laboratory earthquakes," Geophysical Research Letters.


The funding: Los Alamos National Laboratory Directed Research and Development (LDRD).

Can we someday predict earthquakes?

Predicting earthquakes | Los Alamos National LaboratoryNew ways of looking at seismic information and innovative laboratory experiments are offering tantalizing clues to what triggers earthquakes—and when.

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Can we someday predict earthquakes?


The only thing we know for sure about earthquakes is that one will happen again very soon. Earthquakes pose a vital yet puzzling set of research questions that have confounded scientists for decades, but new ways of looking at seismic information and innovative laboratory experiments are offering tantalizing clues to what triggers earthquakes — and when.


Millions of earthquakes shake the globe every year, the ground suddenly lurching in response to movements of the tectonic plates that form Earth’s crust. These plates jostle over, under and against each other as they shift. All that shoving and grinding builds up stress along faults — fractures or breaks in the rock of the crust — until something has to give: an earthquake.


The science of seismology seeks to understand what causes earthquakes by tracking their occurrence, measuring their force and using sophisticated imaging technology to probe the subsurface geology where they happen. Today we can locate faults, characterize them and explain many of the stresses building toward their failure.


We still don’t fully understand the details inside faults or how those details might control the location and timing of earthquakes, but geophysicists and computer scientists at Los Alamos National Laboratory and their colleagues are wielding an array of new tools to study the interactions among earthquakes, precursor quakes (often very small earth movements) and faults.


These tools include aquarium-sized experiments in the laboratory that replicate quakes, more sensitive and more densely deployed seismology instruments worldwide producing vast data streams and supercomputers that can make sense out of this massive data set.


Because it’s so hard to observe geologic-scale interactions underground, the Los Alamos team, with collaborators at Penn State, the U.S. Geological Survey, ETH in Zurich and the Institute of Physics of the Globe and the Ecole Normale in Paris, France, has developed laboratory experiments to figure out when faults might fail.


Using an “earthquake machine” built by Chris Marone at Penn State, the team is investigating the role that “fault gouge” — the loose material created by the constant grinding at a fault — may play in triggering and influencing the size of quakes. The lab machine creates conditions similar to faults with gouge, then submits them to sound waves as surrogate seismic waves.


These experiments have produced strange and startling effects. The team observed that when stress built up on a fault and it approached failure as a miniature earthquake, a series of small precursor quakes rippled through at rates that followed specific patterns. Comparing these results to actual seismic data reveals similar rate failures when precursors are observed before real earthquakes.


They also found that the applied sound waves played a key role in triggering laboratory-scale earthquakes by making the gouge more fluid. Amazingly, precursors can trigger major earthquakes thousands of miles away from their origin and often months later.


Frequently no precursors are observed before a quake, but that might be because extremely small precursors elude detection. To test that hypothesis, Los Alamos is bringing its supercomputing horsepower to bear on the subject, combing through historical data to see if smaller-magnitude events seemed to signal precursor events preceding temblors in the past. Starting with the lab data on simulated precursor quakes and using a technique called machine learning, Los Alamos is “training” a computer program to sift through this data set and spot precursors.


After the computer program has taught itself to recognize precursors, the team will run the program against actual seismic data. The team will then compare the accuracy of those results to more traditional interpretations of the same data.


Other data sets from actual seismic monitoring will be added to the experiment in a process called “ground truthing,” intended to verify the computer program’s predictive accuracy. The goal is to develop a computer program that reviews new data in almost real time and spots precursors heralding an upcoming major earthquake.


Within the next year or so, the team plans to use the newest computers at Los Alamos, some of the most powerful in the world, to crunch the numbers from larger and larger data sets — first from mining areas, then tectonic regions like the San Andreas fault and finally worldwide — to reveal previously hidden patterns of seismic signals.


Dreaming big, the team dares to pursue the Holy Grail of seismology: forecasting major earthquakes. That won’t happen any time soon. The first level of forecasting will be characterizing when an earthquake might happen within some time span. But as supercomputing power continues to grow, it will certainly drive us closer to accurately forecasting massive earthquakes.


While Los Alamos maintains technical expertise in seismology and the geodynamics of Earth’s crust as a means of monitoring underground nuclear testing worldwide, that expertise could one day alleviate suffering from unexpected earthquakes on a global scale.


Paul Johnson is a geophysicist, Los Alamos National Laboratory Fellow, a Fellow of the Acoustical Society of America and an American Geophysical Union Fellow in the Laboratory’s Geophysics group.


This story originally appeared in The Santa Fe New Mexican.

Published Research

Machine Learning for Materials Science

By Bernard Rouet-Leduc
Abstract

Machine learning is a branch of artificial intelligence that uses data to automatically build inferences and models designed to generalize and make predictions. In this thesis, the use of machine learning in materials science is explored, for two different problems: the optimisation of gallium nitride optoelectronic devices, and the prediction of material failure in the setting of laboratory earthquakes.

Machine Learning for Materials Science



Machine learning is a branch of artificial intelligence that uses data to automatically build inferences and models designed to generalize and make predictions. In this thesis, the use of machine learning in materials science is explored, for two different problems: the optimisation of gallium nitride optoelectronic devices, and the prediction of material failure in the setting of laboratory earthquakes. Light emitting diodes based on III-nitrides quantum wells have become ubiquitous as a light source, owing to their direct band-gap that covers UV, visible and infra-red light, and their very high quantum efficiency. This efficiency originates from most electronic transitions across the band-gap leading to the emission of a photon. At high currents however this efficiency sharply drops. In chapters 3 and 4 simulations are shown to provide an explanation for experimental results, shedding a new light on this drop of efficiency. Chapter 3 provides a simple and yet accurate model that explains the experimentally observed beneficial effect that silicon doping has on light emitting diodes. Chapter 4 provides a model for the experimentally observed detrimental effect that certain V-shaped defects have on light emitting diodes. These results pave the way for the association of simulations to detailed multi-microscopy. In the following chapters 5 to 7, it is shown that machine learning can leverage the use of device simulations, by replacing in a targeted and efficient way the very labour intensive tasks of making sure the numerical parameters of the simulations lead to convergence, and that the physical parameters reproduce experimental results. It is then shown that machine learning coupled with simulations can find optimal light emitting diodes structures, that have a greatly enhanced theoretical efficiency. These results demonstrate the power of machine learning for leveraging and automatising the exploration of device structures in simulations. Material failure is a very broad problem encountered in a variety of fields, ranging from engineering to Earth sciences. The phenomenon stems from complex and multi-scale physics, and failure experiments can provide a wealth of data that can be exploited by machine learning. In chapter 8 it is shown that by recording the acoustic waves emitted during the failure of a laboratory fault, an accurate predictive model can be built. The machine learning algorithm that is used retains the link with the physics of the experiment, and a new signal is thus discovered in the sound emitted by the fault. This new signal announces an upcoming laboratory earthquake, and is a signature of the stress state of the material. These results show that machine learning can help discover new signals in experiments where the amount of data is very large, and demonstrate a new method for the prediction of material failure.


Videos

What's That Noise? Earthquake Prediction
An acoustic signal emitted by a lab-created earthquake can help us predict how much time there is before the fault fails.

Can We Predict Earthquakes?
The only thing we know for sure about earthquakes is that one will happen again very soon.

Photos

    Black download icon A simulation, captured by a camera with a polarizing lens, represents the structure and dynamics of geological faults.

    Black download icon Geophysicist Paul Johnson holds a block of acrylic plastic used to study the dynamic interaction of elastic waves within solids, which may offer clues to understanding earthquakes.


    Expert

    Geophysicist Paul Johnson of the Los Alamos National Laboratory researches the science of predicting earthquakes.

    Paul Johnson

    Paul Johnson is a geophysicist, Los Alamos National Laboratory Fellow, a Fellow of the Acoustical Society of America, and an American Geophysical Union Fellow in the Laboratory’s Geophysics group. His research specialties include:

    • Nonlinear and Disordered Systems
    • Seismic Strong Ground Motion
    • General Acoustics
    • Rock Physics
    • Acoustical Nondestructive Testing of Materials
    • Earthquake Source Mechanics
    • Time Reverse Acoustics in Solids

    Contact

    Charles Poling, (505) 257-8006, cpoling@lanl.gov
    Nick Njegomir, (505) 665 9394, 
    nickn@lanl.gov

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