Public Abstract

DE-SC0020512: Machine Learning and Data Science to Advance Laboratory Earthquake Prediction and Illuminate the Mechanics of Precursors to Failure

Award Status: Expired

Institution: The Pennsylvania State University, University Park, PA
UEI: NPM2J7MSCF61
DUNS: 003403953

Most Recent Award Date: 01/30/2023
Number of Support Periods: 3
PM: Wilk, Philip

Current Budget Period: 02/01/2022 - 03/31/2023
Current Project Period: 02/01/2020 - 03/31/2023
PI: Marone, Chris

Supplement Budget Period: N/A

Public Abstract

Earthquakes represent one of our greatest natural hazards and in recent years human induced seismicity is adding to the threat. Even a modest improvement in the ability to forecast devastating large earthquakes or smaller shallow events associated with fluid injection could save thousands of lives and billions of dollars. Current efforts to forecast earthquakes are limited by knowledge of earthquake physics and hampered by a lack of reliable lab or field observations. However, recent work in our lab and elsewhere provides a critical opportunity for advancement. We have found: 1) clear and consistent precursors prior to earthquake-like failure in the laboratory and 2) that lab earthquakes can be predicted using machine learning (ML). These works show that stick-slip failure events –the lab equivalent of earthquakes– are preceded by a cascade of micro-failure events that radiate elastic energy in a manner that foretells catastrophic failure. Remarkably, ML predicts the fault zone stress state, the failure time and in some cases the magnitude of lab earthquakes. In addition, the observations include clear precursors to failure in the form of changes in fault zone properties prior to lab earthquakes. Precursors have been observed in previous laboratory studies but their origin is poorly understood and their possible connection to ML based earthquake prediction is unknown. Here, we propose to dramatically expand these efforts and develop an integrated data science approach to illuminate the physics of earthquake precursors and lab earthquake prediction. The proposed work will accelerate the development of ML, artificial intelligence (AI), and related data science approaches by providing massive data sets that are tightly connected to critical scientific problems and by bringing together leading subject matter experts and data scientists. Earthquake physics involves phenomena that are far from equilibrium. The proposed work will leverage data science methods to illuminate these phenomena and investigate how they relate to earthquake prediction. In addition to a large database with many types of labeled events that will be openly distributed, the proposed work will advance our fundamental understanding of seismic forecasting, earthquake physics, and fault rheology.

Earthquakes represent one of our greatest natural hazards and in recent years human induced seismicity is adding to the threat. Even a modest improvement in the ability to forecast devastating large earthquakes or smaller shallow events associated with fluid injection could save thousands of lives and billions of dollars. Current efforts to forecast earthquakes are limited by knowledge of earthquake physics and hampered by a lack of reliable lab or field observations. However, recent work in our lab and elsewhere provides a critical opportunity for advancement. We have found: 1) clear and consistent precursors prior to earthquake-like failure in the laboratory and 2) that lab earthquakes can be predicted using machine learning (ML). These works show that stick-slip failure events –the lab equivalent of earthquakes– are preceded by a cascade of micro-failure events that radiate elastic energy in a manner that foretells catastrophic failure. Remarkably, ML predicts the fault zone stress state, the failure time and in some cases the magnitude of lab earthquakes. In addition, the observations include clear precursors to failure in the form of changes in fault zone properties prior to lab earthquakes. Precursors have been observed in previous laboratory studies but their origin is poorly understood and their possible connection to ML based earthquake prediction is unknown. Here, we propose to dramatically expand these efforts and develop an integrated data science approach to illuminate the physics of earthquake precursors and lab earthquake prediction. The proposed work will accelerate the development of ML, artificial intelligence (AI), and related data science approaches by providing massive data sets that are tightly connected to critical scientific problems and by bringing together leading subject matter experts and data scientists. Earthquake physics involves phenomena that are far from equilibrium. The proposed work will leverage data science methods to illuminate these phenomena and investigate how they relate to earthquake prediction. In addition to a large database with many types of labeled events that will be openly distributed, the proposed work will advance our fundamental understanding of seismic forecasting, earthquake physics, and fault rheology.