Using a physics-informed neural network and fault zone acoustic monitoring to predict lab earthquakes

Journal Article · 20 June 2023 · Nature Communications

DOI: https://doi.org/10.1038/s41467-023-39377-6 · OSTI ID:1986092

Borate, Prabhav; Rivière, Jacques; Marone, Chris; Mali, Ankur ; Kifer, Daniel; Shokouhi, Parisa

Abstract Predicting failure in solids has broad applications including earthquake prediction which remains an unattainable goal. However, recent machine learning work shows that laboratory earthquakes can be predicted using micro-failure events and temporal evolution of fault zone elastic properties. Remarkably, these results come from purely data-driven models trained with large datasets. Such data are equivalent to centuries of fault motion rendering application to tectonic faulting unclear. In addition, the underlying physics of such predictions is poorly understood. Here, we address scalability using a novel Physics-Informed Neural Network (PINN). Our model encodes fault physics in the deep learning loss function using time-lapse ultrasonic data. PINN models outperform data-driven models and significantly improve transfer learning for small training datasets and conditions outside those used in training. Our work suggests that PINN offers a promising path for machine learning-based failure prediction and, ultimately for improving our understanding of earthquake physics and prediction.

• Research Organization: Pennsylvania State Univ., University Park, PA (United States); Pennsylvania State University, University Park, PA (United States)
• Sponsoring Organization: European Research Council (ERC); National Science Foundation (NSF); USDOE; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Office; USDOE Office of Science (SC), Basic Energy Sciences (BES)
• Grant/Contract Number: EE0008763; SC0017585; SC0020512; SC0022842
• OSTI ID: 1986092
• Journal Information: Nature Communications, Journal Name: Nature Communications Journal Issue: 1 Vol. 14; ISSN 2041-1723
• Publisher: Nature Publishing GroupCopyright Statement
• Country of Publication: United Kingdom
• Language: English