2025-01-24 ペンシルベニア州立大学 (PennState)
＜関連情報＞
- https://www.psu.edu/news/engineering/story/predicting-lab-earthquakes-physics-informed-artificial-intelligence
- https://www.nature.com/articles/s41598-024-75826-y
- https://www.nature.com/articles/s41467-023-39377-6
物理学に精通したニューラルネットワークが，実験室でのスティックスリップ実験の音響モニタリングから速度と状態の摩擦パラメータを取得可能 Physics informed neural network can retrieve rate and state friction parameters from acoustic monitoring of laboratory stick-slip experiments
Prabhav Borate,Jacques Rivière,Samson Marty,Chris Marone,Daniel Kifer & Parisa Shokouhi
Scientific Reports Published:19 October 2024
DOI:https://doi.org/10.1038/s41598-024-75826-y
Abstract
Various machine learning (ML) and deep learning (DL) techniques have been recently applied to the forecasting of laboratory earthquakes from friction experiments. The magnitude and timing of shear failures in stick-slip cycles are predicted using features extracted from the recorded ultrasonic or acoustic emission (AE) signals. In addition, the Rate and State Friction (RSF) constitutive laws are extensively used to model the frictional behavior of faults. In this work, we use data from shear experiments coupled with passive acoustic (variance, kurtosis, and AE rate) interleaved with active source ultrasonic monitoring (transmitted wave amplitude) to develop physics-informed neural network (PINN) models incorporating the RSF law and AE rate generation equation with wave amplitude serving as a proxy for friction state variable. This PINN framework allows learning RSF parameters from stick-slip experiments rather than measuring them through a series of velocity step experiments. We observe that when the stick-slip cycles are irregular, the PINN models outperform the data-driven DL models. Transfer learning (TL) PINN models are also developed by pre-training on data collected at one normal stress level followed by forecasting shear failures and retrieving RSF parameters at other stress levels (i.e., with different recurrence intervals) after retraining on a limited amount of new data. Our findings suggest that TL models perform better compared to standalone models. Both standalone and TL PINN-estimated RSF parameters and their ground truth values show excellent agreements thus demonstrating that RSF parameters can be retrieved from laboratory stick-slip experiments using the corresponding acoustic data and that the transmitted wave amplitude provides a good representation of the evolving frictional state during stick-slips.
物理学情報に基づいたニューラルネットワークと断層帯音響モニタリングを使って実験室の地震を予測する Using a physics-informed neural network and fault zone acoustic monitoring to predict lab earthquakes
Prabhav Borate,Jacques Rivière,Chris Marone,Ankur Mali,Daniel Kifer & Parisa Shokouhi
Nature communications Published:21 June 2023
DOI:https://doi.org/10.1038/s41467-023-39377-6
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.
