Earthquake forecasting and prediction have long and in some cases sordid histories but recent work has rekindled interest based on advances in early warning, hazard assessment for induced seismicity and successful prediction of laboratory earthquakes. In the lab, frictional stick-slip events provide an analog for earthquakes and the seismic cycle. Labquakes are also ideal targets for machine learning (ML) because they can be produced in long sequences under controlled conditions. Indeed, recent works show that ML can predict several aspects of labquakes using fault zone acoustic emissions (AE). Here, we extend these works with: 1) deep learning (DL) methods for labquake prediction, 2) by introducing an autoregressive (AR) forecasting DL method to predict fault zone shear stress, and 3) by expanding the range of lab fault zones studied. The AR methods allow forecasting stress at future times via iterative predictions using previous measurements. Our DL methods outperform existing ML models and can predict based on limited training. We also explore forecasts beyond a single seismic cycle for aperiodic failure. We describe significant improvements to existing methods of labquake prediction and demonstrate: 1) that DL models based on Long-Short Term Memory and Convolution Neural Networks predict labquakes under conditions including pre-seismic creep, aperiodic events and alternating slow/fast events and 2) that fault zone stress can be predicted with fidelity, confirming that acoustic energy is a fingerprint of fault zone stress. Our DL methods predict time to start of failure (TTsF) and time to the end of Failure (TTeF) for labquakes. Interestingly, TTeF is successfully predicted in all seismic cycles, while the TTsF prediction varies with the amount of preseismic fault creep. We report AR methods to forecast the evolution of fault stress using three sequence modelling frameworks: LSTM, Temporal Convolution Network and Transformer Network. AR forecasting is distinct from existing predictive models, which predict only a target variable at a specific time. The results for forecasting beyond a single seismic cycle are limited but encouraging. Our ML/DL models outperform the state-of-the-art and our autoregressive model represents a novel framework that could enhance current methods of earthquake forecasting.

Deep learning for laboratory earthquake prediction and autoregressive forecasting of fault zone stress

Laura Laurenti
;
Elisa Tinti;Fabio Galasso;Luca Franco;Chris Marone
2022

Abstract

Earthquake forecasting and prediction have long and in some cases sordid histories but recent work has rekindled interest based on advances in early warning, hazard assessment for induced seismicity and successful prediction of laboratory earthquakes. In the lab, frictional stick-slip events provide an analog for earthquakes and the seismic cycle. Labquakes are also ideal targets for machine learning (ML) because they can be produced in long sequences under controlled conditions. Indeed, recent works show that ML can predict several aspects of labquakes using fault zone acoustic emissions (AE). Here, we extend these works with: 1) deep learning (DL) methods for labquake prediction, 2) by introducing an autoregressive (AR) forecasting DL method to predict fault zone shear stress, and 3) by expanding the range of lab fault zones studied. The AR methods allow forecasting stress at future times via iterative predictions using previous measurements. Our DL methods outperform existing ML models and can predict based on limited training. We also explore forecasts beyond a single seismic cycle for aperiodic failure. We describe significant improvements to existing methods of labquake prediction and demonstrate: 1) that DL models based on Long-Short Term Memory and Convolution Neural Networks predict labquakes under conditions including pre-seismic creep, aperiodic events and alternating slow/fast events and 2) that fault zone stress can be predicted with fidelity, confirming that acoustic energy is a fingerprint of fault zone stress. Our DL methods predict time to start of failure (TTsF) and time to the end of Failure (TTeF) for labquakes. Interestingly, TTeF is successfully predicted in all seismic cycles, while the TTsF prediction varies with the amount of preseismic fault creep. We report AR methods to forecast the evolution of fault stress using three sequence modelling frameworks: LSTM, Temporal Convolution Network and Transformer Network. AR forecasting is distinct from existing predictive models, which predict only a target variable at a specific time. The results for forecasting beyond a single seismic cycle are limited but encouraging. Our ML/DL models outperform the state-of-the-art and our autoregressive model represents a novel framework that could enhance current methods of earthquake forecasting.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1656424
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