Probing the evolution of fault properties during the seismic cycle with deep learning

Laura Laurenti (), Gabriele Paoletti, Elisa Tinti, Fabio Galasso, Cristiano Collettini and Chris Marone
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Laura Laurenti: Sapienza University of Rome
Gabriele Paoletti: Sapienza University of Rome
Elisa Tinti: Sapienza University of Rome
Fabio Galasso: Sapienza University of Rome
Cristiano Collettini: Sapienza University of Rome
Chris Marone: Sapienza University of Rome

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: Abstract We use seismic waves that pass through the hypocentral region of the 2016 M6.5 Norcia earthquake together with Deep Learning (DL) to distinguish between foreshocks, aftershocks and time-to-failure (TTF). Binary and N-class models defined by TTF correctly identify seismograms in test with > 90% accuracy. We use raw seismic records as input to a 7 layer CNN model to perform the classification. Here we show that DL models successfully distinguish seismic waves pre/post mainshock in accord with lab and theoretical expectations of progressive changes in crack density prior to abrupt change at failure and gradual postseismic recovery. Performance is lower for band-pass filtered seismograms (below 10 Hz) suggesting that DL models learn from the evolution of subtle changes in elastic wave attenuation. Tests to verify that our results indeed provide a proxy for fault properties included DL models trained with the wrong mainshock time and those using seismic waves far from the Norcia mainshock; both show degraded performance. Our results demonstrate that DL models have the potential to track the evolution of fault zone properties during the seismic cycle. If this result is generalizable it could improve earthquake early warning and seismic hazard analysis.

Date: 2024
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DOI: 10.1038/s41467-024-54153-w

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