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.

Artificial Intelligence technique based on Deep Learning is used to differentiate seismic waves before and after a M6.5 earthquake. The model classifies foreshocks, aftershocks, and time-to-failure, providing insights into how fault zone properties change before and after earthquakes.

Details

Title
Probing the evolution of fault properties during the seismic cycle with deep learning
Author
Laurenti, Laura 1   VIAFID ORCID Logo  ; Paoletti, Gabriele 2   VIAFID ORCID Logo  ; Tinti, Elisa 2   VIAFID ORCID Logo  ; Galasso, Fabio 3   VIAFID ORCID Logo  ; Collettini, Cristiano 2   VIAFID ORCID Logo  ; Marone, Chris 4   VIAFID ORCID Logo 

 Sapienza University of Rome, Department of Computer, Control and Management Engineering, Rome, Italy (GRID:grid.7841.a) 
 Sapienza University of Rome, Department of Earth Sciences, Rome, Italy (GRID:grid.7841.a) 
 Sapienza University of Rome, Department of Computer Science, Rome, Italy (GRID:grid.7841.a) 
 Sapienza University of Rome, Department of Earth Sciences, Rome, Italy (GRID:grid.7841.a); Pennsylvania State University, Department of Geosciences, Pennsylvania, USA (GRID:grid.29857.31) (ISNI:0000 0001 2097 4281) 
Pages
10025
Publication year
2024
Publication date
2024
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3131034414
Copyright
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.