Deep learning to investigate post-seismic evolution of fault zone elastic properties

Fault zone properties evolve dynamically during the seismic cycle due to stress changes, microcracking, and wall rock damage. Understanding these changes is vital to gaining insights into earthquake preparation and post-seismic processes. The latter include fault healing, which refers to the recovery of mechanical and elastic properties in fault zones after seismic and aseismic fault slip. Despite its importance, detecting and characterizing fault healing through seismic signals remains a challenge due to the subtle nature of these changes.

In this study, we investigate the potential of deep learning techniques, specifically a 4-layer Convolutional Neural Network (CNN), to characterize post-seismic evolution by analyzing raw seismic waveforms recorded after the largest event (Mw 6.5,  30 October) of the 2016 Central Italy seismic sequence. These data provide a unique opportunity to examine fault zone dynamics. A key aspect of our approach is the hypothesis that ray paths traversing highly impacted areas of the fault zone contain richer information about its temporal evolution. To test this hypothesis, we examined seismic waves from two clusters — DHwS, located in the hanging wall beneath the hypocentral region, and C1, situated in the footwall. They represent contrasting ray trajectories as recorded on seismic stations MC2 and MMO1. Seismic waves recorded at MC2 pass through heavily damaged fault regions, which are likely to reveal evolving fault properties, whereas MMO1 predominantly captures paths that skirt or in the case of C1 completely miss these impacted areas, serving as a comparative baseline.

We assessed temporal variations in elastic properties using binary classification tests on normalized, raw seismic waveforms of events before and after a reference date. This date was arbitrarily selected within the temporal range of the analyzed seismicity and serves solely as a neutral point of comparison. Our hypothesis is that if the CNN can achieve good classification performance, it implies the presence of time-evolving properties in the fault zone, potentially linked to healing processes or other time-dependent factors.

To further validate these findings, we employed adversarial training, a technique designed to disentangle time-dependent effects from structural changes. By introducing controlled label noise into one cluster during training, we isolated the influence of confounding factors such as seasonal variations. Preliminary results suggest that adversarial training enhances the model's robustness and provides valuable insights into the time-evolving properties of the fault zone.

Deep learning offers significant potential for analyzing spatiotemporal changes in elastic properties and thus the evolution of fault zone properties over the seismic cycle. By detecting subtle temporal and structural changes, we hope to gain a deeper understanding of fault dynamics and post-seismic processes.