Insights on earthquake nucleation revealed by numerical simulation and unsupervised machine learning of laboratory-scale earthquake.

ABSTRACT: Understanding earthquake nucleation is vital for predicting and mitigating seismic events, saving lives, and enhancing construction practices in earthquake-prone areas. Cascade triggering and preslip triggering are prevalent theories, posing challenges in differentiation based on field observations. Our study employs a novel unsupervised machine learning pipeline, integrating macroscopic- and grain-scale data from stick-slip experiments in a discrete element method (DEM) framework. Running 27 simulations, we cluster foreshocks and mainshocks separately and assess their correlation. The study supports the cascade triggering model on the macro-scale, as we did not observe any scaling between nucleation parameters and the mainshock size. On the other hand, further grain-scale analysis identif

SUBMITTER: Ye SH 

PROVIDER: S-EPMC11579008 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature