Physics informed neural network can retrieve rate and state friction parameters from acoustic monitoring of laboratory stick-slip experiments.

ABSTRACT: Various machine learning (ML) and deep learning (DL) techniques have been recently applied to the forecasting of laboratory earthquakes from friction experiments. The magnitude and timing of shear failures in stick-slip cycles are predicted using features extracted from the recorded ultrasonic or acoustic emission (AE) signals. In addition, the Rate and State Friction (RSF) constitutive laws are extensively used to model the frictional behavior of faults. In this work, we use data from shear experiments coupled with passive acoustic (variance, kurtosis, and AE rate) interleaved with active source ultrasonic monitoring (transmitted wave amplitude) to develop physics-informed neural network (PINN) models incorporating the RSF law and AE rate generation equation with wave amplitude serving as

SUBMITTER: Borate P 

PROVIDER: S-EPMC11490533 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature