Predicting failure in solids has broad applications including earthquake prediction which
remains an unattainable goal. However, recent machine learning work shows that laboratory …

Earthquake forecasting and prediction have long and in some cases sordid histories but
recent work has rekindled interest based on advances in early warning, hazard assessment …

After decades of low but continuing activity, applications of machine learning (ML) in solid
Earth geoscience have exploded in popularity. This special collection provides a snapshot …

Data-driven machine-learning for predicting instantaneous and future fault-slip in laboratory
experiments has recently progressed markedly, primarily due to large training data sets. In …

Recently, Machine learning (ML) has been widely utilized for laboratory earthquake
(labquake) prediction using various types of data. This study pioneers in time to failure (TTF) …

We link changes in crustal permeability to informative features of microearthquakes (MEQs)
using two field hydraulic stimulation experiments where both MEQs and permeability …

Abstract Machine learning (ML) has been used to study the predictability of laboratory
earthquakes. However, the question remains whether or not this approach can be applied in …

Given the recent developments in machine-learning technology, its application has rapidly
progressed in various fields of earthquake seismology, achieving great success. Here, we …

During the RESOLVE project (“High‐resolution imaging in subsurface geophysics:
development of a multi‐instrument platform for interdisciplinary research”), continuous …

It has been recently demonstrated that Machine Learning (ML) can predict laboratory
earthquakes. Here we propose a prediction framework that allows forecasting future surface …