BACKGROUND The solid Earth, oceans, and atmosphere together form a complex
interacting geosystem. Processes relevant to understanding Earth's geosystem behavior …

Machine learning (ML) and artificial intelligence (AI) are rising stars in many scientific
disciplines and industries, and high hopes are being pinned upon them. Likewise, ML and …

This review gives an overview of the development of machine learning in geoscience. A
thorough analysis of the codevelopments of machine learning applications throughout the …

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

An evidently precursory acoustic emission (AE) time series, characterized by a time‐
reversed Omori law, is registered prior to the unconfined catastrophic rupture of brittle …

Tectonic faults fail in a spectrum of modes, ranging from earthquakes to slow slip events.
The physics of fast earthquakes are well described by stick–slip friction and elastodynamic …

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 …

Tectonic faults slip in various manners, which range from ordinary earthquakes to slow slip
events to aseismic fault creep. Slow slip and associated tremor are common to many …

Despite the growing spatiotemporal density of geophysical observations at subduction
zones, predicting the timing and size of future earthquakes remains a challenge. Here we …

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 …