Source locations provide fundamental information on earthquakes and lay the foundation for
seismic monitoring at all scales. Seismic source location as a classical inverse problem has …

The discipline of seismology is based on observations of ground motion that are inherently
undersampled in space and time. Our basic understanding of earthquake processes and our …

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

Earthquake prediction, the long-sought holy grail of earthquake science, continues to
confound Earth scientists. Could we make advances by crowdsourcing, drawing from the …

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 …

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 …

We discuss data of three laboratory stick‐slip experiments on Westerly Granite samples
performed at elevated confining pressure and constant displacement rate on rough fracture …

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) …

Foreshocks have been documented as preceding less than half of all mainshock
earthquakes. These observations are difficult to reconcile with laboratory earthquake …

Abstract Machine learning models using seismic emissions as input can predict
instantaneous fault characteristics such as displacement and friction in laboratory …