Earthquake forecasting poses significant challenges, especially due to the elusive nature of
stress states in fault systems. To tackle this problem, we use features derived from seismic …

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 …

Rocks under stress deform by creep mechanisms that include formation and slip on small‐
scale internal cracks. Intragranular cracks and slip along grain contacts release energy as …

When a rock is subjected to stress it deforms by creep mechanisms that include formation
and slip on small-scale internal cracks. Intragranular cracks and slip along grain contacts …

Abstract Machine learning (ML) techniques have become increasingly important in
seismology and earthquake science. Lab‐based studies have used acoustic emission data …

Seismicity clusters within fault zones can be connected to the structure, geometric
complexity and size of asperities which perturb and intensify the stress field in their …

Recent work shows that machine learning (ML) can predict failure time and other aspects of
laboratory earthquakes using the acoustic signal emanating from the fault zone. These …

We analyzed the earthquake density of the Geysers geothermal field (California) as a
function of time and space over a decade. We grouped parts of the volume of the geothermal …

The earthquake rupture process comprises complex interactions of stress, fracture, and
frictional properties. New machine learning methods demonstrate great potential to reveal …

To investigate possible indicators of critical point behavior prior to rock failure, the statistical
properties of pre-failure damage were analyzed based on acoustic emission events (AE) …