The accumulation of large datasets and increasing data availability have led to the
emergence of data-driven paleontological studies, which reveal an unprecedented picture of …

Exploration seismology uses reflected and refracted seismic waves, emitted from a
controlled (active) source into the ground, and recorded by an array of seismic sensors …

Seismology is witnessing explosive growth in the diversity and scale of earthquake catalogs.
A key motivation for this community effort is that more data should translate into better …

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) …

Microseismic event identification is of great significance for enhancing our understanding of
underground phenomena and ensuring geological safety. This paper employs a literature …

Optimized deep learning (DL)-based workflows can improve the efficiency and accuracy of
earthquake detection and location processes. This article introduces a six-step automated …

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 …

We develop an algorithm to detect and locate sources of long‐period (25–100 s) seismic
signals. Our method is based on the analysis of seismological data recorded at global …

Fracture flow is the fluid movement in a fracture or a fracture zone. Since fracture flow can
induce long-duration (LD) microseismic events, classifying different types of microseismicity …

Effective response strategies to earthquake disasters are crucial for disaster management in
smart cities. However, in regions where earthquakes do not occur frequently, model …