Seismic waves from earthquakes and other sources are used to infer the structure and
properties of Earth's interior. The availability of large-scale seismic datasets and the …

Recently deep learning (DL), as a new data‐driven technique compared to conventional
approaches, has attracted increasing attention in geophysical community, resulting in many …

We propose a new approach to the solution of the wave propagation and full waveform
inversions (FWIs) based on a recent advance in deep learning called physics‐informed …

As natural disasters are induced by geodynamic activities or abnormal changes in the
environment, geological hazards tend to wreak havoc on the environment and human …

Seismology is a data rich and data-driven science. Application of machine learning for
gaining new insights from seismic data is a rapidly evolving sub-field of seismology. The …

In this study, we present a fast and reliable method for end‐to‐end estimation of earthquake
magnitude from raw waveforms recorded at single stations. We design a regressor (MagNet) …

Microseismic monitoring during mining operations generates spatiotemporal data that could
indicate strata fractures and deformations leading to water inrush anomalies. However …

The need for an earthquake early-warning system (EEWS) is unavoidable to save lives. In
terms of managing earthquake disasters and achieving effective risk mitigation, the quick …

One of the key objectives in geophysics is to characterize the subsurface through the
process of analyzing and interpreting geophysical field data that are typically acquired at the …

The confluence of our ability to handle big data, significant increases in instrumentation
density and quality, and rapid advances in machine learning (ML) algorithms have placed …