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 …

Recent work in machine learning shows that deep neural networks can be used to solve a
wide variety of inverse problems arising in computational imaging. We explore the central …

Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …

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

Seismic velocity is one of the most important parameters used in seismic exploration.
Accurate velocity models are the key prerequisites for reverse time migration and other high …

This article provides an overview of current applications of machine learning (ML) in
seismology. ML techniques are becoming increasingly widespread in seismology, with …

We have addressed the geophysical problem of obtaining an elastic model of the
subsurface from recorded normal-incidence seismic data using convolutional neural …

We propose a new method to tackle the mapping challenge from time-series data to spatial
image in the field of seismic exploration, ie, reconstructing the velocity model directly from …