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 …

Within seismology, geology, civil and structural engineering, deep learning (DL), especially
via generative adversarial networks (GANs), represents an innovative, engaging, and …

The rapid growth of deep learning research, including within the field of computational
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …

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 …

Seismic inversion is a fundamental tool in geophysical analysis, providing a window into
Earth. In particular, it enables the reconstruction of large-scale subsurface Earth models for …

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 …

Velocity model inversion is one of the most challenging tasks in seismic exploration, and an
accurate velocity model is essential for high-resolution seismic imaging. Recently, velocity …

Solving the wave equation is one of the most (if not the most) fundamental problems we face
as we try to illuminate the Earth using recorded seismic data. The Helmholtz equation …

Building realistic and reliable models of the subsurface is the primary goal of seismic
imaging. We have constructed an ensemble of convolutional neural networks (CNNs) to …

Among the biggest challenges we face in utilizing neural networks trained on waveform (ie,
seismic, electromagnetic, or ultrasound) data is its application to real data. The requirement …