The goal of seismic inversion is to obtain subsurface properties from surface measurements.
Seismic images have proven valuable, even crucial, for a variety of applications, including …

Full waveform inversion (FWI) infers the subsurface structure information from seismic
waveform data by solving a non-convex optimization problem. Data-driven FWI has been …

We review five types of physics-informed machine-learning (PIML) algorithms for inversion
and modeling of geophysical data. Such algorithms use the combination of a data-driven …

Full waveform inversion (FWI) is widely used in geophysics to reconstruct high-resolution
velocity maps from seismic data. The recent success of data-driven FWI methods results in a …

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 …

In the study of subsurface seismic imaging, solving the acoustic wave equation is a pivotal
component in existing models. The advancement of deep learning (DL) enables solving …

Bayesian inference for high-dimensional inverse problems is computationally costly and
requires selecting a suitable prior distribution. Amortized variational inference addresses …

We introduce a probabilistic technique for full-waveform inversion, using variational
inference and conditional normalizing flows to quantify uncertainty in migration-velocity …

Ultrasound computed tomography (USCT) is an emerging imaging modality that holds great
promise for breast imaging. Full-waveform inversion (FWI)-based image reconstruction …

This paper investigates the impact of big data on deep learning models to help solve the full
waveform inversion (FWI) problem. While it is well known that big data can boost the …