Full-waveform inversion (FWI) can be made immune to cycle skip** by matching the
recorded data with traveltime errors smaller than one-half period from inaccurate subsurface …

Full-waveform inversion (FWI) is a powerful method for providing a high-resolution
description of the subsurface. However, the misfit function of the conventional FWI method …

Optimal transport distance is an appealing tool to measure the discrepancy between
datasets in the frame of inverse problems, for its ability to perform global comparisons and its …

In seismological full waveform inversion, the choice of misfit functions plays a critical role in
quantifying the discrepancy between observed and synthetic data, affecting convergence …

Full-waveform inversion, a high-resolution seismic imaging method, is known to require
sufficiently accurate initial models to converge toward meaningful estimations of the …

To effectively overcome the cycle-skip** issue in full-waveform inversion (FWI), we have
developed a deep neural network (DNN) approach to predict the absent low-frequency (LF) …

Seismic data are a significant facilitator for monitoring in carbon capture and sequestration
projects, providing high-resolution images of fluid migration, using, for example, full …

Conventional methods solve the full‐waveform inversion making use of gradient‐based
algorithms to minimize an error function, which commonly measure the Euclidean distance …

Full-waveform inversion (FWI) promises a high-resolution model of the earth. It is, however,
a highly nonlinear inverse problem; thus, we iteratively update the subsurface model by …

We describe a new method, full-waveform inversion by model extension (FWIME), that
recovers accurate acoustic subsurface velocity models from seismic data when conventional …