Low-frequency (LF) signal content in seismic data as well as a realistic initial model are key
ingredients for robust and efficient full-waveform inversions (FWIs). However, acquiring LF …

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 …

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 …

We have developed an artificial neural network to estimate P-wave velocity models directly
from prestack common-source gathers. Our network is composed of a fully connected layer …

Exploration seismic data are heavily manipulated before human interpreters are able to
extract meaningful information regarding subsurface structures. This manipulation adds …

Depth imaging projects dedicated to hydrocarbon exploration or field development rely
heavily on velocity model building. When salt bodies are present, their accurate delineation …

During the 13th Five-Year Plan period, PetroChina has kept up with the development trend
of advanced artificial intelligence and major needs in the fi eld of geophysical prospecting …

This chapter presents a novel convolutional neural network (CNN)-based approach to
seismic tomography, which is widely used in velocity model building (VMB). VMB is a key …

Velocity model building is not straightforward in geologically complex environments. We
train a convolutional neural network (CNN) to map full wavefields to smooth subsurface …

Seismic full-waveform inversion (FWI) is a nonlinear computational imaging technique that
can provide detailed estimates of subsurface geophysical properties. Solving the FWI …