Neural networks are commonly used for poststack and prestack seismic inversion. With
sufficient labeled data, the neural network-based seismic inversion results are more …

Coherent and incoherent noise in seismic data inevitably reduces the quality of subsequent
processing, eg, migration and inversion. Different from random noise, erratic noise follows …

Ground roll is a type of coherent noise with low frequency, low velocity, and high amplitude,
which masks useful signals and decreases the quality of subsequent seismic data …

Prestack seismic inversion is an effective approach to obtain elastic parameters for reservoir
characterization in seismic exploration. However, the difficulty in achieving reliable inversion …

The frequency-dependent amplitude-versus-offset (FAVO) method has great potential for
reservoir parameters estimation; however, it is hard work to establish the FAVO inversion …

Prestack seismic data amplitude variation with angle (AVA) inversion is critical in identifying
oil and gas reservoirs. Recently, deep learning (DL) has gained significant popularity in AVA …

In seismic inversion, supervised learning is highly effective when abundant paired data are
available, yet such conditions are often not met in this domain due to the scarcity of well …

Deep learning has shown excellent performance in simulating complex nonlinear map**s
from the seismic data to elastic parameters. However, seismic acoustic impedance …

In elastic wave inverse scattering problems, the material-property reconstruction, eg,
distributions of mass density, compressional wave speed, and shear wave speed from a …

Pre-stack seismic inversion is an effective way to investigate the characteristics of
hydrocarbon-bearing reservoirs. Multi-parameter application is the key to identifying …