Recently deep learning (DL), as a new data‐driven technique compared to conventional
approaches, has attracted increasing attention in geophysical community, resulting in many …

Constraining geophysical models with observed data usually involves solving nonlinear and
nonunique inverse problems. Neural mixture density networks (MDNs) provide an efficient …

Permeability is a critical parameter for evaluating reservoir properties, and accurate
prediction is an important basis for identifying high-quality reservoirs and geological …

The exploration and exploitation of shale oil is an important aspect in the oil industry.
Seismic properties and well‐log data are essential to establish wave‐propagation models …

Numerous efforts have been made to determine the five independent elastic constants of
transversely isotropic (TI) rocks. Recently, the novel strip load test method combined with the …

Seismic anisotropy reveals that seismic wave velocity, amplitude, and other physical
properties show variations in different directions, which can be divided into lattice preferred …

The deep-learning method has been successfully applied to many geophysical problems to
extract features from seismic big data. However, some applications may not have sufficient …

Reservoir parameter inversion from seismic data is an important issue in rock physics. The
traditional optimisation-based inversion method requires high computational expense, and …

When the elastic parameters of rocks such as shales vary with the propagation direction of a
passing seismic wave that rock is called anisotropic. Modern seismic data are typically …

Stratigraphy in the crust is widely anisotropic. Anisotropic parameters play an important role
from inversion and migration to stratigraphic interpretation and reservoir characterization. At …