Seismic waves from earthquakes and other sources are used to infer the structure and
properties of Earth's interior. The availability of large-scale seismic datasets and the …

Automated seismic fault interpretation has been an active area of research. Since 2018,
Deep learning (DL) based seismic fault interpretation methods have emerged and shown …

Exploration seismology uses reflected and refracted seismic waves, emitted from a
controlled (active) source into the ground, and recorded by an array of seismic sensors …

Missing trace reconstruction is an essential step in the seismic data processing. Various
interpolation methods have been proposed for handling this issue. In recent years, deep …

Delineating seismic faults is one of the main steps in seismic structure interpretation.
Recently, deep learning (DL) models are used to automatic seismic fault interpretation. For …

Seismic impedance inversion is essential to characterize hydrocarbon reservoir and detect
fluids in field of geophysics. However, it is nonlinear and ill-posed due to unknown seismic …

Fault detection is a fundamental and important research topic in automatic seismic
interpretation since the geometry of faults usually reveals the accumulation and migration of …

Fault identification has important geological significance and practical production value. Due
to the effects of earth filtering and environmental noise, it is difficult to identify minor faults …

Due to the constraints of natural environments, acquired prestack seismic data is usually not
complete, which seriously affects subsequent seismic data processing. With the progress of …

Seismic fault structures are important for the detection and exploitation of hydrocarbon
resources. Due to their development and popularity in the geophysical community, deep …