This study offered a detailed review of data sciences and machine learning (ML) roles in
different petroleum engineering and geosciences segments such as petroleum exploration …

This review gives an overview of the development of machine learning in geoscience. A
thorough analysis of the codevelopments of machine learning applications throughout the …

An inversion algorithm is commonly used to estimate the elastic properties, such as P-wave
velocity (VP), S-wave velocity (VS), and density (ρ) of the earth's subsurface. Generally, the …

Salt boundary interpretation is important for the understanding of salt tectonics and velocity
model building for seismic migration. Conventional methods consist of computing salt …

Mapping of seismic and lithologic facies from 3D reflection seismic data plays a key role in
depositional environment analysis and reservoir characterization during hydrocarbon …

As a process that identifies geologic structures of interest such as faults, salt domes, or
elements of petroleum systems in general, seismic structural interpretation depends heavily …

Deep learning is widely used for seismic impedance inversion, but few work provides in-
depth research and analysis on designing the architectures of deep neural networks and …

Depicting geologic sequences from 3D seismic surveying is of significant value to
subsurface reservoir exploration, but it is usually time-and labor-intensive for manual …

Paleokarst systems are found extensively in carbonate‐prone basins worldwide. They can
form large reservoirs and provide efficient pathways for hydrocarbon migration, but they can …

Seismic channel interpretation involves detecting channel structures, which often appear as
meandering shapes in 3D seismic images. Many conventional methods are proposed for …