There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …

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 …

There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …

Physics-informed machine learning (PIML), referring to the combination of prior knowledge
of physics, which is the high level abstraction of natural phenomenons and human …

The challenging task of automatic seismic fault detection recently gained in quality with the
emergence of deep learning techniques. Those methods successfully take advantage of a …

Deep learning (DL) is a powerful tool for mining features from data, which can theoretically
avoid assumptions (eg, linear events) constraining conventional interpolation methods …

The advent of technology including big data has allowed machine learning technology to
strengthen its place in solving different science and engineering complex problems …

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 …

Segmentation of faults based on seismic images is an important step in reservoir
characterization. With the recent developments of deep-learning methods and the …

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 …