Deep-learning seismology

SM Mousavi, GC Beroza - Science, 2022‏ - science.org
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 …

Probabilistic inversion of seismic data for reservoir petrophysical characterization: Review and examples

D Grana, L Azevedo, L De Figueiredo, P Connolly… - Geophysics, 2022‏ - library.seg.org
The physics that describes the seismic response of an interval of saturated porous rocks with
known petrophysical properties is relatively well understood and includes rock physics …

Applications of deep neural networks in exploration seismology: A technical survey

SM Mousavi, GC Beroza, T Mukerji, M Rasht-Behesht - Geophysics, 2024‏ - library.seg.org
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 …

Sensing prior constraints in deep neural networks for solving exploration geophysical problems

X Wu, J Ma, X Si, Z Bi, J Yang, H Gao, D **e… - Proceedings of the …, 2023‏ - pnas.org
One of the key objectives in geophysics is to characterize the subsurface through the
process of analyzing and interpreting geophysical field data that are typically acquired at the …

[HTML][HTML] GAN-based generation of realistic 3D volumetric data: A systematic review and taxonomy

A Ferreira, J Li, KL Pomykala, J Kleesiek, V Alves… - Medical image …, 2024‏ - Elsevier
With the massive proliferation of data-driven algorithms, such as deep learning-based
approaches, the availability of high-quality data is of great interest. Volumetric data is very …

Imputation of missing well log data by random forest and its uncertainty analysis

R Feng, D Grana, N Balling - Computers & Geosciences, 2021‏ - Elsevier
Well logs are commonly used by geoscientists to infer and extrapolate physical properties of
subsurface rocks. However, at some depth intervals, well log values might be missing due to …

Regularized elastic full-waveform inversion using deep learning

Z Zhang, T Alkhalifah - Advances in subsurface data analytics, 2022‏ - Elsevier
Elastic full-waveform inversion, which aims to match the waveforms of prestack seismic data,
potentially provides more accurate high-resolution reservoir characterization from seismic …

Implicit seismic full waveform inversion with deep neural representation

J Sun, K Innanen, T Zhang… - Journal of Geophysical …, 2023‏ - Wiley Online Library
Full waveform inversion (FWI) is arguably the current state‐of‐the‐art amongst
methodologies for imaging subsurface structures and physical parameters with seismic data; …

Seismic facies segmentation via a segformer-based specific encoder–decoder–hypercolumns scheme

Z Wang, Q Wang, Y Yang, N Liu… - IEEE Transactions on …, 2023‏ - ieeexplore.ieee.org
Seismic facies classification plays an important role in oil and gas reservoir interpretation. In
the past few years, convolution neural network (CNN)-based models have been widely used …

Bayesian convolutional neural networks for seismic facies classification

R Feng, N Balling, D Grana… - … on Geoscience and …, 2021‏ - ieeexplore.ieee.org
The seismic response of geological reservoirs is a function of the elastic properties of porous
rocks, which depends on rock types, petrophysical features, and geological environments …