SaltSeg: Automatic 3D salt segmentation using a deep convolutional neural network
Salt boundary interpretation is important for the understanding of salt tectonics and velocity
model building for seismic migration. Conventional methods consist of computing salt …
model building for seismic migration. Conventional methods consist of computing salt …
A comprehensive review of seismic inversion based on neural networks
M Li, XS Yan, M Zhang - Earth Science Informatics, 2023 - Springer
Seismic inversion is one of the fundamental techniques for solving geophysics problems. To
obtain the elastic parameters or petrophysical parameters, it is necessary to establish a …
obtain the elastic parameters or petrophysical parameters, it is necessary to establish a …
Deep learning for low-frequency extrapolation from multioffset seismic data
Low-frequency seismic data are crucial for convergence of full-waveform inversion (FWI) to
reliable subsurface properties. However, it is challenging to acquire field data with an …
reliable subsurface properties. However, it is challenging to acquire field data with an …
Seismic full-waveform inversion using deep learning tools and techniques
A Richardson - ar** full seismic waveforms to vertical velocity profiles by deep learning
Building realistic and reliable models of the subsurface is the primary goal of seismic
imaging. We have constructed an ensemble of convolutional neural networks (CNNs) to …
imaging. We have constructed an ensemble of convolutional neural networks (CNNs) to …
Applying machine learning to 3D seismic image denoising and enhancement
E Wang, J Nealon - Interpretation, 2019 - library.seg.org
We have trained a supervised deep 3D convolutional neural network (CNN) on marine
seismic images for poststack structural seismic image enhancement and noise attenuation …
seismic images for poststack structural seismic image enhancement and noise attenuation …
Hierarchical transfer learning for deep learning velocity model building
Deep learning is a promising approach to velocity model building because it has the
potential of processing large seismic surveys with minimal resources. By leveraging large …
potential of processing large seismic surveys with minimal resources. By leveraging large …
Geologic model building in SEAM Phase II—Land seismic challenges
C Regone, J Stefani, P Wang, C Gerea… - The Leading …, 2017 - library.seg.org
Three digital earth models were designed and constructed during SEAM Phase II to study
exploration challenges at the scale of modern land seismic surveys. Although built as …
exploration challenges at the scale of modern land seismic surveys. Although built as …
A deep transfer learning framework for seismic data analysis: A case study on bright spot detection
Bright spots, strong indicators of the existence of hydrocarbon accumulations, have been
primarily used by geophysicists in oil and gas exploration. Recently, machine-learning …
primarily used by geophysicists in oil and gas exploration. Recently, machine-learning …