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 …
Seismic velocity inversion transformer
Velocity model inversion is one of the most challenging tasks in seismic exploration, and an
accurate velocity model is essential for high-resolution seismic imaging. Recently, velocity …
accurate velocity model is essential for high-resolution seismic imaging. Recently, velocity …
Acoustic impedance inversion from seismic imaging profiles using self attention U-Net
Seismic impedance inversion is a vital way of geological interpretation and reservoir
investigation from a geophysical perspective. However, it is inevitably an ill-posed problem …
investigation from a geophysical perspective. However, it is inevitably an ill-posed problem …
Deblending and recovery of incomplete blended data via MultiResUnet
B Wang, J Li, D Han, J Song - Surveys in Geophysics, 2022 - Springer
Blended acquisition is still open to improve the efficiency of seismic data acquisition.
Deblending is an essential procedure to provide separated gathers for subsequent …
Deblending is an essential procedure to provide separated gathers for subsequent …
Multi-Task Full Attention U-Net for Prestack Seismic Inversion
Deep learning (DL) has been widely used in seismic inversion. Since the label data
obtained in production are actually a small amount of 1-D well-log data, most DL-based …
obtained in production are actually a small amount of 1-D well-log data, most DL-based …
Seismic AVO inversion method for viscoelastic media based on a tandem invertible neural network model
Seismic amplitude variation with offset (AVO) inversion provides elastic parameters for
reservoir identification. When processing field seismic data, conventional elastic medium …
reservoir identification. When processing field seismic data, conventional elastic medium …
Semi-supervised learning seismic inversion based on spatio-temporal sequence residual modeling neural network
The Spatio-temporal sequence residual modeling neural network (STSRM-net) is built to
address the geophysical problem of obtaining P-impedance of the subsurface from the zero …
address the geophysical problem of obtaining P-impedance of the subsurface from the zero …
Disentangling noise patterns from seismic images: Noise reduction and style transfer
Seismic interpretation is a fundamental approach for obtaining static and dynamic
information about subsurface reservoirs, such as geological faults/salt bodies and …
information about subsurface reservoirs, such as geological faults/salt bodies and …
CNN-based network application for petrophysical parameter inversion: Sensitivity analysis of input–output parameters and network architecture
H Li, B Qiu, Y Zhang, B Wu, Y Wang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Accurate estimation of petrophysical properties (eg, porosity, clay volume) of subsurface
rock from seismic data/elastic properties is significant to reservoir characterization …
rock from seismic data/elastic properties is significant to reservoir characterization …
Deep learning seismic inversion based on prestack waveform datasets
J Zhang, H Sun, G Zhang, X Zhao - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Prediction of elastic parameters (eg, P-, S-wave velocity, and density) from observed seismic
data is one of the most common means of reservoir characterization. Recently, deep …
data is one of the most common means of reservoir characterization. Recently, deep …