Deep learning for multidimensional seismic impedance inversion
X Wu, S Yan, Z Bi, S Zhang, H Si - Geophysics, 2021 - library.seg.org
Deep-learning (DL) methods have shown promising performance in predicting acoustic
impedance from seismic data that is typically considered as an ill-posed problem for …
impedance from seismic data that is typically considered as an ill-posed problem for …
Simultaneous source separation using a robust Radon transform
We adopted the robust Radon transform to eliminate erratic incoherent noise that arises in
common receiver gathers when simultaneous source data are acquired. The proposed …
common receiver gathers when simultaneous source data are acquired. The proposed …
Data and model dual-driven seismic deconvolution via error-constrained joint sparse representation
Y Wang, G Zhang, T Chen, Y Liu, B Shen, J Liang… - Geophysics, 2023 - library.seg.org
Deconvolution is an essential step in seismic data processing. Sparse-spike deconvolution
often is used to enhance the resolution of the seismic image by adding a model-driven …
often is used to enhance the resolution of the seismic image by adding a model-driven …
Seismic acoustic impedance inversion via optimization-inspired semisupervised deep learning
Seismic acoustic impedance inversion (SAII) aims at recovering the subsurface impedance
to achieve lithology interpretation. However, its ill-posedness and nonlinearity pose a great …
to achieve lithology interpretation. However, its ill-posedness and nonlinearity pose a great …
Optimization-inspired deep learning high-resolution inversion for seismic data
Seismic high-resolution processing plays a critical role in reservoir target detection. As one
of the most common approaches, regularization can achieve a high-resolution inversion …
of the most common approaches, regularization can achieve a high-resolution inversion …
Fast 3D blind seismic deconvolution via constrained total variation and GCV
When improving the Earth's description of seismic data via deconvolution, the spatial
coherency of the information that can be extracted may be damaged through suboptimal …
coherency of the information that can be extracted may be damaged through suboptimal …
The high‐resolution seismic deconvolution method based on joint sparse representation using logging–seismic data
Y Wang, G Zhang, H Li, W Yang, W Wang - Geophysical Prospecting, 2022 - earthdoc.org
Seismic high‐resolution processing is an essential part of seismic processing. Sparse‐spike
deconvolution is a widely used method for improving the resolution of seismic data …
deconvolution is a widely used method for improving the resolution of seismic data …
A fast automatic multichannel blind seismic inversion for high-resolution impedance recovery
A Gholami - Geophysics, 2016 - library.seg.org
The inversion of seismic reflection data for acoustic impedance (AI) is a common and
accepted method for the interpretation of poststack seismic data. The original mathematical …
accepted method for the interpretation of poststack seismic data. The original mathematical …
Improving the generalization of deep neural networks in seismic resolution enhancement
Seismic resolution enhancement is a key step for subsurface structure characterization.
Although many have proposed the use of deep learning (DL) for resolution enhancement …
Although many have proposed the use of deep learning (DL) for resolution enhancement …