Unsupervised deep learning for 3D interpolation of highly incomplete data
We propose to denoise and reconstruct the 3D seismic data simultaneously using an
unsupervised deep learning (DL) framework, which does not require any prior information …
unsupervised deep learning (DL) framework, which does not require any prior information …
Fast dictionary learning for high-dimensional seismic reconstruction
A sparse dictionary is more adaptive than a sparse fixed-basis transform since it can learn
the features directly from the input data in a data-driven way. However, learning a sparse …
the features directly from the input data in a data-driven way. However, learning a sparse …
Intelligent missing shots' reconstruction using the spatial reciprocity of Green's function based on deep learning
B Wang, N Zhang, W Lu, J Geng… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
The trace interval in the common shot and receiver gathers is always inconsistent. The
inconsistency affects the final performance of seismic data processing, and the …
inconsistency affects the final performance of seismic data processing, and the …
Reconstruction of irregular missing seismic data using conditional generative adversarial networks
Q Wei, X Li, M Song - Geophysics, 2021 - library.seg.org
During acquisition, due to economic and natural reasons, irregular missing seismic data are
always observed. To improve accuracy in subsequent processing, the missing data should …
always observed. To improve accuracy in subsequent processing, the missing data should …
Erratic noise suppression using iterative structure‐oriented space‐varying median filtering with sparsity constraint
Erratic noise often has high amplitudes and a non‐Gaussian distribution. Least‐squares–
based approaches therefore are not optimal. This can be handled better with non–least …
based approaches therefore are not optimal. This can be handled better with non–least …
Irregularly sampled seismic data interpolation via wavelet-based convolutional block attention deep learning
Seismic data interpolation, especially irregularly sampled data interpolation, is a critical task
for seismic processing and subsequent interpretation. Recently, with the development of …
for seismic processing and subsequent interpretation. Recently, with the development of …
Simultaneous seismic data interpolation and denoising based on nonsubsampled contourlet transform integrating with two-step iterative log thresholding algorithm
Seismic data interpolation and denoising play vital roles in obtaining complete and clean
data in seismic data processing. Seismic data usually miss along various spatial axes and …
data in seismic data processing. Seismic data usually miss along various spatial axes and …
Reconstructing regularly missing seismic traces with a classifier-guided diffusion model
Reconstructing missing seismic data is crucial for seismic processing and interpretation.
Recent methods struggle when seismic traces are regularly missing, such as near-offset …
Recent methods struggle when seismic traces are regularly missing, such as near-offset …
Sparse prior-net: A sparse prior-based deep network for seismic data interpolation
M Wu, L Fu, W Fang, J Cao - Geophysics, 2024 - library.seg.org
Seismic data interpolation plays a crucial role in obtaining dense and regularly sampled
data, contributing to improving the quality of seismic data in seismic exploration. Sparsity …
data, contributing to improving the quality of seismic data in seismic exploration. Sparsity …
Seismic data interpolation based on denoising diffusion implicit models with resampling
The incompleteness of the seismic data caused by missing traces along the spatial
extension is a common issue in seismic acquisition due to the existence of obstacles and …
extension is a common issue in seismic acquisition due to the existence of obstacles and …