Seismic data interpolation using deep learning with generative adversarial networks

H Kaur, N Pham, S Fomel - Geophysical Prospecting, 2021 - earthdoc.org
We propose an algorithm for seismic trace interpolation using generative adversarial
networks, a type of deep neural network. The method extracts feature vectors from the …

Adaptive damped rank-reduction method for random noise attenuation of three-dimensional seismic data

YASI Oboué, W Chen, OM Saad, Y Chen - Surveys in Geophysics, 2023 - Springer
Rank-reduction methods are effective for separating random noise from the useful seismic
signal based on the truncated singular value decomposition (TSVD). However, the results …

Random noise attenuation based on residual convolutional neural network in seismic datasets

L Yang, W Chen, W Liu, B Zha, L Zhu - Ieee Access, 2020 - ieeexplore.ieee.org
Seismic random noise attenuation is a key step in seismic data processing. The random
seismic data recorded by the detector tends to have strong noise, and this noisy seismic …

Random noise attenuation in seismic data using Hankel sparse low-rank approximation

R Anvari, AR Kahoo, MS Monfared… - Computers & …, 2021 - Elsevier
The Hankel matrix's low-rank property derived from the noise-free seismic data describing a
few linear events and has been successively leveraged in many low-rank seismic data de …

Robust damped rank-reduction method for simultaneous denoising and reconstruction of 5D seismic data

YAS Innocent Oboué, W Chen, H Wang, Y Chen - Geophysics, 2021 - library.seg.org
We have developed a new method for simultaneous denoising and reconstruction of 5D
seismic data corrupted by random noise and missing traces. Several algorithms have been …

Low-Rank Approximation Reconstruction of Five-Dimensional Seismic Data

G Chen, Y Liu, M Zhang, Y Sun, H Zhang - Surveys in Geophysics, 2024 - Springer
Low-rank approximation has emerged as a promising technique for recovering five-
dimensional (5D) seismic data, yet the quest for higher accuracy and stronger rank …

Nonlocal weighted robust principal component analysis for seismic noise attenuation

X Liu, X Chen, J Li, Y Chen - IEEE Transactions on Geoscience …, 2020 - ieeexplore.ieee.org
Seismic data are usually contaminated by various noises. Noise suppression plays an
important role in seismic processing. In this article, we propose a new denoising method …

Simultaneous seismic data interpolation and denoising based on nonsubsampled contourlet transform integrating with two-step iterative log thresholding algorithm

C Li, X Wen, X Liu, S Zu - IEEE Transactions on Geoscience …, 2022 - ieeexplore.ieee.org
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 …

DRR: An open-source multi-platform package for the damped rank-reduction method and its applications in seismology

Y Chen, W Huang, L Yang, YASI Oboué… - Computers & …, 2023 - Elsevier
We present an open-source multi-platform package for the damped rank-reduction (DRR)
method, that has been widely used in various problems in seismology. The DRR method is …

Seismic data interpolation based on denoising diffusion implicit models with resampling

X Wei, C Zhang, H Wang, C Tan, D Xiong… - arXiv preprint arXiv …, 2023 - arxiv.org
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 …