Seismic data interpolation using deep learning with generative adversarial networks
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 …
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
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 …
signal based on the truncated singular value decomposition (TSVD). However, the results …
Random noise attenuation based on residual convolutional neural network in seismic datasets
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 …
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
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 …
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
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 …
seismic data corrupted by random noise and missing traces. Several algorithms have been …
Low-Rank Approximation Reconstruction of Five-Dimensional Seismic Data
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 …
dimensional (5D) seismic data, yet the quest for higher accuracy and stronger rank …
Nonlocal weighted robust principal component analysis for seismic noise attenuation
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 …
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
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 …
DRR: An open-source multi-platform package for the damped rank-reduction method and its applications in seismology
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 …
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
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 …