High-precision seismic data reconstruction with multi-domain sparsity constraints based on curvelet and high-resolution Radon transforms

Wang, Hanchuang; Tao, Chunhui; Chen, Sheng‐Chang; Wu, Ziyin; Du, Yong; Zhou, Jianping; Qiu, Lei; Shen, Huijuan; Xu, Wenyuan; Liu, Yunlong

doi:10.1016/j.jappgeo.2018.12.003

Cited by 9 publications

(3 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…FDCT is a multi-scale and multi-directional localized transform used for interpolation with projection on convex sets (POCS) [40]. FDCT is also used as sparsity promoting filter for noise attenuation in seismic data [33] [41] [42]. We assume that clean seismic data X has sparse representation d in FDCT domain C is represented as…”

Section: B Fast Discrete Curvelet Transformmentioning

confidence: 99%

Seismic Data Reconstruction Based on Double Sparsity Dictionary Learning With Structure Oriented Filtering

Kuruguntla,

Dodda,

Mandpura

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

In seismic data processing, denoising and reconstruction are the two steps for identification of resources in the earth subsurface layers'. The seismic data quality is affected by random noise and interference during acquisition. Further, the noisy data may be incomplete with missing traces. In this work, we propose a method for incomplete seismic data denoising and reconstruction based on double sparsity dictionary learning (DSDL) with structure oriented filtering (SOF). The main function of the DSDL step is denoising and SOF is used for residual noise attenuation and filling the missing data points. The proposed method is tested on 2D synthetic and field data sets. The test results show that the DSDL-SOF method has better noise attenuation and reconstruction in terms of signal-to-noise ratio (SNR) and mean squared error (MSE) as compared to existing methods.

show abstract

Section: B Fast Discrete Curvelet Transformmentioning

confidence: 99%

Seismic Data Reconstruction Based on Double Sparsity Dictionary Learning With Structure Oriented Filtering

Kuruguntla,

Dodda,

Mandpura

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

show abstract

“…However, in scenarios where data are missing, STFA cannot accurately distinguish effective signals from data-missing interferences because the spectrum interference caused by data loss also has sparse characteristics. Missing data is a common disturbance [30,31] mainly caused by propagation fading, sensor failure, impulsive noise removal etc. Missing samples in the time domain can cause missing terms in the instantaneous auto-correlation function (IAF) and lead to interference in the TFA.…”

Section: Introductionmentioning

confidence: 99%

Robust sparse time‐frequency analysis for data missing scenarios

Chen

Huang

Song

2023

IET Signal Processing

View full text Add to dashboard Cite

Sparse time-frequency analysis (STFA) can precisely achieve the spectrum of the local truncated signal. However, when the signal is disturbed by unexpected data loss, STFA cannot distinguish effective signals from missing data interferences. To address this issue and establish a robust STFA model for time-frequency analysis (TFA) in data loss scenarios, a stationary Framelet transform-based morphological component analysis is introduced in the STFA. In the proposed model, the processed signal is regarded as a sum of the cartoon, texture and data-missing parts. The cartoon and texture parts are reconstructed independently by taking advantage of the stationary Framelet transform. Then, the signal is reconstructed for STFA. The forward-backwards splitting method is employed to split the robust STFA model into the data recovery and robust timefrequency imaging stages. The two stages are then solved separately by using the alternating direction method of multipliers (ADMM). Finally, several experiments are conducted to show the performance of the proposed robust STFA method under different data loss levels, and it is compared with some existing state-of-the-art time-frequency methods. The results indicate that the proposed method outperforms the compared methods in obtaining the sparse spectrum of the effective signal when data are missing.The proposed method has a potential value in TFA in scenarios where data is easily lost.

show abstract

“…To solve this problem, numerous non-uniformly reconstruction algorithms have been raised. These algorithms can be classified into three categories: operators-based [4], prediction error filter (PEF)-based [5], transformation-based [6][7][8], and machine learning-based [9][10][11] reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Divide and Conquer Partition for Fourier Reconstruction Sparse Inversion with its Applications

2022

Teh. vjesn.

View full text Add to dashboard Cite

A partition method, with an efficient divide and conquer partition strategy, for the non-uniform sampling signal reconstruction based on Fourier reconstruction sparse inversion (FRSI) is developed. The novel partition FRSI(P-FRSI) is motivated by the observation that the partition processing of multi-dimensional signals can reduce the reconstruction difficulty and save the reconstruction time. Moreover, it is helpful to choose suitable reconstruction parameters. The P-FRSI employs divide and conquer strategy, and the signal is firstly partitioned into some blocks. Following that, traditional FRSI is applied to reconstruct signals in each block. We adopt linear or nonlinear superposition to determine the weight coefficients during integrating these blocks. Finally, P-FRSI is applied to two-dimensional seismic signal reconstruction. The superiority of the new method over conventional FRSI is demonstrated by numerical reconstruction experiments.

show abstract

High-precision seismic data reconstruction with multi-domain sparsity constraints based on curvelet and high-resolution Radon transforms

Cited by 9 publications

References 37 publications

Seismic Data Reconstruction Based on Double Sparsity Dictionary Learning With Structure Oriented Filtering

Seismic Data Reconstruction Based on Double Sparsity Dictionary Learning With Structure Oriented Filtering

Robust sparse time‐frequency analysis for data missing scenarios

Divide and Conquer Partition for Fourier Reconstruction Sparse Inversion with its Applications

Contact Info

Product

Resources

About