Prestack Seismic Data Reconstruction Method Based Undecimated Discrete Wavelet Transform

Guo, Na; Chen, M.; Cui, Yu

doi:10.3997/2214-4609.201413120

Cited by 2 publications

(2 citation statements)

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“…Guo et al. (2014) used the undecimated discrete wavelet transform (UDWT) to sparse represent the data and reconstruct them based on the compressive sensing theory, and the results confirmed that the UDWT‐based seismic data reconstruction method can effectively recover the missing information of non‐regular seismic traces and also has some robustness to background noise. Different wavelet functions have different sparse representation ability and reconstruction effect for the data.…”

Section: Introductionmentioning

confidence: 93%

Seismic data reconstruction using Bregman iterative algorithm based on compressed sensing and discrete orthonormal wavelet transform

Wang

Zhang

2022

Geophysical Prospecting

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Due to the limitations of the actual acquisition environment in the field, especially in ocean bottom seismometer (OBS) acquisition, the acquired seismic data are often irregular and incomplete, which affects the subsequent data imaging, interpretation and hydrocarbon‐bearing reservoir prediction. The interpolation reconstruction algorithm based on the compressive sensing theory can reconstruct the data without the limitation of Nyquist sampling interval. However, the reconstruction accuracy and effect are different for different sparse representations of the data. On the basis of compressed sensing theory, we propose a Bregman iterative seismic data reconstruction method based on the sparse decomposition of discrete orthonormal Coiflets and Symlets wavelet transforms. First, the discrete orthonormal matrix is constructed by using the above two wavelet functions to make the original seismic data sparse, then the Bregman iterative algorithm is used to reconstruct the sparse coefficients in the discrete wavelet domain, and finally the recovery matrix is used to reconstruct the seismic data. The discrete orthonormal Coiflets and Symlets wavelet transforms have good sparse representation ability and can compensate for the problem that discrete Fourier transform cannot well sparse representation of the data. After numerical experiments on horizontal‐layered model, Marmousi2 model and actual data, it is verified that the proposed method can reconstruct the missing seismic data under the regular observation system and under the irregular observation system with non‐uniform OBS distribution. Furthermore, this method can hardly bring the interference of random noise, and the reconstruction result is of high accuracy.

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Section: Introductionmentioning

confidence: 93%

Seismic data reconstruction using Bregman iterative algorithm based on compressed sensing and discrete orthonormal wavelet transform

Wang

Zhang

2022

Geophysical Prospecting

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“…From these coefficients, the original seismic signal is recovered using a thresholding operator. A variety of sparse transforms with predefined basis such as Fourier [17], Wavelet [18], Curvelet [19], Radon [20], Shearlet [21], Seislet [22] and Dreamlet [23] have been studied for reconstruction of seismic data. Seislet and dreamlet are two types of fixed sparse bases commonly used in seismic data processing, specifically for denoising and interpolation tasks.…”

Section: Introductionmentioning

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

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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.

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Prestack Seismic Data Reconstruction Method Based Undecimated Discrete Wavelet Transform

Cited by 2 publications

References 0 publications

Seismic data reconstruction using Bregman iterative algorithm based on compressed sensing and discrete orthonormal wavelet transform

Seismic data reconstruction using Bregman iterative algorithm based on compressed sensing and discrete orthonormal wavelet transform

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

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