Experimental analysis and application of sparsity constrained deconvolution

Li, Guofa; Di, Qianqian; Peng, Gang; Yue, Ying; Zhai, Tongli

doi:10.1007/s11770-013-0377-1

Cited by 17 publications

(3 citation statements)

References 12 publications

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“…Most efforts focus on enhancing seismic resolution by bandwidth enhancement methods. Because of the limitation imposed by the effective bandwidth of seismic signals, it is almost impossible for traditional deconvolution methods to enhance seismic resolution sufficiently to resolve each thin bed separately (Li et al, 2013). Wiggins (1978), Sacchi (1997), Velis (2008), and Chai et al (2014) attempt to overcome this limit and restore reflectivity spikes by introducing an assumption of sparse reflectivity.…”

Section: Introductionmentioning

confidence: 99%

Characterization of interbedded thin beds using zero-crossing-time amplitude stratal slices

Sacchi

Wang

et al. 2015

GEOPHYSICS

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We have developed a novel method to characterize thin beds using zero-crossing-time (ZCT) amplitude slices based on an interesting phenomenon we have noticed. Because of the reflection interferences and wavelet overlap, it is almost impossible to identify each thin bed from the composite response of interbedded thin beds. However, the seismic response of one thin bed does not always interfere with other thin beds at all times. At its ZCTs, a thin bed's reflection makes no contribution to the composite response. Therefore, unlike standard amplitude stratal slices, the imprint of a thin bed does not appear in its ZCT amplitude stratal slices. The latter makes the interference pattern of ZCT amplitude stratal slices different from those of other amplitude stratal slices. Based on this difference, the ZCT amplitude stratal slices of each thin bed can be identified from consecutive amplitude slices, and the thickness, depth, and lateral distribution of each thin bed, as well as the vertical spacing between them, can be estimated from their ZCTs. This interpretive method has been systematically tested with synthetic 3D data and has also been applied to field seismic data.

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Section: Introductionmentioning

confidence: 99%

Characterization of interbedded thin beds using zero-crossing-time amplitude stratal slices

Sacchi

Wang

et al. 2015

GEOPHYSICS

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“…This should also be evident as a continuous trait. Many scholars have conducted extensive research in this area (Heimer and Cohen, 2009;Gholami and Sacchi, 2012;Gholami and Sacchi, 2013;Li et al, 2013;Yuan et al, 2016;Du et al, 2018;Ma et al, 2020). The focus of these studies has mainly been on the spatial continuity of seismic data.…”

mentioning

confidence: 99%

A novel high-resolution imaging method based on sparsity in the time domain and spectral fitting in the frequency domain

Guo,

Gao,

Yong

et al. 2024

Front. Earth Sci.

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During the propagation of seismic waves underground, the high-frequency seismic response of thin reservoir is absorbed and attenuated, which poses a challenge in seismic thin reservoir prediction. The high-resolution processing techniques have the capability to significantly expand the frequency range of the seismic data, so it becomes a key technique for thin reservoir prediction. Most of these techniques necessitate the extraction of seismic wavelets. However, the spatial and temporal variations of seismic data result in multiple solutions for wavelet extraction. Simultaneously, the majority of techniques fail to consider the influence of spatial tectonic features on the high-resolution processing. In this paper, we propose a novel solution to address these two fundamental challenges by utilizing seismic spectral expansion, sparse reflection coefficients, and spatial continuity constraints. First, we propose an innovative spectral fitting method that aims to expand the frequency bandwidth while adhering to the desired wavelet constraints. This method allows us to fully utilize the effective frequency information. It not only obtains broadband seismic data but also captures precise wavelets. Then, sparse deconvolution is employed to further extend the frequency range by utilizing the accurately expected wavelet and obtaining a high-resolution reflection coefficient. Finally, the Hessian matrix regularization is employed to constrain the spatial continuity of the reflection coefficient. This method is validated in both the model and real seismic data. Compared to traditional sparse deconvolution and spectral modeling deconvolution with spatial constraints, this method not only expands the frequency bandwidth and enhances seismic resolution but also preserves operational frequency information and improves the spatial continuity of seismic data. It has been verified that this approach can be used to forecast thin reservoir and reconstruct spatial tectonic characteristics.

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“…Then, Sacchi used Huber distribution, Cauchy distribution and Sech distribution to recover sparse reflection sequences (Sacchi, 1997). Li et al (2013) used the Cauchy criterion as a constraint and used the preconditioned conjugate gradient method to estimate the sequence of seismic wavelet and sparse reflection coefficients. Velis (2008) proposed stochastic sparse-spike deconvolution and pointed out that the sparse-spike deconvolution is essentially to find some spikes from noisy records and determine their positions and amplitudes.…”

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confidence: 99%

The high‐resolution seismic deconvolution method based on joint sparse representation using logging–seismic data

Wang

Zhang

et al. 2022

Geophysical Prospecting

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Seismic high-resolution processing is an essential part of seismic processing. Sparsespike deconvolution is a widely used method for improving the resolution of seismic data. However, the stratigraphic reflection coefficients do not fully satisfy the hypothesis of sparse-spike deconvolution, and this method does not make full use of prior information, such as well-logging data. In this paper, we have developed a highresolution processing method based on joint sparse representation using logging and seismic data. This method can extract stratigraphic information from well-logging reflection coefficients and observational seismic data at the same location by joint dictionary learning. Through joint sparse representation, the relationship between observed seismic data and the reflection coefficient is established. Under the framework of joint sparse representation, the deconvolution of seismic data is realized. The synthetic data and field data tests show that our method can reveal thin layers and can invert reflection coefficients from strongly noisy seismic data accurately. Moreover, the deconvolution results of our method match well with the well-logging data. The tests demonstrate that the improvement of accuracy of deconvolution results with our method, compared to sparse-spike deconvolution.

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Experimental analysis and application of sparsity constrained deconvolution

Cited by 17 publications

References 12 publications

Characterization of interbedded thin beds using zero-crossing-time amplitude stratal slices

Characterization of interbedded thin beds using zero-crossing-time amplitude stratal slices

A novel high-resolution imaging method based on sparsity in the time domain and spectral fitting in the frequency domain

The high‐resolution seismic deconvolution method based on joint sparse representation using logging–seismic data

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