Application of Sparse Inverse Spectral Attributes to Channels Detection

Wang, T.Y.; Yuan, Sanyi; Song, Zhiqiang; Luo, Chengping; He, Yansong; Wang, S.X.

doi:10.3997/2214-4609.201801264

Cited by 5 publications

(2 citation statements)

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“…After the ISD is proposed, its improvements are focused primarily on the appropriate selection of the wavelet library (such as the truncated sinusoid, the Ricker wavelet, the Morlet wavelet or the extracted wavelet), the type of the constraint or the prior information (such as the l 2 norm, the l 1 norm, the mixed l 1 -l 2 norm, the coherency-based constraint, or the hierarchical Gaussian prior), and the algorithm (such as the iterative soft thresholding algorithm, the iteratively reweighted least-squares algorithm, spectral projected gradient for l 1 minimization, Bregman iterative algorithm or sparse Bayesian learning) for solving ISD (Puryear et al 2012;Han et al 2012;Gholami 2013;Tary et al 2014;Amosu et al 2016;Ma et al 2019;Yuan et al 2019). In contrast to the development of methods, the fine application of the ISD results is rare (Gholami 2013;Oyem and Castagna 2013;Han et al 2016;Li et al 2016;Wang et al 2018), especially for interpreting 3D seismic data more than tens of thousands of traces, which are very common in oil companies. Moreover, how to make full use of the ISD results is not unimportant.…”

Section: Introductionmentioning

confidence: 99%

Inverse spectral decomposition using an lp-norm constraint for the detection of close geological anomalies

et al. 2020

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An important application of spectral decomposition (SD) is to identify subsurface geological anomalies such as channels and karst caves, which may be buried in full-band seismic data. However, the classical SD methods including the wavelet transform (WT) are often limited by relatively low time–frequency resolution, which is responsible for false high horizon-associated space resolution probably indicating more geological structures, especially when close geological anomalies exist. To address this issue, we impose a constraint of minimizing an lp (0 < p < 1) norm of time–frequency spectral coefficients on the misfit derived by using the inverse WT and apply the generalized iterated shrinkage algorithm to invert for the optimal coefficients. Compared with the WT and inverse SD (ISD) using a typical l1-norm constraint, the modified ISD (MISD) using an lp-norm constraint can yield a more compact spectrum contributing to detect the distributions of close geological features. We design a 3D synthetic dataset involving frequency-close thin geological anomalies and the other 3D non-stationary dataset involving time-close anomalies to demonstrate the effectiveness of MISD. The application of 4D spectrum on a 3D real dataset with an area of approximately 230 km2 illustrates its potential for detecting deep channels and the karst slope fracture zone.

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Section: Introductionmentioning

confidence: 99%

Inverse spectral decomposition using an lp-norm constraint for the detection of close geological anomalies

et al. 2020

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“…Several seismic attributes, such as coherence, curvature, phase, spectral decomposition, and other edge-sensitive attributes (e.g., Luo et al 2003;Oyedele 2005;Yuan et al 2019a, b;Li et al 2018;Wang et al 2018c), have been proposed and used to detect geological anomalies. Different seismic attributes are sensitive to different geological structures.…”

Section: Introductionmentioning

confidence: 99%

Multi-sparsity-based spectral attributes for discontinuity detection

et al. 2019

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Structural and stratigraphic discontinuities, such as faults and channels, generally contribute to the construction of traps and reservoirs. Spectral decomposition can utilize the sensitivities of different frequency components to different geological conditions to identify these geological anomalies. The sparse inverse spectral decomposition (SISD) involves a sparse constraint of time-frequency spectra, and one critical parameter is the sparsity which determines the time-frequency resolution. A small sparsity gives a low temporal resolution result that cannot be used for thin-bed detection. Conversely, a large sparsity provides a high-resolution result, but it may lose weak reflection signals. The complex geological conditions in the subsurface will lead to some difficulties in detecting the discontinuities by using the SISD method with a fixed sparsity. To address this issue, we propose multi-sparsity-based spectral attributes by fusing the amplitude spectra results of three different sparsities to detect subsurface discontinuities. Compared with the fixed sparsity, the multi-sparsity-based spectral attributes can detect more geological details and highlight geological edges more clearly. The application on a 3D real data with an area of 230 km 2 from deep formation in Northwest China exhibits its effectiveness in discontinuity detection. The proposed method can detect the weak or small hidden geological details more and better than the fixed sparsity method, suggesting that it may serve as a future tool for detecting the distribution of geological abnormalities in subsurface.

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