Q-Compensated Denoising of Seismic Data

Wang, Hang; Huang, Guangtan; Chen, Wei; Chen, Yangkang

doi:10.1109/tgrs.2020.3010813

Cited by 9 publications

(1 citation statement)

References 48 publications

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“…To solve the problem of normal L1 norm inversion, the main methods include the iterative shrinkage thresholding/ shaping method [28][29][30], the preconditioned conjugate gradient (PCG) method [31,32], and split-Bregman method [33]. Among them, the PCG method has the advantages of solving the convenient inverse matrix and accelerating the convergence rate [34].…”

Section: E Ant Colony Optimizationmentioning

confidence: 99%

Ant Colony Optimization Inversion Using the L1 Norm in Advanced Tunnel Detection

Zhou

Deng

et al. 2021

Mathematical Problems in Engineering

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During the construction of the tunnel, there may be water-bearing anomalous structures such as fault fracture zone. In order to ensure the safety of the tunnel, it is necessary to carry out advanced tunnel detection. The traditional linear inversion method is highly dependent on the initial model in the tunnel resistivity inversion, which makes the inversion results falling into the local optimal optimum rather than the global one. Therefore, an inversion method for tunnel resistivity advanced detection based on ant colony algorithm is proposed in this paper. In order to improve the accuracy of tunnel advanced detection of deep anomalous bodies, an ant colony optimization (ACO) inversion is used by integrating depth weighting into the inversion function. At the same time, in view of the high efficiency and low cost of one-dimension inversion and the advantages of L1 norm in boundary characterization, a one-dimensional ant colony algorithm is adopted in this paper. In order to evaluate the performance of the algorithm, two sets of numerical simulations were carried out. Finally, the application of the actual tunnel water-bearing anomalous structure was carried out in a real example to evaluate the application effect, and it was verified by excavation exposure.

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Section: E Ant Colony Optimizationmentioning

confidence: 99%

Ant Colony Optimization Inversion Using the L1 Norm in Advanced Tunnel Detection

Zhou

Deng

et al. 2021

Mathematical Problems in Engineering

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Fast sparse Bayesian learning-based seismic resolution enhancement

Zhang,

Duan,

Lan

2023

Journal of Applied Geophysics

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Robust seismic attenuation compensation based on generalized minimax concave penalty sparse representation

Duan,

Zhang

2023

Journal of Geophysics and Engineering

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Deep hydrocarbon resources have become more and more important nowadays. However, owing to the affection of long-distance propagation and stratigraphic absorption, seismic data coming from deep beds generally suffer from weak energy, low resolution, and low signal-to-noise ratio (SNR), which seriously influence the reliability of seismic interpretation. Generally, inverse Q (quality factor) filtering (IQF) is used for absorption compensation, but it may amplify noise at the same time. Although compensation methods based on inversion overcomes the instability, it is still difficult to obtain high-SNR results. To address this issue, under the framework of sparse representation theory, we proposed a single-channel attenuation compensation method constrained by generalized minimax concave (GMC) penalty function. It takes the modified Kolsky model to describe seismic absorption and combines sparse representation theory to create objective function. Furthermore, a GMC penalty function is utilized to promote sparsity. It allows more accurate estimates of sparse coefficients from noise-contaminated seismic data. Although the GMC penalty itself is concave, the objective function remains strictly convex. Therefore, globally optimal sparse solutions can be obtained through an operator-splitting algorithm. Even in the presence of noise, this method can obtain stable and accurate compensation results through reconstruction. Synthetic data tests and field seismic data application showed that this method has high robustness to noise. It can stably and effectively compensate for the energy loss of seismic data, as well as maintain high SNR.

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Q-Compensated Denoising of Seismic Data

Cited by 9 publications

References 48 publications

Ant Colony Optimization Inversion Using the L1 Norm in Advanced Tunnel Detection

Ant Colony Optimization Inversion Using the L1 Norm in Advanced Tunnel Detection

Fast sparse Bayesian learning-based seismic resolution enhancement

Robust seismic attenuation compensation based on generalized minimax concave penalty sparse representation

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