scholarly journals Seismic impedance inversion using second-order overlapping group sparsity with A-ADMM

2019 ◽  
Vol 17 (1) ◽  
pp. 97-116 ◽  
Author(s):  
Hao Wu ◽  
Shu Li ◽  
Yingpin Chen ◽  
Zhenming Peng

Abstract The anisotropic total variation with overlapping group sparsity (ATV_OGS) regularisation term is an improvement on the anisotropic total variation (ATV) regularisation term. It has been employed successfully in seismic impedance inversion as it can enhance the boundary information and relieve the staircase effect by exploring the structured sparsity of seismic impedance. However, because ATV_OGS constrains only the structured sparsity of the impedance's first-order difference and ignores the structured sparsity of the second-order difference, the staircase effect still occurs in an inversion result based on ATV_OGS. To further fit the structured sparsity of the impedance's second-order gradients, we introduce the overlapping group sparsity into the second-order difference of the impedance and propose a novel second-order ATV with overlapping group sparsity (SATV_OGS) seismic impedance inversion method. The proposed method reduces the interference of the large amplitude noise and further mitigates the staircase effect of the ATV_OGS. Furthermore, the accelerated alternating direction method of multipliers (A-ADMM) framework applied to this novel method. It can increase the efficiency of inversion. The experiments are carried out on a general model data and field data. Based on the experimental results, the proposed method can obtain higher resolution impedance than some impedance inversion methods based on total variation.

2018 ◽  
Vol 8 (11) ◽  
pp. 2317 ◽  
Author(s):  
Lingzhi Wang ◽  
Yingpin Chen ◽  
Fan Lin ◽  
Yuqun Chen ◽  
Fei Yu ◽  
...  

Models based on total variation (TV) regularization are proven to be effective in removing random noise. However, the serious staircase effect also exists in the denoised images. In this study, two-dimensional total variation with overlapping group sparsity (OGS-TV) is applied to images with impulse noise, to suppress the staircase effect of the TV model and enhance the dissimilarity between smooth and edge regions. In the traditional TV model, the L1-norm is always used to describe the statistics characteristic of impulse noise. In this paper, the Lp-pseudo-norm regularization term is employed here to replace the L1-norm. The new model introduces another degree of freedom, which better describes the sparsity of the image and improves the denoising result. Under the accelerated alternating direction method of multipliers (ADMM) framework, Fourier transform technology is introduced to transform the matrix operation from the spatial domain to the frequency domain, which improves the efficiency of the algorithm. Our model concerns the sparsity of the difference domain in the image: the neighborhood difference of each point is fully utilized to augment the difference between the smooth and edge regions. Experimental results show that the peak signal-to-noise ratio, the structural similarity, the visual effect, and the computational efficiency of this new model are improved compared with state-of-the-art denoising methods.


2016 ◽  
Vol 216 ◽  
pp. 502-513 ◽  
Author(s):  
Jun Liu ◽  
Ting-Zhu Huang ◽  
Gang Liu ◽  
Si Wang ◽  
Xiao-Guang Lv

2019 ◽  
Vol 341 ◽  
pp. 128-147 ◽  
Author(s):  
Meng Ding ◽  
Ting-Zhu Huang ◽  
Si Wang ◽  
Jin-Jin Mei ◽  
Xi-Le Zhao

2018 ◽  
Vol 26 (2) ◽  
pp. 229-241 ◽  
Author(s):  
Dehua Wang ◽  
Jinghuai Gao ◽  
Hongan Zhou

AbstractAcoustic impedance (AI) inversion is a desirable tool to extract rock-physical properties from recorded seismic data. It plays an important role in seismic interpretation and reservoir characterization. When one of recursive inversion schemes is employed to obtain the AI, the spatial coherency of the estimated reflectivity section may be damaged through the trace-by-trace processing. Meanwhile, the results are sensitive to noise in the data or inaccuracies in the generated reflectivity function. To overcome the above disadvantages, in this paper, we propose a data-driven inversion scheme to directly invert the AI from seismic reflection data. We first explain in principle that the anisotropic total variation (ATV) regularization is more suitable for inverting the impedance with sharp interfaces than the total variation (TV) regularization, and then establish the nonlinear objective function of the AI model by using anisotropic total variation (ATV) regularization. Next, we solve the nonlinear impedance inversion problem via the alternating split Bregman iterative algorithm. Finally, we illustrate the performance of the proposed method and its robustness to noise with synthetic and real seismic data examples by comparing with the conventional methods.


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