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Journal of Inverse and Ill-posed Problems

Editor-in-Chief: Kabanikhin, Sergey I.

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1569-3945
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Data-driven multichannel seismic impedance inversion with anisotropic total variation regularization

Dehua Wang
/ Jinghuai Gao
• School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, P. R. China
• Email
• Other articles by this author:
/ Hongan Zhou
Published Online: 2017-09-21 | DOI: https://doi.org/10.1515/jiip-2017-0024

Abstract

Acoustic 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.

MSC 2010: 86A22; 86-08; 65J20

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Revised: 2017-07-08

Accepted: 2017-08-24

Published Online: 2017-09-21

Published in Print: 2018-04-01

Funding Source: National Natural Science Foundation of China

Award identifier / Grant number: 41390454

The research is supported by Young Talent fund of University Association for Science and Technology in Shaanxi, China (Grant No. 20170701) and the Major Program of the National Natural Science Foundation of China (Grant No. 41390454).

Citation Information: Journal of Inverse and Ill-posed Problems, Volume 26, Issue 2, Pages 229–241, ISSN (Online) 1569-3945, ISSN (Print) 0928-0219,

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