Multichannel deconvolution with spatial refl ection regularization

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Abstract Seismic deconvolution plays an important role in the seismic characterization of thin-layer structures and seismic resolution enhancement. However, the trace-by-trace processing strategy is applied and ignores the spatial connection along seismic traces, which gives the deconvolved result strong ambiguity and poor spatial continuity. To alleviate this issue, we developed a structurally constrained deconvolution algorithm. The proposed method extracts the reflection structure characterization from the raw seismic data and introduces it to the multichannel deconvolution algorithm as a spatial reflection regularization. Benefiting from the introduction of the reflection regularization, the proposed method enhances the stability and spatial continuity of conventional deconvolution methods. Synthetic and field data examples confirm the correctness and feasibility of the proposed method.

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Key words： Deconvolution spatial reflection regularization resolution sparse-spike

Received: 2020-11-02;

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This work was supported by National Key R & D Program of China (No.2018YFA0702504), the National Natural Science Foundation of China (Nos.42074141, 41874141) and the Strategic Cooperation Technology Projects of CNPC and CUP (ZLZX2020-03).

Corresponding Authors: Li Guo-Fa (Email: lgfseismic@126.com).
E-mail: lgfseismic@126.com

About author: Li Hao, he earned his master's degree ( 2012) in geological engineering (geophysical exploration) from the China University of Petroleum (Beijing). Now he is studying his PhD in College of Geophysics of the university. His research interests are high-resolution seismic data processing and complex reservoir prediction methods

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. Multichannel deconvolution with spatial refl ection regularization[J]. APPLIED GEOPHYSICS, 2021, 18(1): 85-93.

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