Seismic data reconstruction based on segmented random sampling and MCA

WANG De-Ying; ZHANG Kai; LI Zhen-Chun; ZHANG Yi-Kui; XU Xin

doi:10.11720/wtyht.2022.1458

2022 Vol. 46, No. 5

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Article navigation > Geophysical and Geochemical Exploration > 2022 > 46(5): 1214-1224

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WANG De-Ying, ZHANG Kai, LI Zhen-Chun, ZHANG Yi-Kui, XU Xin. 2022. Seismic data reconstruction based on segmented random sampling and MCA. Geophysical and Geochemical Exploration, 46(5): 1214-1224. doi: 10.11720/wtyht.2022.1458

Citation:

WANG De-Ying, ZHANG Kai, LI Zhen-Chun, ZHANG Yi-Kui, XU Xin. 2022. Seismic data reconstruction based on segmented random sampling and MCA. Geophysical and Geochemical Exploration, 46(5): 1214-1224. doi: 10.11720/wtyht.2022.1458

Seismic data reconstruction based on segmented random sampling and MCA

1. Research Institute of Petroleum Exploration & Development-Northwest (NWGI),PetroChina,Lanzhou 730030,China；
2. School of Geosciences,China University of Petroleum,Qingdao 266580,China；
3. Wuhua Energy Technology Co.,Ltd,Xi'an 710067,China

Received Date: 25 November 2021

Revised Date: 20 October 2022

Publish Date: 03 January 2023

Abstract

Abstract

Data reconstruction is a critical preliminary work in the processing of seismic data.Compressed sensing (CS) has been well applied in data reconstruction.The key to CS is random sampling,which converts the mutual coherent alias caused by regular under-sampling into lower-amplitude incoherent noise. But traditional sampling methods lack constraints on sampling points, resulting in excessive noise interference. The segmented random sampling (SRS) method can effectively control the distance between sampling points. Furthermore, a single mathematical transformation will lead to incomplete sparse representation and impact data reconstruction. The morphological component analysis (MCA) can decompose a signal into several components with outstanding morphological features to approximate the complex internal structure of data. A new dictionary combination (Shearlet+DCT) has been found under the MCA framework, and the block coordinate relaxation (BCR) algorithm has been used to get the optimal solution to obtain desired reconstruction results. Tests of real data have proven that the proposed method can produce good effects when used to reconstruct the SRS data.
- compressed sensing /
- segmented random sampling /
- morphological component analysis /
- data reconstruction

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References

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