Title: Seismic data reconstruction method based on morphological component analysis

Authors: Jianhong Yao; Jicheng Liu; Ya Gu; Yongxin Chou

Addresses: School of Automotive Engineering, Changshu Institute of Technology, Changshu, 215500, China ' School of Electric and Automation Engineering, Changshu Institute of Technology, Changshu, 215500, China ' School of Electric and Automation Engineering, Changshu Institute of Technology, Changshu, 215500, China ' School of Electric and Automation Engineering, Changshu Institute of Technology, Changshu, 215500, China

Abstract: The real seismic data is usually under-sampled in space domain because of the physical or economic limitations. So the incomplete seismic data need to be reconstructed before the subsequent processing. A method based on morphological component analysis (MCA) is discussed in the paper, which uses the curvelet dictionary and local discrete cosine transform (LDCT) dictionary to reconstruct the smooth components and the singular components respectively. Block coordinate relaxation (BCR) algorithm is adopted to complete the sparse optimisation. The validity of the proposed method was tested by numerical experiments on synthetic and real data demonstrate. As the method based on curvelet combining with POCS is widely used in practice, we compare its reconstructed results with the MCA-based method. The numerical results validate that the proposed method has higher reconstruction performance.

Keywords: seismic data reconstruction; morphological component analysis; MCA; curvelet; local discrete cosine transform; LDCT.

DOI: 10.1504/IJMIC.2021.121835

International Journal of Modelling, Identification and Control, 2021 Vol.37 No.3/4, pp.211 - 217

Received: 20 Sep 2020
Accepted: 16 Dec 2020
Published online: 07 Apr 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article