3D Block Matching Seismic Data Denoising Based on Curvelet Noise Estimation

SUN Chengyu1,2 DIAO Juncai1,2 LI Wenjing3

(1.School of Geosciences, China University of Petroleum (East China), Qingdao, Shandong Province, China 266580)
(2.Function Laboratory of Marine Geo-Resource Evaluation and Exploration Technology, Laboratory for Marine Mineral Resources, Qingdao, Shandong Province, China 266071)
(3.Research & Development Center, BGP Inc., CNPC, Zhuozhou, Hebei Province, China 072751)

【Abstract】Conventional block matching 3D (BM3D) algorithms are used for seismic data denoising. However, some parameters such as filtering threshold are difficult to be determined because of the lack of prior noise information in the practical processing. In this paper, an improved BM3D denoising method based on the curvelet noise estimation is developed for seismic data. First the noise variance of seismic data is estimated by the curvelet transform. Then appropriate threshold parameters are adaptively determined. Finally the noise elimination is accurately achieved by this improved BM3D. Based on model and real data tests, the proposed algorithm can better eliminate random noise and protect signals than the conventional BM3D and curvelet transform. Furthermore, the proposed algorithm maintains most detailed information of boundary reflection and its computational efficiency is relatively high.

【Keywords】 seismic data denoising; block matching 3D (BM3D); noise prior estimation; curvelet transform; signal-to-noise ratio (SNR);

【DOI】

【Funds】 National Natural Science Foundation of China (41874153) National Science and Technology Major Project (2016ZX05006-002-003)

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This Article

ISSN:1000-7210

CN: 13-1095/TE

Vol 54, No. 06, Pages 1188-1194+1171

December 2019

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Article Outline

Abstract

  • 0 Introduction
  • 1 Principles
  • 2 Model tests
  • 3 Application example
  • 4 Conclusion
  • References