基于Shearlet变换的非局部均值地震噪声压制

doi:10.11720/wtyht.2023.2630

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Abstract

Owing to the limitations of both the field environment for seismic data acquisition and the performance of instruments,the seismic signals collected in seismic exploration are inevitably mixed with strong noise,thus greatly affecting the subsequent processing and interpretation.In recent years,multi-scale geometric analysis methods have become an important topic in noise suppression owing to their unique advantages.This study proposed suppressing the seismic noise using a non-local mean (NLM) algorithm in the Shearlet domain.First,the non-subsampled Shearlet transform (NSST) was performed for seismic signals.Then,the decomposed coefficient subset was further processed using the NLM method,and the weight function was improved by using eight Sobel operators to approximate the omnidirectional structure.Finally,the inverse Shearlet transform was performed for the coefficients to obtain the denoised seismic signals.Experimental results show that this combined algorithm can effectively suppress the random noise and preserve the weak events,thus showing high practicability in the seismic data processing.

Key words： random noise Shearlet transform Sobel operator non-local mean structure preservation

Received: 26 December 2021 Published: 24 February 2023

ZTFLH:

P631.4

Corresponding Authors: AN Yong E-mail: wjg4541@stu.ouc.edu.cn;yongan@cup.edu.cn

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	Articles by authors
	Jin-Gang WANG
	Yong AN
	Zhen-Wang XU

Cite this article:

Jin-Gang WANG,Yong AN,Zhen-Wang XU. Seismic noise suppression using non-local means algorithm based on the Shearlet transform[J]. Geophysical and Geochemical Exploration, 2023, 47(1): 199-207.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2023.2630 OR https://www.wutanyuhuatan.com/EN/Y2023/V47/I1/199

Sobel operators templates in different directions

An illustration of Shearlet decomposition

The variation of P_SNR with smoothing parameters under different noise levels

Synthetic seismic record
a—the original profile;b—simulated a profile with random noise

The results of model data
a—denoising result with traditional NML algorithm;b—the residual profile

The results of medel data
a—denoising result with the method in this paper;b—the residual profile

Frequency-wavenumber spectrum
a—raw seismic data;b—simulated a profile with random noise;c—traditional NLM algorithm;d—the algorithm of this paper

Original data,noisy data and the results of two denoising methods in single trace comparison

Original data spectrum,noisy data spectrum and the results of this article spectrum

Original post-stack data (a),traditional NLM method processing(b) and the method in this paper(c)

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