基于快速自适应非局部均值滤波的地震随机噪声压制方法

doi:10.11720/wtyht.2022.1522

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Abstract

The quality of seismic data plays a critical role in geological interpretation.However,the real seismic data usually contain a lot of noise,leading to fuzzy strata and unclear fault structures.The non-local means (NLM) filtering algorithm can effectively suppress random noise,but its computational efficiency is low.Therefore,it has limitations when being applied to large-scale seismic data processing.This study proposed a fast adaptive NLM algorithm,for which the computational efficiency was improved using the centrosymmetric data integration algorithm and the filtering parameters were adaptively adjusted using the standard deviation of similarity to estimate the homogeneity,thus further improving the noise attenuation effect.Therefore,the modified NLM filtering algorithm can effectively improve computational efficiency and enhance the noise attenuation effect.Furthermore,the feasibility and effectiveness of the algorithm were verified using model data and actual data.

Key words： adaptive NLM filtering centrosymmetric data integration homogeneity estimation random noise suppression

Received: 23 September 2021 Published: 03 January 2023

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	Ya-Tong CUI
	Sheng-Hou WANG
	Zhong-Xian CAI

Cite this article:

Ya-Tong CUI,Sheng-Hou WANG,Zhong-Xian CAI. Seismic random noise attenuation method based on the fast adaptive non-local means filtering algorithm[J]. Geophysical and Geochemical Exploration, 2022, 46(5): 1187-1195.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2022.1522 OR https://www.wutanyuhuatan.com/EN/Y2022/V46/I5/1187

Distribution of the filtering parameter h²
a—noise-free data;b—noisy data;c—parameter distribution of minimum variance estimation;d—parameter distribution of standard deviation of similarity

Noise attenuation results of synthetic model data from model data test 1
a—noise-free data;b—noisy data(SNR=-4.55 dB);c—denoised result by using conventional NLM method;d—difference between a and c;e—denoised result by using NLM based on minimum variance estimation;f—difference between a and e;g—denoised result by using proposed method;h—difference between a and g

Comparison of SNR,PSNR and MSE using different methods

Noise attenuation results of synthetic model data from model data test 2
a—noise-free data;b—noisy data (SNR=-3.01 dB);c—denoised result by using conventional NLM method;d—difference between a and c;e—denoised result by using NLM based on minimum variance estimation;f—difference between a and e;g—denoised result by using proposed method;h—difference between a and g

Comparison of calculation time and effect of different denoising methods based on model data test 2
a—computational time comparison;b—denoising quality comparison

Noise attenuation results of field data
a—field data;b—denoised result by using conventional NLM method;c—denoised result by using NLM based on minimum variance estimation;d—denoised result by using proposed method;e—difference between a and b;f—difference between a and c;g—difference between a and e

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