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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摘要

地震资料的质量对于地质解释起着至关重要的作用。实际地震数据通常会携带大量噪声,使地层模糊,断层构造不清晰。非局部均值滤波方法(NLM)可以有效地压制随机噪声,但其计算效率较低,因此在大型地震数据处理应用中具有局限性。本文给出了一种快速自适应NLM算法,该方法利用中心对称数据积分算法提高NLM方法的计算效率,并利用相似度标准差估计均匀性来自适应地调整滤波参数,进一步提高去噪效果。因此,改进后的非局部均值滤波方法可以有效地提高计算效率,同时可以增强噪声压制效果。最后,通过模型数据和实际数据验证了该方法的可行性、有效性。

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	崔亚彤
	王胜侯
	蔡忠贤

关键词 ：自适应非局部均值滤波, 中心对称数据积分, 均匀性估计, 随机噪声压制

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

收稿日期: 2021-09-23 修回日期: 2022-07-22 出版日期: 2022-10-20

ZTFLH:

P631.4

基金资助:中国科学院战略性先导科技专项(A类)(XDA14010302)

作者简介: 崔亚彤(1993-),女,博士,2021年毕业于中国地质大学(北京)地球物理学专业,主要从事地球物理数据处理及相关研究工作。Email:YatongCui@email.cugb.edu.cn

引用本文:

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

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https://www.wutanyuhuatan.com/CN/10.11720/wtyht.2022.1522 或 https://www.wutanyuhuatan.com/CN/Y2022/V46/I5/1187

Fig.1 滤波参数h²分布
a—无噪声的数据;b—加噪数据;c—最小方差估计的参数分布情况;d—相似度标准差的参数分布情况

Fig.2 模型试验1的合成模型数据噪声压制结果
a—无噪声的数据;b—噪声数据(SNR=-4.55 dB);c—传统NLM方法去噪结果;d—a与c的差剖面;e—基于最小方差估计的NLM方法去噪结果;f—a与e的差剖面;g—本文方法去噪的结果;h—a与g的差剖面

Table 1 不同方法的SNR、PSNR和MSE对比

Fig.3 模型试验2的合成模型数据噪声压制结果
a—无噪声的数据;b—噪声数据(SNR=-3.01 dB);c—传统NLM方法去噪结果;d—a与c的差剖面;e—基于最小方差估计的NLM方法去噪结果;f—a与e的差剖面;g—本文方法去噪的结果;h—a与g的差剖面

Fig.4 基于模型数据2不同方法去噪计算时间及效果对比
a—计算时间对比;b—去噪质量对比

Fig.5 实际地震数据去噪结果
a—实际地震数据;b—传统NLM方法去噪结果;c—基于最小方差估计的NLM去噪结果;d—本文方法去噪结果;e—a与b的差剖面;f—a与c的差剖面;g—a与e的差剖面

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