Seismic random noise attenuation method based on the fast adaptive non-local means filtering algorithm
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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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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. -
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