扩散滤波和统计学字典学习联合地震噪声压制方法

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中图分类号：P631 文献标识码：A DOI：10.13810/j.cnki.issn.1000-7210.20240392

Abstract： In seismic surveys of complex surfaces such as loess plateau areas，coherent noise greatly reduces the signal-to-noise ratio（SNR）of seismic data，which seriously afects the accuracy of subsequent seismic imaging and physical inversion. To this end，a new coherent noise suppression strategy is proposed in the paper. The method mainly consists of the folowing core steps.Firstly，anisotropic diffsion filtering is adopted to effectively suppress the incoherent noise components in the data，and initially improve the overallquality of the low SNR data. Next，the dictionary learning method is employed to sparsely characterize the seismic data，and statistical indicators are applied to precisely locate and eliminate the dictionary atoms with a large variance of gradients.These atoms tend to be the main carriers oflinear coherent noise and random noise.Then，the dictionary atoms and their corresponding sparse coeficients that can efctively characterize the effective signals are filtered and retained to reconstruct the seismic data.Finall，the effctive signals are further extracted from the re moved noisethrough the principle of signal-to-noise local orthogonalization. The simulated data and typical real data tests show that the method ensures the intact preservation of the efective signals while suppressing the co herent and random noises，which further improves the SNR of the data. The method can provide a reference for the treatment of linear coherent noise.

Keywords：diffusion filtering，dictionary learning，statistical atomicclassification，linear noise attenuation 董旭光，刘斌，张浩，等：扩散滤波和统计学字典学习联合地震噪声压制方法[J].石油地球物理勘探，2025，60（4）： 852-860. DONG Xuguang，LIU Bin，ZHANG Hao，et al. Diffusion filtering and statistical dictionary learning combined with seismic noise suppression[J]. Oil Geophysical Prospecting，2025，60（4） ：852-860.

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随着国内油气勘探开发的不断深人，勘探目标已从传统的构造油气藏拓展至更复杂的岩性油气藏；同时，勘探作业环境也从相对简单的地表条件向西部地区极具挑战性的复杂地表条件转变[。（剩余12729字）