动校正域CNN法压制自由表面多次波  

Surfaced-related multiple attenuation using the CNN method in the NMO domain

作　　者：黄柱富 刘剑锋[1] 方文倩 付丽华[1] HUANG Zhufu;LIU Jianfeng;FANG Wenqian;FU Lihua(School of Mathematics and Physics,China University of Geosciences,Wuhan 430074,China)

机构地区：[1]中国地质大学(武汉)数学与物理学院,湖北武汉430074

出　　处：《煤田地质与勘探》2024年第11期160-170,共11页Coal Geology & Exploration

基　　金：国家自然科学基金项目(42274172);重庆市自然科学基金项目(2023NSCQ-MSX0207);广东省自然科学基金项目(2024A1515011680)。

摘　　要：【目的】自由表面多次波的存在影响了地震资料的解释精度,有效压制多次波是地震处理的重要环节。多次波是具有与有效信号相似特征的相干噪声,传统的卷积神经网络(convolution neural network, CNN)方法难以从全波场数据中分辨出多次波信号。此外,不同工区的自由表面多次波差异较大,导致CNN方法在迁移时面临更严重的问题。【方法】为此,引入物理先验,提出一种基于动校正(normal moveout correction,NMO)域的CNN方法。利用NMO域上一次波与多次波呈现出的曲率特征差异训练CNN,以实现对多次波的有效识别和压制。通过仿真和真实数据测试了方法的性能。【结果和结论】实验结果表明:动校正域上训练的CNN能够有效识别并压制多次波信号,同时保护一次波反射信号。与传统Radon算法相比,提出的方法减少了人为调整参数并降低了计算复杂度,且有效信号泄漏更少;与直接端到端基于CNN的自由表面多次波压制方法相比,新方法面向新数据的适应能力更好。研究成果可为提高地震资料解释精度和降低计算成本方面提供新思路。[Objective]The presence of surface-related multiples results in a lower accuracy in seismic data interpretations,and the effective multiple attenuation is a key step in seismic data processing.Multiples are coherent noise signals with similar characteristics to effective signals.It is difficult impossible to distinguish multiple signals from full-wave field data using a traditional convolutional neural network(CNN).Additionally,since the surface-related multiples vary significantly with surveyed areas,the CNN method will face more severe challenges when being transferred to other networks.[Methods]This study developed a CNN method based on the normal moveout correction(NMO)domain by introducing physical priors.The CNN was trained using the differences in curvature characteristics between the primary waves and multiples in the NMO domain,aiming to achieve effective multiple identification and attenuation.The performance of this method was tested using simulations and practical data.[Results and Conclusions]Experimental results indicate that the CNN trained in the NMO domain can effectively identify and attenuate multiples while protecting the reflected signals of primary waves.Compared to the traditional Radon algorithm,the proposed method exhibits reduced manual parameter adjustments and calculation complexity,along with less leakage of effective signals.Compared to direct end-to-end CNN-based methods for surface-related multiple attenuation,the novel method is more adaptable to new data.The results of this study can provide a new philosophy for improving the accuracy of seismic data interpretations and reducing the calculation cost.

关 键 词：自由表面多次波 卷积神经网络 动校正 相干噪声 多次波压制 

分 类 号：P631[天文地球—地质矿产勘探]