Review of Research on Filtering-based Methods for Seismic Scattered Wave Separation
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摘要:
在地震勘探中，由于地下结构错综复杂，多尺度非均匀的地质体常会形成包含反射波、散射波等在内的复杂的地震波场。传统的成像方法一般只考虑反射波场，忽略了散射波场，这使得细小结构无法准确成像，从而影响对复杂构造的识别。为了对小尺度构造进行准确的地震成像，要将散射波从地震波场中分离出来。在众多波场分离算法中，基于滤波的波场分离方法可以准确提取散射波，提高成像分辨率。本文调研和归纳多种基于滤波处理的地震散射波分离方法，围绕国内外学者在滤波处理波场分离方面的研究成果，总结各种方法的研究进展，并对比和分析各方法的分离效果，最后结合人工智能深度学习的研究趋势，对未来滤波处理散射波分离的发展方向进行展望。
Abstract:In seismic exploration, complex seismic wave fields comprising reflected waves, scattered waves, and other phenomena are formed due to the intricate nature of underground structures. Traditional imaging methods typically focus solely on the reflected wave field, disregarding the scattered wave field. This limitation hampers accurate imaging of small-scale structures and impedes the identification of complex structures. To address this challenge and achieve precise imaging of small-scale structures, it is crucial to separate from the scattered waves from the seismic wave field. Among the various wave field separation algorithms, filtering-based methods have shown promising results in accurately extracting scattered waves and enhancing imaging resolution. This study explores and summarizes different methods for seismic scattered wave separation based on filtering techniques. By reviewing the research findings of both domestic and international scholars in the field of filtering-based scattered wave separation, the study provides an overview of the progress made and compares and analyzes the separation effects of each method. Additionally, considering the advancements in deep learning within the realm of artificial intelligence, the future development direction of filtering-based scattered wave separation is also envisioned.
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Keywords:
- seismic imaging /
- wave field separation /
- scattered wave /
- filtering /
- high resolution
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近年来，随着地震台站不断加密，地震台网监测到的地震数量呈爆发式增长，各省、市地震局、监测中心集中式处理地震数据能力日益捉襟见肘。随着大数据、云计算、人工智能、“互联网+”及5G技术的快速发展，集中式地震处理尽管已经在很大程度上实现了自动化，提升了整个数据中心的处理和快速响应能力，但仍然需要耗费大量人力、物力来提高网络覆盖率和传输速度，而且还需要进行高性能服务器升级以及研发高效率自动化地震波形处理系统等。如何将深度学习等强大的人工智能技术高效地部署在资源受限的终端设备，从而使人工智能更加贴近用户端与传感器端，解决人工智能落地的“最后一公里”这一问题，已经引起了学术界与工业界的高度关注。
针对密集台阵的海量地震数据近实时处理的需求，中国地震局地球物理研究所科研团队基于千万量级高质量标注数据集训练出了P、S震相检测，P波初动极性检测、单台定位、震级预测等深度学习网络模型，将这些预训练的高泛化力深度学习模型轻量化，并将其部署在边缘计算设备。
边缘计算设备与人工智能彼此赋能，催生“边缘智能”的崭新范式，给地震实时监测和预警等领域带来了深刻革命。
北京白家疃地球科学国家野外科学观测研究站以大数据技术方法为驱动，依托野外站丰富的数据优势资源，提供地震行业大数据算法和相关模型效果评估与检验的云资源环境。通过设计科研专项基金等形式，遴选和孵化具有发展潜力的地震大数据分析算法和相关模型的行业应用。“谛听”智慧盒子的研发就是野外站提供技术孵化环境，促成科技成果转化的布局与尝试。通过多年的努力，研发了“谛听”智慧盒子这一边缘计算设备。
经过近两年的技术更新和产品迭代，“谛听”智慧盒子集成了地震业务低代码开发平台环境DatistEQ Lite（DEQL），与中国地震局地震会商开发平台同源，打造出适用于中心站、无人值守台站等业务体量较少的软硬件一体化会商服务平台；此外，盒子内也部署了中国地震局地球物理研究所自主开发的地球物理开源软件，涵盖重力数据处理、位场正反演、地震目录分析、连续时间序列处理等多个领域，可以解决地球物理数据处理与解释领域的相关科研和生产问题；最后，盒子内置了基于地震科普大模型QuakeGPT的地震科普数字人“小Q”，在科普知识、震例分析、资料查询等方面，“小Q”展现出了相较主流通用大模型更加精准和专业的能力，为公众提供地震相关问题的解答，提供精准的学习与防灾建议，增强公众的参与度和学习效果，提高他们在日常和紧急情况下的应对能力。
谛听智慧盒子功能还将不断迭代升级，助力地震监测由数字化向智能化的方向转变。
中国地震局地球物理研究所
张博 陈石 赵明 吴德孟
附：“谛听”人工智能地震学训练数据集详情介绍见附件
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表 1 各种滤波分离方法对比
Table 1 Comparison of different filtering-based methods for wave separation.
方法名称 适用条件 处理域 优缺点 基于平面波解构滤波的散
射波分离方法适用于在倾角域形态或曲线特征上具有显著差异的各种波之间的分离。 叠前处理、叠后处理 优点是能够很好地压制反射波，实现散射波分离；缺点是计算成本较高。 基于扩散滤波的散射波分
离方法适用于在能量大小和分布具有显著差异的各种波之间的分离。 叠后处理 优点是可以不断调整扩散系数和迭代次数，有效提高散射波分离精度，提高了偏移剖面的信噪比；缺点是计算量较大。 基于倾角滤波的散射波分
离方法适用于在不同道集域（共中心点道集、共炮点道集、共偏移距道集）具有特征差异的各种波之间的分离。 叠前处理 优点是能够较完整地分离散射波；缺点是低倾角的散射波信息易失真或丢失。 基于偏移滤波的散射波分
离方法适用于在传播时差和传播路径具有特征差异的各种波之间的分离。 叠前处理 优点是在各个变换域的分辨率都得到提高；缺点是计算量较大，不能很好地处理混叠的波场。 基于F-K滤波的散射波分
离方法适用于在频率波数域和频率偏移距域具有特征差异的各种波之间的分离。 叠前处理 优点是去噪能力强、振幅保真性好，能够消除反射波，增强散射波；缺点是反射波消除不彻底，易破坏反射信息的波形特征和振幅特征。 
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