有效压制地震多次波是地震资料处理过程中的重要环节,尽管已有多种多次波压制方法,但是依然存在多次波压制不充分、计算量大的问题,对于复杂地质条件下的多次波压制更具挑战.传统的多次波压制方法依赖先验地质构造信息且需要大量的计算,对于复杂地质产生的多次波压制效果较差,且计算速度较低.使用深度神经网络压制多次波,能有效降低人工成本,同时还可以提高多次波的压制效率.本文提出一种改进的U-net网络多次波压制方法,采用U-net作为主要网络,在网络中加入注意力局部对比度(Atentional Local Contrast,ALC)模块,该模块有利于对地震数据中细节信息进行合理处理,突出地震多次波与一次波之间的差异.将含有多次波和一次波的地震数据作为输入,把只含一次波的地震数据作为输出,对本文提出的网络进行训练.通过两个水平层状速度模型和sigsbee2B速度模型验证了本文方法在多次波压制中的有效性和稳定性,利用迁移学习使得训练后的模型具有跨工区压制多次波的能力,有效提高了多次波压制效率.
Seismic multiple attenuation based on improved U-Net
Effective attenuation of seismic multiples is a crucial step in the seismic data processing workflow.Despite the existence of various methods for multiple attenuation,challenges persist,such as incomplete attenuation and high computational requirements,particularly in complex geological conditions.Conventional multiple attenuation methods rely on prior geological information and involve extensive computations.Using deep neural networks for multiple attenuation can effectively reduce manual labor costs while improving the efficiency of multiple suppression.This study proposes an improved U-net-based method for multiple attenuation.The conventional U-net serves as the primary network,incorporating an attentional local contrast module to effectively process detailed information in seismic data.Emphasis is placed on distinguishing between seismic multiples and primaries.The improved network is trained using seismic data containing both multiples and primaries as input and seismic data containing only primaries as output.The effectiveness and stability of the proposed method in multiple attenuation are validated using two horizontal layered velocity models and the Sigsbee2B velocity model.Transfer learning is employed to endow the trained model with the capability to suppress multiples across seismic exploration areas,effectively improving multiple attenuation efficiency.
Multiple suppressionU-netAttentional local contrast
张全、吕晓雨、雷芩、彭博、李艳、Yao-wen Zhang
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西南石油大学计算机科学学院,成都 610500
油气藏地质及开发工程国家重点实验室(西南石油大学),成都 610500
School of Computer Science and Software Engineering,Southwest Petroleum University,Chengdu 610500,China
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