基于注意力机制的层间多次波压制方法  

Internal multiple elimination method based on attention mechanism

作　　者：包培楠 王维红[1] 李芷薇 张斯奇 BAO PeiNan;WANG WeiHong;LI ZhiWei;ZHANG SiQi(School of Earth Sciences,Northeast Petroleum University,Daqing 163318,China)

机构地区：[1]东北石油大学地球科学学院,大庆163318

出　　处：《地球物理学进展》2024年第4期1474-1482,共9页Progress in Geophysics

基　　金：国家青年科学基金项目(42304114);黑龙江省自然科学基金项目(LH2023D014);国家自然科学基金面上项目(42274167);国家基金培育基金项目(2023GPL-12)联合资助。

摘　　要：地震资料层间多次波压制一直是油气勘探领域的研究热点和难点.地震波经过地下强反射界面会形成能量较强的层间多次波,严重影响有效波的识别,使地震成像的真实性及可靠性降低.基于深度学习的多次波压制方法能够通过组合低层特征形成更加抽象的高层特征以更好的发现数据的有效信息,多次波分离精度较高.本文针对传统卷积神经网络训练成本高的问题引入注意力机制,提出了基于注意力机制的层间多次波压制方法,以降低神经网络模型训练成本.数据测试表明,该方法不受传统层间多次波压制方法局限性的影响,可以避免地震数据的规则化处理,降低了计算负担,具有重要的理论与工业应用价值.Internal multiple suppression of seismic data has been a research hotspot and difficulty in the field of oil and gas exploration.The strong reflection interface in the subsurface will form internal multiples with strong energy,which seriously affect the identification of primaries.It also can reduce the authenticity and reliability of seismic imaging.The deep learning-based multiple suppression method can form more abstract high-level features by combining the low-level features to better discover the effective features of the data,and the multiple separation accuracy is high.In this paper,the attention mechanism is introduced for the problem of high training cost of traditional convolutional neural network,and an internal multiple suppression method based on the attention mechanism is proposed to reduce the training cost of neural network model.The data test shows that the method is not affected by the limitations of the traditional internal multiple suppression method and can avoid the regularization of seismic data,thus reducing the computational burdens and improving the computational efficiency,which has important theoretical and industrial application value.

关 键 词：层间多次波压制 深度学习 神经网络 注意力机制 

分 类 号：P631[天文地球—地质矿产勘探]