基于卷积神经网络的地震偏移剖面中散射体的定位和成像  

Location and imaging of scatterers in seismic migration profiles based on convolution neural network

作　　者：奚先 黄江清 XI Xian;HUANG JiangQing(China University of Geosciences(Wuhan),School of Mathematics and Physics,Wuhan 430074,China;China University of Geosciences(Wuhan),Wuhan 430074,China)

机构地区：[1]中国地质大学(武汉)数学与物理学院

出　　处：《地球物理学报》2020年第2期687-714,共28页Chinese Journal of Geophysics

基　　金：国家自然科学基金项目(41630643,11702259,61601417)联合资助

摘　　要：本文提出了一种基于深度学习卷积神经网络(CNN)的全波形反演方法,可对地震散射波场中的散射体进行成像和定位.本文的灵感来自如下猜想:在散射波场剖面上的每个点附近的局部波场与该点到各散射体之间的最小距离有关系,并且这个关系可以被CNN网络所识别.我们将该最小距离定义为散射距离场,并将散射距离场的类别(即大小等级)作为CNN网络的预期输出,而输入就是该点附近的局部波场.最后用上述CNN网络对散射波场进行逐点训练和识别.计算结果证实了我们的灵感猜想,即上述CNN网络能够在复杂散射波场中对散射体进行成像.只通过一个训练模型的学习,CNN网络即可反演多种散射模型的偏移剖面,最后得到"类别函数预测值"和"滤波剖面"两种成像结果,由此可以辨识出在复杂的偏移剖面中各散射体的位置.In this paper,a full waveform inversion method based on deep learning Convolution Neural Network(CNN)is proposed,which can image and locate scatterers in the seismic scattered wave field.The inspiration of this paper comes from the following conjecture:the local wave field near each point in a scattered wave field profile is related to the minimum distance between the point and every scatterer,and this relationship can be identified by CNN network.We define the minimum distance as the scattering distance field,and take the category of the scattering distance field(i.e.the size level)as the expected output of the CNN network,with input as the local wave field near the point.Finally,the scattered wave field is trained and identified point by point using the above CNN network.The calculation results confirm our speculation that the above CNN network can image scatterers in complex scattering wave fields.By learning only one training model,the migration profiles of various scattering models can beinverted by CNN network.Finally,two imaging results of"category function prediction value"and"filter profile"care obtained.Thus,the positions of scatterers in complex migration profiles can be identified.

关 键 词：散射波场反演成像 卷积神经网络 散射距离场 类别函数 

分 类 号：P631[天文地球—地质矿产勘探]