摘要
最小二乘逆时偏移成像方法因计算量巨大,限制了其大规模的工业应用。基于此,建立循环对抗神经网络表征Hessian矩阵的逆,构建逆时偏移结果和反射系数之间的映射关系。通过建立的神经网络对逆时偏移成像结果进行去模糊化处理,提高成像质量,同时大幅减少计算时间。将训练好的网络应用于Marmousi模型和Sigsbee2A模型的逆时偏移结果。结果表明,本文方法在不显著增加计算量的情况下较好地提高了逆时偏移成像质量。
The high computational costs of the least-squares iterative solution limit the large-scale industrial application of the least-squares reverse time migration(LSRTM)method.The difference between traditional reverse time migration(RTM)and least-squares reverse time migration is whether to solve the inverse Hessian matrix or not.This paper proposes a solution by simulating the inverse of the Hessian matrix using a cycle-consistent adversarial neural network(cycleGAN).The network constructs a mapping relationship between the reverse time migration and high-precision imaging,improving imaging quality while significantly reducing computation costs.The trained network is applied to the reverse time migration results of the Marmousi model and the Sigsbee2A model,and the imaging results obtained from the network prediction demonstrate that this method improves the offset imaging quality better with almost no increase in computational effort.
作者
黄韵博
黄建平
李振春
刘博文
HUANG Yunbo;HUANG Jianping;LI Zhenchun;LIU Bowen(School of Geosciences in China University of Petroleum(East China),Qingdao 266580,China)
出处
《中国石油大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2023年第3期55-61,共7页
Journal of China University of Petroleum(Edition of Natural Science)
基金
国家创新群体项目(41821002)
优秀青年科学基金项目(41922028)
“十四五”重大项目(2021QNLM020001)
国家重点研发计划项目(2019YFC0605503C)
中石油重大科技项目(ZD2019-183-003)。
关键词
逆时偏移
最小二乘
HESSIAN矩阵
循环对抗神经网络
reverse time migration
least-squares
Hessian matrix
cycle-consistent adversarial neural network
