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- Title
基于循环对抗神经网络的快速最小二乘逆时 偏移成像方法.
- Authors
黄韵博; 黄建平; 李振春; 刘博文
- Abstract
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.
- Subjects
HESSIAN matrices; MATRIX inversion; INDUSTRIAL applications; COST
- Publication
Journal of China University of Petroleum, 2023, Vol 47, Issue 3, p55
- ISSN
1673-5005
- Publication type
Article
- DOI
10.3969/j.issn.1673-5005.2023.03.006