Preprint
Forward and inverse problems for Eikonal equation based on DeepONet
ArXiv.org
06/09/2023
DOI: 10.48550/arxiv.2306.05754
Abstract
Seismic forward and inverse problems are significant research areas in geophysics. However, the time burden of traditional numerical methods hinders their applications in scenarios that require fast predictions. Machine learning-based methods also have limitations as retraining is required for every change in initial conditions. In this letter, we adopt deep operator network (DeepONet) to solve forward and inverse problems based on the Eikonal equation, respectively. DeepONet approximates the operator through two sub-networks, branch net and trunk net, which offers good generalization and flexibility. Different structures of DeepONets are proposed to respectively learn the operators in forward and inverse problems. We train the networks on different categories of datasets separately, so that they can deliver accurate predictions with different initial conditions for the specific velocity model. The numerical results demonstrate that DeepONet can not only predict the travel time fields with different sources for different velocity models, but also provide velocity models based on the observed travel time data.
Details
- Title: Subtitle
- Forward and inverse problems for Eikonal equation based on DeepONet
- Creators
- Yifan MeiYijie ZhangXueyu ZhuRongxi Gou
- Resource Type
- Preprint
- Publication Details
- ArXiv.org
- DOI
- 10.48550/arxiv.2306.05754
- ISSN
- 2331-8422
- Language
- English
- Date posted
- 06/09/2023
- Academic Unit
- Mathematics
- Record Identifier
- 9984433850102771
Metrics
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