| Autor: |
Shi Yao, Jue Hou, Yuepeng Huang, Tao Xu, Zhiming Bai, Zhenghui Gao |
| Jazyk: |
čínština |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
地球与行星物理论评, Vol 54, Iss 1, Pp 81-90 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2097-1893 |
| DOI: |
10.19975/j.dqyxx.2021-049 |
| Popis: |
Seismic wave traveltime calculation plays an important role in many areas of seismology, such as seismic tomography, migration and microseismic location. Solving the eikonal equation with the finite difference method is an essential method for calculating traveltime. The conventional method of solving the eikonal equation needs to calculate the traveltime field of each source. As the number of grids increases, it will consume a lot of time and memory. We introduce the EikoNet based on a deep neural network. Its samples are generated by sampling in the three-dimensional space, using the given velocity model as labels to optimize the network. Furthermore, it can transmit information about seismic wavefield and velocity structure during calculation and is highly suited for GPU. The EikoNet can quickly determine the traveltime between any two points in a three-dimensional domain without meshes, significantly improving calculation efficiency and reducing memory consumption. Numerical experiments of the EikoNet and the fast marching method (FMM) on several velocity models show that the EikoNet has higher efficiency while maintaining high accuracy. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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