Deep Learning Methods and Applications to Geophysics
Deep learning (DL) methods have been widely applied in various fields. This course focuses on employing neural network (NN)-based methods to solve forward and inverse problems of geophysics, especially seismic rock physics. We first introduce some methods and approaches summarized by the lecturer based on published literature, and then introduce their applications in geophysics, including background knowledge, methods and numerical examples. There will be interactive time in each class, and all attendees are welcome to ask questions and communicate with the lecturer.
讲师
日期
2025年10月14日 至 12月30日
位置
| Weekday | Time | Venue | Online | ID | Password |
|---|---|---|---|---|---|
| 周二 | 13:30 - 16:55 | A3-1-301 | ZOOM 06 | 537 192 5549 | BIMSA |
修课要求
Basic knowledge on deep neural networks, geophysics, partial differential equations, and the Python language.
课程大纲
1. Introduction to neural network (NN)-based approaches, some important knowledge points
NN-based Surrogate Modeling Methods:
2. Time stepping operator based on different network architectures
3. Different neural ODE benchmark models and the involved constraints
4. Learning explicit/implicit models from data
Several NN-based Methods:
5. Physics-informed neural network (PINN)
6. Deep neural operator (DeepONet)
7. Reduced order modeling (ROM)
8. Other new methods
The Application in Geophysics Problems:
9. Seismic rock physics
10. Joint inversion of seismic and electromagnetic methods
11. Full waveform inversion
12. Some review literatures, communication, and interaction
NN-based Surrogate Modeling Methods:
2. Time stepping operator based on different network architectures
3. Different neural ODE benchmark models and the involved constraints
4. Learning explicit/implicit models from data
Several NN-based Methods:
5. Physics-informed neural network (PINN)
6. Deep neural operator (DeepONet)
7. Reduced order modeling (ROM)
8. Other new methods
The Application in Geophysics Problems:
9. Seismic rock physics
10. Joint inversion of seismic and electromagnetic methods
11. Full waveform inversion
12. Some review literatures, communication, and interaction
参考资料
1. Some methods and approaches summarized from published literature.
2. Published literature related to deep learning for solving differential equations, and the application in geophysics.
2. Published literature related to deep learning for solving differential equations, and the application in geophysics.
听众
Graduate
, 博士后
视频公开
不公开
笔记公开
不公开
语言
中文
讲师介绍
熊繁升，现任北京雁栖湖应用数学研究院助理研究员，曾任北京应用物理与计算数学研究所所聘博士后。先后毕业于中国地质大学(北京)、清华大学，美国耶鲁大学联合培养博士。研究兴趣主要集中于基于机器学习算法(DNN、PINN、DeepONet等)求解微分方程模型正/反问题及其在地球物理波传播问题中的应用，相关成果发表在JGR Solid Earth、GJI、Geophysics等期刊上。