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About
President
Governance
Partner Institutions
Visit
People
Management
Faculty
Postdocs
Visiting Scholars
Administration
Academic Support
Research
Research Groups
Courses
Seminars
Join Us
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Forum
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Qiuzhen College, Tsinghua University
Yau Mathematical Sciences Center, Tsinghua University (YMSC)
Tsinghua Sanya International  Mathematics Forum (TSIMF)
Shanghai Institute for Mathematics and  Interdisciplinary Sciences (SIMIS)
BIMSA > BIMSA Digital Economy Lab Seminar Seismic wave propagation modeling and parameter inversion using machine learning assisted approaches
Seismic wave propagation modeling and parameter inversion using machine learning assisted approaches
Organizers
Ruize Gao , Liyan Han , Zhen Li , Fei Long , Dongbo Shi , Ke Tang , Qi Zhang
Speaker
Fansheng Xiong
Time
Friday, October 24, 2025 3:00 PM - 4:00 PM
Venue
A3-2-303
Online
Zoom 435 529 7909 (BIMSA)
Abstract
Currently, “AI for Science” is driving a paradigm shift in scientific community, which has significantly impacted the study of forward and inverse problems in exploration geophysics. The main content of this talk is as follows: First, it introduces a model establishing approach that build a framework based on existing knowledge and then predicts unknown factors using machine learning. Next, it presents examples of establishing wave propagation equations and the simplification assisted by machine learning, and the effectiveness of the approach is preliminarily validated by experimental and logging data. Finally, it demonstrates the application in reservoir parameter inversion and joint seismic-resistivity parameter inversion. The approaches and examples discussed in this talk aim to make meaningful explorations into key issues in exploration geophysics.
Speaker Intro
Fansheng Xiong (熊繁升) is currently an Assistant Professor of BIMSA. Before that, he received his doctoral degree in 2020 from Tsinghua University, and he was a visiting research assistant at Yale University during 2018-2019. His research interest mainly focuses on solving forward/inverse problems of PDEs based on machine learning method, and the application of PINN, DeepONet, Reduced Order Model in exploration geophysics, especially seismic rock physics. He is PI for grant from National Natural Science Foundation of China, and he has published papers in journals like Journal of Geophysical Research-Solid Earth, Geophysical Journal International, and Geophysics.
Beijing Institute of Mathematical Sciences and Applications
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