Yangkang Chen
Yangkang Chen is a seismologist and team lead for AI/ML research at the Texas Seismological Network (TexNet) at The University of Texas at Austin. He received a Ph.D. degree in geophysics from The University of Texas at Austin in 2015. From 2016 to 2018, he was a distinguished postdoctoral research associate with the Oak Ridge National Laboratory in Oak Ridge, Tennessee. His research interests include seismic signal analysis, seismic modeling and inversion, deep learning, and high-performance computing. Chen dedicates a significant amount of his time to open-source reproducible research, for instance, projects like Advanced Array Seismic data Processing and Imaging Platform , Chen's Github , and Chen's reproducible research .
After joining UT-Austin, Chen has been dedicated to the development of a mature and robust AI system from scratch for automatically picking the P- and S-wave arrival times, P-wave first-motion polarities and conducting the follow-up analysis to create automatic and highly complete seismic catalog and focal mechanisms of thousands of small (magnitude<1.5) earthquakes. Chen's AI system has been in production and now is the baseline model for TexNet analysts to conduct daily analysis and earthquake monitoring, which helps significantly reduce the manual workload and improve the analysis quality. An overview of Chen's AI-assisted real-time earthquake monitoring was given in the YouTube video: A journey to put AI in production .
Areas of Expertise
Chen is interested in computational methods that can be broadly applied to seismic signal analysis, seismic modeling and imaging, and earthquake monitoring. In the past ten years, his team's research has focused on applying artificial intelligence (AI) techniques to various geoscience problems. One particularly intriguing problem is to detect earthquakes that are likely induced by industrial injection activities from continuous waveforms of hundreds of broadband stations, which requires a precise picking of key seismic phases (e.g., P and S waves) and a sophisticated workflow for the association and location of the detected phases. Chen strongly promotes open-source reproducible research. Most of Chen's codes (EQCCT, EQpolarity, pyseistr, pyekfmm, pydrr, pyortho, pyseisdl, MATdrr, MATortho, MATseisdl, MATseistr, etc. ) developed over the past years can be found at Advanced Array Seismic data Processing and Imaging Platform and Chen's Github Many of Chen's papers are in a completely reproducible format (the whole package of Latex, Python, Matlab, C) at Chen's reproducible research Chen's representative AI/ML papers can be found at UTBox
Current Research Programs & Projects
Texas seismological network ( view )First Prize in GeoHackathon 2024 (Mentor) - The University of Texas at Austin (2024)
First Prize in AETA Earthquake forecast competition using AI - AETA&PekingUniversity; (2023 - 2023)
Top Cited Article Award on JGR-Solid Earth - American Geophysical Union (2023 - 2023)
IEEE Senior Membership - Institute of Electrical and Electronics Engineers (2022)
IEEE GRSL Best Reviewer Award - Institute of Electrical and Electronics Engineers (2021 - 2021)
Clarivate Highly Cited Researcher - Clarivate (2020 - 2020)
Distinguished Postdoctoral Fellowship - Oak Ridge National Laboratory (2016 - 2018)
BEG publication awards - Bureau of Economic Geology (2015)
Chevron Fellowship - The University of Texas at Austin (2014 - 2015)
Marathon Geophysical Fellowship - The University of Texas at Austin (2014 - 2015)
Member, BEG Grants, Appointments & Awards Committee Service (GAAC), Bureau of Economic Geology (2024)
Lead convener, Session ``Recent advances in forecasting/nowcasting and seismic hazard reduction" at the Japanese Geoscience Union (JpGU) Annual Meeting, Japan Geoscience Union (2024)
Lead convener, session ``Towards Advancing Earthquake Forecasting and Nowcasting: Recent Progress Using AI-Enhanced Methods" at the SSA Annual Meeting, Seismological Society of America (2024)
Member, AGU-JGR-Machine Learning and Computation EIC Search, American Geophysical Union (2024)
Editor, JGR: Machine Learning and Computation, American Geophysical Union (2023 - Present)
Lead convener, session ``Geophysical data analysis and inversion in the era of artificial intelligence" at the IEEE International Geoscience and Remote Sensing Symposium, IEEE Geoscience and Remote Sensing Society (2023)
Lead convener, session ``Beyond the Black BoxAdvancing Geo-ML by Incorporating Context with Specialized Architectures, Benchmark Datasets, and Tailored Notions of Interpretability" at the AGU Fall Meeting, American Geophysical Union (2023)
Lead organizer, AGU special collection (JGR-SE,GRL,ESS,JGR-MLC) ``Advanced machine learning in solid earth geoscience", American Geophysical Union (2023)
Associate Editor, IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers (2020 - Present)
Postdocs
Xing Li, 2024 - 2027, The University of Texas at Austin
Advanced artificial intelligence for InSAR data analysis
Jaewook Lee, 2022 - 2024, The University of Texas at Austin (now at University of Texas Permian Basin)
Topic: Deep learning for S-wave velocity model building
Omar M. Saad, 2019 - 2021, Zhejiang University (now at King Abdullah University of Science and Technology (KAUST))
Topic: Deep learning for advanced earthquake monitoring
Guangtan Huang, 2019 - 2021, Zhejiang University (now at Chinese Academy of Sciences)
Topic: Well-log-constrained pre-stack seismic inversion
Min Bai, 2018 - 2020, Zhejiang University (now at Yangtze University)
Topic: Time-domain Gaussian Beam
Graduate Students
Akshika Rohatgi , Ph.D., expected 2028 (Committee Member)
Rui Gong (Co-supervisor)
Shirley T Mensah
(Co-supervisor)
Shirley Mensah completed her BS in Geology and Professional Science Master's in Geographic Information Science and Cartography from Eastern Illinois University. After her graduation, she worked with various companies like Apple and Nicor Gas as a GIS Technician and Geospatial Analyst. Currently, she is a Ph.D. student working with Professor Sergey Fomel, and her interest includes machine learning and its...
Sujith Swaminadhan (Committee Member)
Shuang Gao, Ph.D.
(Co-supervisor)
Geological Sciences
Fangxue Zhang, Ph.D., 2025
(Co-supervisor)
Zhejiang University
Topic: Deep learning for induced seismicity
Liuqing Yang, Ph.D., 2024
(Co-supervisor)
China University of Petroleum - Beijing (now at Uppsala University)
Topic: Intelligent seismic data processing and inversion for enhanced reservoir characterization
Hang Wang, Ph.D., 2022
(Supervisor)
Zhejiang University (now at China University of Geoscience - Wuhan)
Topic: High-dimensional seismic data reconstruction and denoising
Chao Fu, Ph.D., 2022
(Co-supervisor)
Shandong University (now at Inner Mongolia University)
Topic: Predictive filtering in complex geological structure}
Quan Zhang, M.S., 2022
(Supervisor)
Zhejiang University (now at University of Ottawa)
Topic: High-resolution Radon transform in deep earth seismic imaging
Innocent Oboue, Ph.D., 2021
(Supervisor)
Zhejiang University
Topic: Five dimensional seismic denoising and reconstruction
Yuxiao Ren, Ph.D., 2021
(Co-supervisor)
Shandong University
Topic: Deep learning full waveform inversion
Xingye Liu, Ph.D., 2020
(Co-supervisor)
China University of Petroleum - Beijing (now at Chengdu University of Technology)
Topic: Deep learning for reservoir characterization
Undergraduate Students
Jiachen Hu (now at Zhejiang University)
Constantinos Skevofilax (now at University of Texas at Austin)
Matan Lebovits (now unknown)
Liuwei Xu (now at University of California, Los Angeles)
Pushing the limit of 5D interpolation using deep learning, Society of Exploration Geophysicists, Houston (2024)
Real-Time Earthquake Forecasting in China Using AI, Japan Geoscience Union, Chiba (2024)
Massive focal mechanism solutions from deep learning in West Texas, Society of Exploration Geophysicists, Houston (2024)
Benchmark dataset and framework accelerate AI research, Bureau of Economic Geology, Austin (2024)
Tuning a passive-seismic phase picker using TXED, European Association of Geoscientists and Engineers, Oslo (2024)
An Ai-Assisted Real-Time Earthquake Forecasting Case Study in China, Seismological Society of America, Anchorage (2024)
A journey to put AI in production for real-time earthquake monitoring, Bureau of Economic Geology, Austin (2024)
High-Resolution Mantle Transition Zone Imaging Using Multi-Dimensional Reconstruction of SS Precursors, Jackson School of Geosciences, Austin (2024)
AI-based fully automatic earthquake detection, magnitude estimation, data processing, and location, IEEE Geoscience and Remote Sensing Society, Pasadena (2023)
Graduate Positions
Students/postdocs on ML/AI-based earthquake data analysis
I am looking for self-motivated graduate students and postdocs who are interested in research on advanced earthquake data analysis using ML/AL.
News: AI-Driven Earthquake Forecasting Shows Promise in Trials
Bureau Team Wins International Earthquake Forecasting Competition
The Bureau of Economic Geology's Yangkang Chen led a team to win first place in the 2022 AETA Earthquake Prediction AI Algorithm Competition hosted by Peking University Shenzhen Graduate School in China, a competition that included 600 international teams.
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Advanced Array Seismic data Processing and Imaging Platform
We publish all of our computational seismic imaging codes in open-source repositories. Here is a platform for all our mature, ready-to-go, and highly scalable software packages developed over the past decades.