摘要
Topic
Distributed Sensing Intelligence: Learning Across Wavefields in Fiber-Optic DAS
Abstracts
With its long spatial coverage and dense spatiotemporal sampling, fiber-optic distributed acoustic sensing (DAS) has seen growing adoption across monitoring and imaging applications. However, translating the resulting large-scale, high-dimensional time-series data into physical insight and actionable information remains a major challenge. This talk highlights recent examples of applying machine-learning approaches to DAS, focusing on how data-driven models can learn across coupled mechanical and optical wavefields to improve signal interpretation, modeling, and inference.
Biography
Weichang LI is Chair Professor at Zhejiang University, with joint appointment at the College of Information Science and Electronic Engineering and the Ocean College. He received dual M.S. degrees in Electrical Engineering and Computer Science, and in Ocean Engineering, and a Ph.D. in Electrical and Oceanographic Engineering from the Massachusetts Institute of Technology (MIT). He was an Office of Naval Research Postdoctoral Fellow at the Woods Hole Oceanographic Institution.
Prior to joining Zhejiang University, Weichang led the Machine Learning and Data Analytics group at ExxonMobil’s Corporate Strategic Research Laboratory and later founded and directed the AI Technology Group at Aramco Americas’ Houston Research Center.
He has published over 130 papers and holds more than 40 patents. His research focuses on machine learning/AI and signal processing for geoscience, ocean monitoring, and energy applications.
