2018年海洋信息论坛第30期
报告题目：Physics-based coastal current tomography
报告人：Prof. T.C.Yang
时间：7月31日 下午14:00-16:00
地点：水声楼315会议室
报告人简介：
T. C. Yang received the Ph.D. degree in high energy physics from the University of Rochester, Rochester, NY, USA, in 1971. He is currently a Professor and previously a Pao Yu-Kong Chair Professor at the Zhejiang University. From 2012 to 2014, he was a National Science Counsel Chair Professor at the Nat. Sun Yat-Sen Univ. Kaohsiung, Taiwan. Before that, he spent 32 years working at the Naval Research Laboratory, Washington, DC, serving as Head of the Arctic Section, Dispersive Wave Guide Effects Group, and acting Head of the Acoustic Signal Processing Branch, and consultant to the division on research proposals. His current research focuses on: (1) environmental impacts on underwater acoustic communications and networking, exploiting the channel physics to characterize and improve performance, (2) environmental acoustic sensing and signal processing using distributed networked sensors, and (3) methods for improved channel tracking and data-based source localization. In earlier years, he pioneered matched mode processing for a vertical line array, and matched-beam processing for a horizontal line array. Other areas of research included geoacoustic inversions, waveguide invariants, effects of internal waves on sound propagation in shallow water, Arctic acoustics, etc. He is a fellow of the Acoustical Society of America.
报告简介：
Ocean acoustic tomography (OAT) can be used, based on measurements of two-way travel-time differences between the nodes deployed on the perimeter of the surveying area, to invert/map the ocean current inside the area. Data at different times can be related using Kalman filter, and given an ocean circulation model, one can in principle now cast and even forecast current distribution given an initial distribution and/or the travel-time difference data on the boundary. However, an ocean circulation model requires many inputs (many of them often not available) and is unpractical for estimation of the current field. A simplified form of the discretized Navier-Stokes equation is used to show that the future velocity state is just a weighted spatial average of the current state. These weights could be obtained from an ocean circulation model, but here in a data driven approach, auto-regressive methods are used to obtain the time and space dependent weights from the data. It is shown, based on simulated data, that the current field tracked using a Kalman filter (with an arbitrary initial condition) is more accurate than that estimated by the standard methods where data at different time are treated independently. Real data are also examined.
This talk will review the basics and point out some interesting oceanographic findings from simulated and real ocean data for future explorations.