主讲人：Prof. Ping Ma（University of Georgia）

时  间：2014年5月23日（周五）15:30-16:30

地  点：必赢76net线路官网北二区教学楼 510 教室

摘  要：Earth's onion-like inner structure is composed of several layers: the crust, the mantle, the outer core, and the inner core. The deep earth's dynamic interior, which extends from the lowermost mantle at the depth of 2,890 km to the core center at the depth of 6,371 km, holds keys to understanding Earth's early state and how its biology, hydrology and atmosphere evolved and shaped the planet on which we now live. Probing the deep Earth is challenging. Direct sampling of Earth's deep interior through man-made probes and volcanism is impossible, due to the extreme pressures and temperatures. Man-made probes can barely descend to about 12 km, and volcanic eruption rarely brings up samples from a location deeper than 150 km. These distances are dwarfed by Earth's dimensions, leaving us only with indirect methods to learn Earth's deep interior. Our knowledge of Earth's deep interior, therefore, is pieced together from a range of surface observations through indirect methods. Among surface observations, seismic waves emitted by earthquakes are effective probes of Earth's deep interior and are relatively inexpensively recorded with networks of seismographs at Earth's surface. Recently, the rapid deployment of dense global seismograph networks brings an unprecedented amount of high resolution seismic data that were inaccessible just a decade ago, offering researchers an unprecedented opportunity to explore Earth's deep interior. In this talk, I will present some efforts in developing nonparametric methods, especially smoothing spline ANOVA models, to surmount two grand seismic challenges in detecting structure and dynamics of Earth's deep interior.

主讲人简介：Ping Ma is an associate professor of the Department of Statistics in University of Georgia. He was Beckman Fellow of the Center for Advanced Study in University of Illinois at Urbana-Champaign, Faculty Fellow at National Center for Supercomputing Applications, and a recipient of the National Science Foundation CAREER Award. His paper won the best paper award of Canadian Journal of Statistics in 2011. He serves on multiple editorial boards including Journal of the American Statistical Association, Journal of Statistical Planning and Inference, and Statistical Applications in Genetics and Molecular Biology.