CAREER: Subsampling Methods in Statistical Modeling of Ultra-Large Sample Geophysics

University Of Illinois At Urbana-Champaign, Urbana IL

Investigators

Abstract

Remote sensing of the Earth's deep interior is challenging. Direct sampling of the Earth's deep interior is impossible due to the extreme pressures and temperatures. Our knowledge of the Earth?s deep interior is thus pieced together from a range of surface observations. Among surface observations, seismic waves emitted by earthquakes are effective probes of the Earth?s deep interior and are relatively inexpensively recorded by networks of seismographs at the Earth's surface. Unprecedented volumes of seismic data brought by dense global seismograph networks offer researchers both opportunities and challenges to explore the Earth?s deep interior. The key challenge is that directly applying statistical methods to this ultra-large sample seismic data using current computing resources is prohibitive. To facilitate geophysical discoveries that can enhance our understanding of the Earth?s deep interior using current computing resources, the investigator proposes a family of novel statistical methods under a subsampling framework. The proposed methods provide an opportunity to study various distinct statistical problems, such as function estimation and variable selection, in a unified framework. The investigator will establish asymptotic and finite sample theory to investigate the approximation accuracy and consistency of the proposed methods. How to analyze ultra-large sample data creates a significant challenge in almost all fields of science and engineering. Scientists and engineers develop various solutions to tackle the problem, such as developing cloud computing for aggregating a wide range of computing resources and building powerful supercomputers. However, the high cost of these solutions creates an extraordinary budget barrier for researchers. The proposed subsampling methods provide alternative methods to surmount this challenge. The theory to be established will benefit a wide spectrum of research in science and engineering. They will offer a unique educational experience for both undergraduate and graduate students to participate in cutting-edge statistical and interdisciplinary research and inspire new lines of researches in three distinct fields: statistics, geophysics, and computational biology.