Computational Mathematics and Scientific Computing Seminar
Scalable algorithms for environmental seismology
Speaker: Eileen Martin, Colorado School of Mines
Date: Nov. 12, 2021, 10 a.m.
Over the past two decades, a method called ambient noise interferometry has revolutionized subsurface seismic imaging for environmental, infrastructure and safety purposes. Ambient noise interferometry is based on cross-correlation of many time series to explore potential time-lagged relationships between them, often indicating an estimate of a Green's function in an area. This requires significant data movement, and new seismic sensor technologies are leading to rapidly growing sensor arrays. In this talk I will show some new methods to calculate array-wide cross-correlations that take advantage of lossy data compression to reduce data movement and computational costs by performing cross-correlations directly on compressed data. Seismologists often use the cross-correlation results as an input to a few types of array beamforming methods (similar to beamforming used in wireless communications and astronomy). In fact, we can calculate these final beamforming results directly from the ambient seismic noise with new linear algorithms that never explicitly calculate cross-correlations.