Characterizing source regions with signal subspace methods: Theory and computational methods.

摘要

A mathematical approach is developed for empirically characterizing a given source region using waveforms from a collection of calibration events. A region is considered to be adequately characterized if the waveforms from any event in the source region can be represented as a linear combination of calibration event waveforms. The purpose of such characterizations is to build waveform ''recognizers'' for specific regions for precision location applications, and to provide a means of separating superimposed waveforms from multiple events in different source regions. The particular form of characterization used is insensitive to variations in the source time function and to anything but changes from the normal range of source mechanisms encountered in the source region. The standard waveform correlation coefficient used to estimate event clustering is generalized to estimate separation between single events and event clusters, and between two clusters of events. The generalized correlation coefficient is insensitive to variations in source time function and, to some extent, mechanism. The statistics of waveform correlation coefficients are developed, and show that conventional estimates made from single station data are often developed for network or array data removes the ambiguity. 23 refs., 4 figs.