Advances in Mixed Signal Processing for Regional and Teleseismic Arrays

摘要

This project considers possible approaches to resolving mixtures of propagating signals observed on arrays. In particular, conventional approaches such as beam-forming, MUSIC and single-signal F-statistics have flaws that will not adapt to certain mixtures. In order to solve this problem, we derive the partial F-statistic for testing for an added signal in a multiple-signal model. In this case a combination of sequential nonlinear partial F-statistics in combination with Akaike's corrected model selection criterion AICC leads to determining the correct configuration of signals and their velocities and azimuths. The conventional estimators and the new estimators are applied to known and unknown configurations of regional signals from China and to a teleseismic mixture involving two known earthquakes and noise caused by an ocean storm. We also analyze a regional event with propagating noise and show that a deconvolution based on the two velocities and azimuths gives an enhanced view of the depth phase. Software as well as internal and external documentation is provided in the form of MATLAB subroutines that can be incorporated into government research tools such as MATSEIS. We provide software for (1) time-frequency analysis, (2) F, MUSIC and Capon detectors, (3) multiple signal analysis, (4) bootstrap confidence intervals and (5) deconvolution.