Advances in Mixed Signal Processing for Regional and Teleseismic Arrays

This project is aimed at applying recently modified array signal processing techniques to the problem of detecting and estimating mixtures of signals observed on teleseismic and regional arrays. We are developing new techniques for enhancing both signal detection and estimation of azimuth and phase velocity parameters. In particular, we seek an automated detection procedure that sequentially isolates signals in an unknown mixture and provides estimators and confidence intervals for both the propagation parameters and the number of signals. The methodology is an extension of the standard F-detector which is well known to be based on a nonlinear regression model that tests for the presence of a single signal as a function of slowness parameters. Formulating the multiple signal model as a test of hypothesis for the presence of the most recently added signal, we are able to develop a sequential procedure analogous to stepwise multiple regression by adding signals until no further additions are statistically significant. Both the sequential F-tests and versions of AIC and squared error are monitored to arrive at the final model. A frequency domain bootstrap procedure provides estimators for the standard errors of the estimated velocities and azimuths of the component signals. Current tests of the methodology have focused on a verifiable mixture of two simultaneously occurring earthquakes observed at the United States Atomic Energy Detection System (USAEDS) long-period seismic array in Korea and a contrived short-period mixture of two regional events. Conventional methods such as single-signal F, MUSIC and high resolution detectors arrive at the incorrect azimuths for this data whereas the multiple-signal F-detector finds the correct number of signals and identifies their propagation characteristics. 463 27th Seismic Research Review: Ground-Based Nuclear Explosion Monitoring Technologies