Global-scale multiple-event location and travel-time analysis using BayesLoc

We have constructed a global-scale seismic arrival-time data set that consists of over 3.5 million arrivals from over 13 thousands events, with arrivals from multiple phases. The data set was constructed to yield about 1 degree spacing between well recorded events at multiple depth levels. We analyze the data set using BayesLoc, a Bayesian multiple-event locator, which simultaneously provides a probabilistic characterization of the unknown hypocenters, along with travel-time corrections, pick precisions, and phase-label assignments, including outlier detection. BayesLoc has been successfully applied to regional data sets, but the regional implementation lacks capable and flexible statistical travel-time correction model for realistic analysis of the global-scale data set of interest here. We present the needed extension to BayesLoc for global analysis and apply the enhanced BayesLoc to the global data set of interest and provide analysis of the resulting hypocenters, travel-time corrections, and the accuracy of the arrival data. [Prepared by LLNL under Contract DE-AC52-07NA27344. LLNL-ABS-452271]