HR: 0800h
AN: S51B-1008    [Abstracts]
TI: Multiple-Event Seismic Location Using the Markov-Chain Monte Carlo Technique
AU: * myers, s C
EM: smyers@llnl.gov
AF: Lawrence Livermore National Laboratory, PO box 808 L-205, Livermore, CA 94551 United States
AU: Johannesson, g
S51B-1008 AF: Lawrence Livermore National Laboratory, PO box 808 L-205, Livermore, CA 94551 United States
AU: Hanley, W
S51B-1008 AF: Lawrence Livermore National Laboratory, PO box 808 L-205, Livermore, CA 94551 United States
AB: We develop a new multiple-event location algorithm (MCMCloc) that utilizes the Markov-Chain Monte Carlo (MCMC) method. Unlike most inverse methods, the MCMC approach produces a suite of solutions, each of which is consistent with observations and prior estimates of data and model uncertainties. Model parameters in MCMCloc consist of event hypocenters, and travel-time predictions. Data are arrival time measurements and phase assignments. Posteriori estimates of event locations, path corrections, pick errors, and phase assignments are made through analysis of the posteriori suite of acceptable solutions. Prior uncertainty estimates include correlations between travel-time predictions, correlations between measurement errors, the probability of misidentifying one phase for another, and the probability of spurious data. Inclusion of prior constraints on location accuracy allows direct utilization of ground-truth locations or well-constrained location parameters (e.g. from InSAR) that aid in the accuracy of the solution. Implementation of a correlation structure for travel-time predictions allows MCMCloc to operate over arbitrarily large geographic areas. Transition in behavior between a multiple-event locator for tightly clustered events and a single-event locator for solitary events is controlled by the spatial correlation of travel-time predictions. We test the MCMC locator on a regional data set of Nevada Test Site nuclear explosions. Event locations and origin times are known for these events, allowing us to test the features of MCMCloc using a high-quality ground truth data set. Preliminary tests suggest that MCMCloc provides excellent relative locations, often outperforming traditional multiple-event location algorithms, and excellent absolute locations are attained when constraints from one or more ground truth event are included. When phase assignments are switched, we find that MCMCloc properly corrects the error when predicted arrival times are separated by several seconds. In cases where the predicted arrival times are within the combined uncertainty of prediction and measurement errors, MCMCloc determines the probability of one or the other phase assignment and propagates this uncertainty into all model parameters. We find that MCMCloc is a promising method for simultaneously locating large, geographically distributed data sets. Because we incorporate prior knowledge on many parameters, MCMCloc is ideal for combining trusted data with data of unknown reliability. This work was performed under the auspices of the U.S. Department of Energy by the University of California Lawrence Livermore National Laboratory under contract No. W-7405-Eng-48, Contribution UCRL-ABS-215048
DE: 7203 Body waves
DE: 7219 Seismic monitoring and test-ban treaty verification
DE: 7240 Subduction zones (1207, 1219, 1240)
SC: Seismology [S]
MN: Fall Meeting 2005