Lawrence Livermore National Laboratory



MCMC sampling
Download open-source version (BayeslocRelease2011.tar.gz is ~45 Mbytes)

What does Bayesloc do?

Given a set of seismic arrivals for one or more events, Bayesloc estimates the joint probability of event locations, corrections to travel time predictions, precision of arrival time measurements, and phase labels for the arrival times. Bayesloc also accepts probabilistic prior constraints on any of the input parameters, which can significantly tighten the distribution of all parameters.

Documentation

The Bayesloc download includes a users guide, including a "Quick Start" that describes rudimentary usage. Input and output file formats are specified.

Code

Bayesloc is a C++ code. Bayesloc utilizes a number of open source libraries, all of which are included in the Bayesloc download. Bayesloc is known to work on Unix-based systems: Linux, OS-X, Solaris. Uses are free to port Bayesloc to other systems.

Bayesloc Utilizes the Markov-Chain Monte Carlo (MCMC) Method

Most seismic locators solve a system of linear equations to determine a point solution. Uncertainties are then estimated by linearly mapping data uncertainties to the event location(s).

The MCMC method samples the joint probability function. The MCMC samples are non-parametric and therefore capture non-linear attributes of the solution. A point estimate of any parameter (location, travel time correction, etc.) may be computed by finding the mean, mode, or median of the MCMC samples. Likewise, conventional uncertainty estimates, e.g. confidence bounds, can be computed by using the MCMC samples and assuming that the samples are drawn from a specified analytical statistical distribution. Bayesloc output includes MCMC samples, point estimates of locations (hypocenters), and second-order statistics assuming the samples are distributed Gaussian.

Examples

MCMC Samples

Point Density

Gaussian Representation

Bayesloc is developed in the following publications and we hope you will cite them if you find Bayesloc useful

Myers, S.C., G. Johannesson, and W. Hanley, "A Bayesian hierarchical method for multiple-event seismic location," Geophys. J. Int., 171, 1049-1063, 2007.

Myers, S.C., G. Johannesson, and W. Hanley, "Incorporation of probabilistic seismic phase labels into a Bayesian multiple-event seismic locator," Geophys. J. Int., 177, 193–204, 2009.

Bayesloc has also been used in development of tomographic data sets

Myers, S.C., G. Johannesson, and N.A. Simmons, "Global-scale P-wave tomography optimized for prediction of teleseismic and regional travel times for Middle East events: 1. Data set Development," Jour, Geophys. Res., 116, B04304, doi:10.1029/2010JB007967, 2011.

Simmons, N.A, S.C. Myers, G. Johannesson, and E. Matzel, "LLNL-G3Dv3: Global P-wave tomography model for improved regional and teleseismic travel time prediction," Jour, Geophys. Res., doi:10.1029/2012JB009525, 2012.

Bayesloc has been developed under an open source software license.

Download software license