PNNL

Abstract

We have developed and tested an algorithm, Bayesian Single Event Location (BSEL), for esti- mating the location of a seismic event. The estimation approach di*ers from established non-linear regression techniques by using a Bayesian prior probability density function (prior PDF) to incor- porate ancillary physical basis constraints about event location. P??wave arrival times from seismic events are used in the development. Depth, a focus of this paper, may be modeled with a prior PDF (potentially skewed) that captures physical basis bounds from ancillary event characteristics. For instance the surface wave Rg is present in a waveform only when an event is shallow. A high- condence Rg detection in one or more event waveforms implies a shallow-skewed prior PDF for the depth parameter. This PDF is constructed from a physically-based Rayleigh wave depth excitation eigenfunction that is based on the minimum period of observation from a spectrogram analysis and estimated near-source elastic parameters. The proposed Bayesian algorithm is illustrated with events that demonstrate its congruity with established hypocenter estimation methods and its application potential. The BSEL method is applied to the Mw 5.6 Dillon, MT earthquake of July 26, 2005 and a shallow Mw 4 earthquake that occurred near Bardwell, KY on June 6, 2003. In both cases we simulate BSEL on a small subset of arrival time data to illustrate the power of the technique. No Rg was actually observed for the Dillon, MT earthquake, but we used the minimum observed period of a Rayleigh wave (7 seconds) to reduce the depth and origin time uncertainty. A strong Rg was observed from the Bardwell, KY earthquake that places very strong constraints on depth and origin time.