Detecting Regional Events via Statistical Analysis of Geodetic Networks

We present an application of hidden Markov models (HMMs) to analysis of geodetic time series in Southern California. Our model-fitting method uses a regularized version of the deterministic annealing expectation-maximization algorithm to ensure that model solutions are both robust and of high quality. Using the fitted models, we segment the daily displacement time series collected by 127 stations of the Southern California Integrated Geodetic Network (SCIGN) over a two-year period. Segmentations of the series are based on statistical changes as identified by the trained HMMs. We look for correlations in state changes across multiple stations that indicate region-wide activity. We find that although in one case a strong seismic event was associated with a spike in station correlations, in all other cases in the study, time period strong correlations were not associated with any seismic event. This indicates that the method was able to identify more subtle signals associated with aseismic events or long-range interactions between smaller events.