Analysis of streaming GPS measurements of surface displacement through a web services environment

We present a method for performing mode classification of real-time streams of GPS surface position data. Our approach has two parts: an algorithm for robust, unconstrained fitting of hidden Markov models (HMMs) to continuous-valued time series, and SensorGrid technology that manages data streams through a series of filters coupled with a publish/subscribe messaging system. The SensorGrid framework enables strong connections between data sources, the HMM time series analysis software, and users. We demonstrate our approach through a Web portal environment through which users can easily access data from the SCIGN and SOPAC GPS networks in Southern California, apply the analysis method, and view results. Ongoing real-time mode classifications of streaming GPS data are displayed in a map-based visualization interface

[1]  H. Dragert,et al.  Episodic Tremor and Slip on the Cascadia Subduction Zone: The Chatter of Silent Slip , 2003, Science.

[2]  Matthew Brand,et al.  Structure Learning in Conditional Probability Models via an Entropic Prior and Parameter Extinction , 1999, Neural Computation.

[3]  Hiromichi Tsuji,et al.  Silent fault slip following an interplate thrust earthquake at the Japan Trench , 1997, Nature.

[4]  Volker Tresp,et al.  Improved Gaussian Mixture Density Estimates Using Bayesian Penalty Terms and Network Averaging , 1995, NIPS.

[5]  Timothy Ian Melbourne,et al.  Rapid postseismic transients in subduction zones from continuous GPS , 2002 .

[6]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[7]  Kenneth W. Hudnut,et al.  THE SOUTHERN CALIFORNIA INTEGRATED GPS NETWORK (SCIGN) , 2001 .

[8]  Yehuda Bock,et al.  Instantaneous geodetic positioning at medium distances with the Global Positioning System , 2000 .

[9]  S. Miyazaki,et al.  A slow thrust slip event following the two 1996 Hyuganada Earthquakes beneath the Bungo Channel, southwest Japan , 1999 .

[10]  Biing-Hwang Juang,et al.  Mixture autoregressive hidden Markov models for speech signals , 1985, IEEE Trans. Acoust. Speech Signal Process..

[11]  Gráinne McGuire,et al.  A Bayesian Model for Detecting Past Recombination Events in DNA Multiple Alignments , 2000, J. Comput. Biol..

[12]  D. Haussler,et al.  Hidden Markov models in computational biology. Applications to protein modeling. , 1993, Journal of molecular biology.

[13]  Tim Melbourne,et al.  Periodic Slow Earthquakes from the Cascadia Subduction Zone , 2002, Science.

[14]  Yehuda Bock,et al.  Instantaneous geodetic positioning with 10–50 Hz GPS measurements: Noise characteristics and implications for monitoring networks , 2006 .

[15]  Timothy Ian Melbourne,et al.  Precursory transient slip during the 2001 Mw = 8.4 Peru earthquake sequence from continuous GPS , 2002 .

[16]  Paul Jamason,et al.  SOPAC Web site (http://sopac.ucsd.edu) , 2004 .

[17]  Geoffrey C. Fox,et al.  NaradaBrokering: A Distributed Middleware Framework and Architecture for Enabling Durable Peer-to-Peer Grids , 2003, Middleware.

[18]  Kenneth Rose,et al.  Deterministically annealed design of hidden Markov model speech recognizers , 2001, IEEE Trans. Speech Audio Process..

[19]  Brian Kan-Wing Mak,et al.  Subspace distribution clustering hidden Markov model , 2001, IEEE Trans. Speech Audio Process..

[20]  Frank H. Webb,et al.  Slow But Not Quite Silent , 2003, Science.

[21]  Geoffrey C. Fox,et al.  Message-based cellular peer-to-peer grids: foundations for secure federation and autonomic services , 2005, Future Gener. Comput. Syst..

[22]  Dm Titterington,et al.  Applying the deterministic annealing expectation maximization algorithm to Naive Bayes networks , 2002 .

[23]  A. Farago,et al.  Algorithm to find the global optimum of left-to-right hidden Markov model parameters , 1989 .

[24]  Jerome R. Bellegarda,et al.  Tied mixture continuous parameter modeling for speech recognition , 1990, IEEE Trans. Acoust. Speech Signal Process..

[25]  Steve J. Young,et al.  State clustering in hidden Markov model-based continuous speech recognition , 1994, Comput. Speech Lang..

[26]  Lawrence R. Rabiner,et al.  A minimum discrimination information approach for hidden Markov modeling , 1989, IEEE Trans. Inf. Theory.