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

Abstract— 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 continuousvalued 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.

• Type: Conference
• Year: 2007
• Where: CIDM
• Authors: Robert Granat, Galip Aydin, Marlon E. Pierce, Zhigang Qi, Yehuda Bock
• Keywords: Artificial Intelligence, CIDM 2007, GPS Surface Position, Mode Classifications, Time Series