Real-time Monitoring of Tectonic Displacements in the Pacific Northwest through an Array of GPS Receivers


  • Răzvan Popovici 1. Altair Engineering Inc. Troy, MI, USA 2. School of Engineering and Computer Science, Oakland University Rochester, MI, USA
  • Răzvan Andonie 1. Computer Science Department, Central Washington University Ellensburg, WA, USA 2. Electronics and Computers Department, Transilvania University BraÅŸov, Romania
  • Walter M. Szeliga Department of Geological Sciences, Central Washington University Ellensburg, WA, USA
  • Timothy I. Melbourne Department of Geological Sciences, Central Washington University Ellensburg, WA, USA
  • Craig W. Scrivner Department of Geological Sciences, Central Washington University Ellensburg, WA, USA


Real time dashboard, signal analysis, data streaming, GPS, slow earthquake


The Pacific Northwest Geodesic Array at Central Washington University collects telemetered streaming data from 450 GPS stations. These real-time data are used to monitor and mitigate natural hazards arising from earthquakes, volcanic eruptions, landslides, and coastal sea-level hazards in the Pacific Northwest. The displacement measurements are performed at millimeter-scale, and require stringent analysis and parameter estimation techniques. Recent improvements in both accuracy of positioning measurements and latency of terrestrial data communication have led to the ability to collect data with higher sampling rates, of up to 1 Hz. For seismic monitoring applications, this means 1350 separate position streams from stations located across 1200 km along the West Coast of North America must be able to be both visually observed and analyzed automatically. We aim to make the real-time information from GPS sensors easily available, including public access via interfaces for all intelligent devices with a connection to the Internet. 
Our contribution is a dashboard application that monitors the real-time status of the network of GPS sensors. We are able to visualize individual and multiple sensors using similar time series scales. We are also able to visualize groups of sensors based on time-dependent statistical similarity, such as sensors with the the highest variance, in real-time. In addition to raw positioning data, users can also display derived quantities, such as the Allan variance or the second derivative of a data stream.


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