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

Authors

  • Răzvan Popovici 1. Altair Engineering Inc. Troy, MI, USA rp@miravtech.com 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

Keywords:

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

Abstract

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.

References

A.C. Aguiar, T.I. Melbourne, and C.W. Scrivner (2009), Moment release rate of Cascadia remor constrained by GPS, Journal of Geophysical Research, Jul. 2009, 114:1-11. http://dx.doi.org/10.1029/2008JB005909

W. Bertiger, S.D. Desai, B.Haines, N. Harvey, A.W. Moore, S. Owen, J.P. Weiss (2010), ingle receiver phase ambiguity resolution with GPS data, Journal of Geodesy, May 2010, 4:327-337. http://dx.doi.org/10.1007/s00190-010-0371-9

B. Franks (2012), Taming The Big Data Tidal Wave: Finding Opportunities in Huge Data treams with Advanced Analytics, Wiley and SAS Business Series, Wiley.

R.T. Fielding (2000), Architectural styles and the design of network-based software architectures, h.D. dissertation.

D. Crockford (2006), The application/json Media Type for JavaScript Object Notation (JSON), IETF, RFC 4627, 7.

M. Simons, S.E. Minson, A. Sladen, F. Ortega, J. Jiang, S.E. Owen, L. Meng, J.-P. Ampuero, . Wei, R. Chu, D. V. Helmberger, H. Kanamori, E. Hetland, A. W. Moore, and F. H. Webb (2011), The 2011 magnitude 9.0 Tohoku-Oki earthquake: Mosaicking the megathrust from econds to centuries, Science, Jun. 2011, 332(6036):1421-1425. http://dx.doi.org/10.1126/science.1206731

A. Goncalves (2010), Beginning Java EE 6 with GlassFish 3 (Expert's Voice in Java Technology), press.

O. Varaksin, M. Caliskan (2013), PrimeFaces Cookbook, Packt Publishing.

G. Svennerberg (2010), Beginning Google Maps API 3 (Expert's Voice in Web Development), press.

S. Weidler (2008), Statistiken für Warehouse Management Systeme: Entwicklung eines tatistiktools mit Hilfe des JAVA-Frameworks JFreeChart, Vdm Verlag Dr. Müller.

D.W. Allan, J.H. Shoaf, D. Halford (1974), Statistics of Time and Frequency Data Analysis, n Time and Frequency: Theory and Fundamentals, B. E. Blair (Ed.).

D. Allan (1965), Statistics of Atomic Frequency Standards, University of Colorado.

H. Hellwig (1972), I. for Basic Standards (U.S.). Time, and F. Division, Frequency standards nd clocks: a tutorial introduction, ser. NBS technical note, Dept. of Commerce, National ureau of Standards, Institute for Basic Standards, Time and Frequency Division.

A. Mao, C.G.A. Harrison, T.H. Dixon (1999), Noise in GPS coordinate time series, Journal f Geophysical Research: Solid Earth, 104(B2):2797-2816. http://dx.doi.org/10.1029/1998JB900033

K. Larson, GPS seismology (2009), Journal of Geodesy, 83(3-4):227-233. http://dx.doi.org/10.1007/s00190-008-0233-x

Z. Malkin, A. Voinov (2001), Preliminary results of processing euref network observations sing a non-fiducial strategy, Physics and Chemistry of the Earth, Part A: Solid Earth and eodesy, 26(68):579 -583.

P. Elósegui, J. L. Davis, D. Oberlander, R. Baena, G. Ekström (2006), Accuracy of high-rate PS for seismology, Geophysical Research Letters, 33(11):1-4. http://dx.doi.org/10.1029/2006GL026065

C. Roberts, P. Morgan, C. Rizos (2002), Allan variance applied to time series baseline results or gps-based deformation monitoring applications, in Proceedings of the 2nd symposium on eodesy for geotechnical and structural applications, Berlin, May 2002, 299-311.

Z. Malkin (2011), Study of astronomical and geodetic series using the allan variance, Kinematics nd Physics of Celestial Bodies, 27(1):42-49.

Published

2014-11-17

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