Comparing Geodetic Data Quality from PBO and non-PBO GPS Stations at Decadal and Continental Scales Abstract

abstract

  • The UNAVCO GAGE GPS data Analysis Centers (ACs) and Analysis Center Coordinator (ACC) currently process more than 1800 GPS stations. Approximately 1100 of these stations are from the Plate Boundary Observatory (PBO) network, with 700 non-PBO stations from COCONet, SCIGN, NGS CORS, and other regional networks. The 700 stations provide improved coverage across North America plus additional reference frame constraints. The extra stations were incorporated into the standard daily processing stream during a massive reprocessing effort from 2012-2014. The combined, continental-scale data set of all GPS positions spans an 18-year time period from 1996-2014 and not only represents a significant opportunity to explore mm-scale geophysical phenomena, but also allows the examination and comparison of data quality parameters between stations and networks. The overall data quality of PBO and non-PBO stations is investigated using a variety of quality parameters. Signal-to-noise and multipath histories derived from TEQC preprocessing are used to assess instrument health. Quality parameters from daily processing are used for station health determination. Position time series are used for noise analysis to characterize site stability based on white and colored noise. Further anomalies can be identified by direct inspection of a stations time series, network velocity field, and strain rate. Non-geophysical factors such as monument instability, equipment failure, and incorrect metadata can also affect data quality. Non-PBO stations were built with a variety of monument designs, equipment, and installation practices, and do not generally have consistently recorded operational and maintenance histories in a central database, because stations were installed by various organizations. Our quality analysis will identify any significant differences between PBO and non-PBO stations, and the resulting overview will help inform time series analysis for geophysical investigations.

publication date

  • 2014

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