This study describes an automated data quality verification procedure supported by a database of power spectral densities (PSD) estimates for geophysical waveform data. The Royal Netherlands Meteorological Institute (KNMI) manages a 100-TB archive of continuous geophysical data collected from accelerometers, geophones, broadband seismometers, and infrasonic arrays deployed across the continental and Caribbean Netherlands. This rapidly expanding network at a scale of over 700 instruments makes the manual evaluation of data quality impractical and must be supported by data policies and automated methods. A technique is presented to compress and store PSD estimates in a database with a storage footprint of less than 0.05% of the raw data archive. Every week, the instrument performance is validated by comparing statistical properties of its latest monthly probabilistic PSD distribution to strict quality metrics. The criteria include thresholds based on global noise models, datalogger quantization noise models, constraints imposed by ambient noise conditions, and confidence intervals based on PSD estimates calculated from validated archived data. When a threshold is crossed, the station operator is alerted of the suspected degraded instrument performance, severely limiting the required amount of manual labor and associated human errors. The automated PSD assessment technique is applicable to accelerometers, geophones, broadband seismometers, infrasonic stations, and is demonstrated to be extendable to hydrophones, gravimeters, tiltmeters, and Global Navigation Satellite System receivers. The approach is therefore suitable for other geophysical monitoring infrastructures, for example, observational networks dedicated to continuous volcano monitoring. It is shown that it possible to detect degraded instrument performance that may otherwise remain undetected.
Mathijs Koymans, Jordi Domingo-Ballesta, Elmer Ruigrok, Reinoud Sleeman, Luca Trani, Läslo Evers. Performance assessment of geophysical instrumentation through the automated analysis of power spectral density estimates
Journal: Earth and Space Science, Volume: 8, Year: 2021, doi: https://doi.org/10.1029/2021EA001675