PQLX - A Software Tool to Evaluate Seismic Station Performance
PQLX (PASSCAL Quick Look eXtended) is open-source software authored by Richard Boaz (Boaz Consultancy) for evaluating seismic station performance and data quality. The software primarily consists of a server and client but also includes data extraction and migration tools. Given waveform data and instrument response files, PQLX server calculates trace statistics, Power Spectral Densities (PSD), and Probability Density Functions (PDF) and writes the results to a MySQL database for quick access. The PQLX client used to access these results includes a GUI which is comprised of three parts:
- Trace Viewer: The Trace Viewer has the same functionality as PQLII (PASSCAL Quick Look II). The user can view trace data, filter, and calculate spectra.
- PDF Viewer: Accessing PSD and PDF information in the database, the PDF Viewer displays PDF plots using predefined and/or user-defined time windows.
- Station Viewer: After connecting to the database, the Station Viewer displays trace statistics (e.g. max/min values and gap counts), PDF thumbnails, and available trace data. The trace data is not stored in the MySQL database, but must be available via either the filesystem or a supported webservice. Additional waveform analysis functionality is in development.
PQLX is compatible with the Linux, Mac OSX, and Solaris operating systems. Waveform data are supported in Mini-SEED, SAC, SEGY, AH, nano, and DR100 data formats and response files must be in SEED RESP file format.
The PSD and PDF calculations are based on the algorithm by D.E. McNamara and R.P. Buland, Ambient Noise Levels in the Continental United States, Bull. Seism. Soc. Am., 94, 4, 1517-1527, 2004.
PQLX at the IRIS Data Management Center
PQLX is used at the IRIS DMC in conjunction with the Earthscope USArray project. Analysts review data from the 400+ active Transportable Array and Backbone Array stations on a regular basis, checking for issues such as instrument failures, poor data quality, high noise levels, and metadata inaccuracy. This quality control effort ensures that potential problems are detected early and that a high quality dataset is archived and made available to the research community.
by Chad Trabant (IRIS DMC)