Automatic Classification of Station Quality by Image Based Pattern Recognition of Ppsd Plots
Abstract
The number of seismic stations is growing and it became common practice to share station waveform data in real-time with the main data centers as IRIS, GEOFON, ORFEUS and RESIF. This made analyzing station performance of increasing importance for automatic real-time processing and station selection. The value of a station depends on different factors as quality and quantity of the data, location of the site and general station density in the surrounding area and finally the type of application it can be used for. The approach described by McNamara and Boaz (2006) became standard in the last decade. It incorporates a probability density function (PDF) to display the distribution of seismic power spectral density (PSD). The low noise model (LNM) and high noise model (HNM) introduced by Peterson (1993) are also displayed in the PPSD plots introduced by McNamara and Boaz allowing an estimation of the station quality. Here we describe how we established an automatic station quality classification module using image based pattern recognition on PPSD plots. The plots were split into 4 bands: short-period characteristics (0.1-0.8 s), body wave characteristics (0.8-5 s), microseismic characteristics (5-12 s) and long-period characteristics (12-100 s). The module sqeval connects to a SeedLink server, checks available stations, requests PPSD plots through the Mustang service from IRIS or PQLX/SQLX or from GIS (gempa Image Server), a module to generate different kind of images as trace plots, map plots, helicorder plots or PPSD plots. It compares the image based quality patterns for the different period bands with the retrieved PPSD plot. The quality of a station is divided into 5 classes for each of the 4 bands. Classes A, B, C, D define regular quality between LNM and HNM while the fifth class represents out of order stations with gain problems, missing data etc. Over all period bands about 100 different patterns are required to classify most of the stations available on the IRIS server. The results are written to a file and stations can be filtered by quality. AAAA represents the best quality in all 4 bands. Also a differentiation between instrument types as broad band and short period stations is possible. A regular check using the IRIS SeedLink and Mustang service allow users to be informed about new stations with a specific quality.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2017
- Bibcode:
- 2017AGUFM.S11C0617W
- Keywords:
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- 7299 General or miscellaneous;
- SEISMOLOGY