Automated Quality Control for Large Seismic Networks: Implementation and Application to the AlpArray Seismic Network

As a consequence of the rapid growing worldwide seismic data set, a huge variety of automatized data‐processing methods have been developed. To perform automatized waveform‐based seismological studies aiming for magnitudes or source process inversion, it is crucial to identify network stations with erroneous transfer functions, gain factors, or component orientations. We developed a new tool dedicated to automated station quality control of dense seismic networks and arrays. The python‐based AutoStatsQ toolbox uses the pyrocko seismic data‐processing environment. The toolbox automatically downloads data and metadata for selected teleseismic events and performs different tests. As a result, relative gain factors, sensor orientation corrections, and reliable frequency bands are computed for all stations in a chosen time period. Relative gain factors are calculated for all stations and events in a time domain based on maximum P‐phase amplitudes. A Rayleigh‐wave polarization analysis is used to identify deviating sensor orientations. The power spectra of all stations in a given frequency range are compared with synthetic ones, accessing Global Centroid Moment Tensor (CMT) solutions. Frequency ranges of coinciding synthetic and recorded power spectral densities (PSDs) may serve as guidelines for choosing band‐pass filters for moment tensor (MT) inversion and help confirm the corner frequency of the instrument. The toolbox was applied to the permanent and temporary AlpArray networks as well as to the denser SWATH‐D network, a total of over 750 stations. Stations with significantly deviating gain factors were identified, as well as stations with inverse polarity and misorientations of the horizontal components. The tool can be used to quickly access network quality and to omit or correct stations before MT inversion.