TY - JOUR A1 - Petersen, Gesa Maria A1 - Cesca, Simone A1 - Kriegerowski, Marius T1 - Automated quality control for large seismic networks BT - implementation and application to the AlpArray seismic network JF - Seismological research letters N2 - 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. Electronic Supplement: List of teleseismic events and tables of median, mean, and standard deviation of relative gain factors, and figures of relative gain factors of all event-station pairs, waveform example showing inverse polarity of horizontal components on ZS.D125, histograms of median, mean, and standard deviation of the correction angles, examples of synthetic and recorded frequency spectra of ZS.D046 and NI.VINO. Y1 - 2019 U6 - https://doi.org/10.1785/0220180342 SN - 0895-0695 SN - 1938-2057 VL - 90 IS - 3 SP - 1177 EP - 1190 PB - Seismological Society of America CY - Albany ER - TY - JOUR A1 - Kriegerowski, Marius A1 - Petersen, Gesa Maria A1 - Vasyura-Bathke, Hannes A1 - Ohrnberger, Matthias T1 - A Deep Convolutional Neural Network for Localization of Clustered Earthquakes Based on Multistation Full Waveforms JF - Seismological research letters N2 - Earthquake localization is both a necessity within the field of seismology, and a prerequisite for further analysis such as source studies and hazard assessment. Traditional localization methods often rely on manually picked phases. We present an alternative approach using deep learning that once trained can predict hypocenter locations efficiently. In seismology, neural networks have typically been trained with either single-station records or based on features that have been extracted previously from the waveforms. We use three-component full-waveform records of multiple stations directly. This means no information is lost during preprocessing and preparation of the data does not require expert knowledge. The first convolutional layer of our deep convolutional neural network (CNN) becomes sensitive to features that characterize the waveforms it is trained on. We show that this layer can therefore additionally be used as an event detector. As a test case, we trained our CNN using more than 2000 earthquake swarm events from West Bohemia, recorded by nine local three-component stations. The CNN successfully located 908 validation events with standard deviations of 56.4 m in east-west, 123.8 m in north-south, and 136.3 m in vertical direction compared to a double-difference relocated reference catalog. The detector is sensitive to events with magnitudes down to M-L = -0.8 with 3.5% false positive detections. Y1 - 2018 U6 - https://doi.org/10.1785/0220180320 SN - 0895-0695 SN - 1938-2057 VL - 90 IS - 2 SP - 510 EP - 516 PB - Seismological Society of America CY - Albany ER -