EGU24-7986, updated on 22 Mar 2024
https://doi.org/10.5194/egusphere-egu24-7986
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

SDQ (Seismic Data Quality): a Python project for seismo-accelerometric data quality check

Fabio Varchetta1, Marco Massa1, Rodolfo Puglia1, Peter Danececk2, Sandro Rao2, Alfonso Mandiello2, and Davide Piccinini3
Fabio Varchetta et al.
  • 1INGV- Istituto Nazionale di Geofisica e Vulcanologia, sezione di Milano, MILANO, Italy (fabio.varchetta@ingv.it)
  • 2INGV, ONT, Osservatorio Nazionale Terremoti, Roma, Italia
  • 3INGV, sezione di PISA, PISA, Italia

In recent years, significant attention has been devoted both to seismic data processing and data quality procedures. At the Italian scale, EIDA Italia (https://eida.ingv.it/it/getdata) and ISMDq (http://ismd.mi.ingv.it/quality.php) represent the web portals currently available for checking seismic data quality. In this work, we introduce the SDQ (Seismic Data Quality) project, a new open-source Python-based tool, designed for the automatic data quality check of sismo-accelerometric stations considering both selected earthquakes and continuous data streams. Regarding earthquake data, the quality of individual waveforms is assessed by initially comparing – at first - the ground motion parameters derived from co-located accelerometers and velocimeters. SDQ operates by using a simple external input file including the INGV event-id and both station and network codes. Event information, station metadata, and waveforms are obtained from FDSN (https://www.fdsn.org) web services (https://www.fdsn.org/webservices/). Each single waveform is assigned to a quality class ranging from A (high quality) to D (data to be rejected) based on time- and frequency-dependent algorithms. Classification thresholds were empirically obtained by combining visual signal inspection and statistical analysis considering 15.000 waveforms recorded in Italy from 2012 to 2023 by IV (National Seismic Network, https://www.fdsn.org/networks/detail/IV/) and MN (MedNet network, https://www.fdsn.org/networks/detail/MN/) sismo-accelerometric stations. Concerning continuous data streams, mini-seed recording signals are analyzed at each selected station to set empirical thresholds considering several data metrics (i.e. frequency-dependent Root Mean Square, RMS, and Power Spectral Density, PSD) and data availability information (i.e. % gap and data availability, sum of gaps, maximum gap etc.) to build a station-quality archive. Users can select and build target time histories for each network, station, data stream and single ground motion component related to the selected input data included in a local mini-seed archive representing the starting point of the procedure. SDQ finally provides summary tables for both earthquake and continuous data, collecting all relevant parameters for each processed waveform and data stream, along with explanatory text files (log and warning files), allowing the user to better evaluate the results. Although SDQ is currently under development, now it is freely available and  downloadable at  https://gitlab.rm.ingv.it/EIDA/quality/sdq

How to cite: Varchetta, F., Massa, M., Puglia, R., Danececk, P., Rao, S., Mandiello, A., and Piccinini, D.: SDQ (Seismic Data Quality): a Python project for seismo-accelerometric data quality check, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7986, https://doi.org/10.5194/egusphere-egu24-7986, 2024.

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