Introduction

scanloc for SeisComP [18] is optimzed for monitoring local and micro seismicity within small as well as large seismic monitoring networks in low and high seismicity environments. Typical networks consist of 5 to 20 stations and sometimes even many more were the station distances are in the range of hundreds of meters to tens of kilometers.

scanloc makes use of the cluster search algorithm DBSCAN [1] to associate detected seismic phases to one or many potential seismic events. While the cluster search itself is based on P phases only, in a second step S picks and more P phases are associated to better locate the event.

In order to provide high-quality picks from S phases, a new S-phase picker is distributed along with scanloc. The S picker allows to identify S phases on the horizontal components or on the vertical components if the horizontals should be unavailable. A user-friendly debugger GUI allows the interactive tuning of the S picker configuration parameters based on waveforms.

Additonal auxiliary tools ship with the scanloc package to support detailed tuning and realistic playbacks.

Applications

scanloc is a backbone module in SeisComP systems for many applications including

  • Real-time monitoring of local and regional earthquakes,

  • Re-processing of historical data for more complete seismicity catalogs,

  • Monitoring of natural and induced seismicity in industrial environments e.g. at

    • geothermal plants,

    • oil and gas production,

    • underground gas storages,

    • open and deep mines,

  • Integration of teleseismic events detected by local monitoring networks

  • Association of P and S phases to detections by other instances e.g. ccloc [5] or LAMBDA [3].

Examples from science

scanloc has been routinely applied, demonstrated, promoted and discussed with scientists and the SeisComP community in scientific publications and at international science conferences.

Examples:

  1. A.F. Bell, P.C. La Femina, M. Ruiz, F. Amelung, M. Bagnardi, C. J. Bean, B. Bernard, C. Ebinger, M. Gleeson, J. R. Grannell, S. Hernandez, M. Higgins, C. Liorzou, P. Lundgren, N. J. Meier, M. Möllhoff, S.-J. Oliva, A. G. Ruiz and M. J. Stock: Caldera resurgence during the 2018 eruption of Sierra Negra volcano, Galápagos Islands, 2021, Nat. Commun., 12, 1397, doi: 10.1038/s41467-021-21596-4.

  2. F. Grigoli, L. Scarabello, M. Boese, B. Weber, S. Wiemer, J. F. Clinton: Pick- and waveform-based techniques for real-time detection of induced seismicity, 2018, Geophys. J. Int., 213:2, doi: 0.1093/gji/ggy019.

  3. J. Clinton, F. Grigoli, T. Diehl, T. Kraft, L. Scarabello, M. Hermann, P. Kaestli, M. Boese, S. Wiemer: Advanced Real-time Monitoring for Natural and Induced seismic sequences, 2018, Geophyscial Research Abracts, EGU General Assembly, Vol. 20, abstract: EGU2018-9480-2.

  4. F. Grigoli, M. Boese, L. Scarabello, T. Diehl, B. Weber, S. Wiemer, J. F. Clinton: Picking vs Waveform based detection and location methods for induced seismicity monitoring, 2018, JpGU2018, Japan, abstract: SSS03-05.

  5. F. Grigoli, M. Boese, L. Scarabello, T. Diehl, B. Weber, S. Wiemer, J. F. Clinton: Picking vs Waveform based detection and location methods for induced seismicity monitoring, 2017, Geophyscial Research Abracts, EGU General Assembly, Vol. 19, abstract: EGU2017-10562.

  6. D. Roessler, J. Becker, E. Ellguth, R. Henneberger, S. Herrnkind, B. Weber: Cluster-search based monitoring of local earthquakes in SeisComP3, 2016, AG Seismology - 42. meeting, Bad Salzschlirf, Germany

  7. D. Roessler, E. Ellguth, S. Herrnkind, B. Weber, R. Henneberger, H. Blanck: Cluster-search based monitoring of local earthquakes in SeisComP3, 2016, AGU Fall Meeting, San Francisco, USA, abstract S31E-06.