Employing machine learning pickers for routine global earthquake monitoring with SeisComP: What are the benefits and how can we quantify the uncertainty of picks?
- 1Deutsches GeoForschungsZentrum, GFZ Potsdam, Potsdam, Germany (tilmann@gfz-potsdam.de)
- 2Université Grenoble Alpes - ISTerre, Grenoble France
- 3Bar-Ilan University, Ramat Gan, israel
- 4Institute for Geological Sciences, Freie Universität Berlin, Berlin, Germany
Recent years have seen the development of several very powerful machine learning pickers for P and S waves. The recent development of the SeisBench platform (https://github.com/seisbench/seisbench) in combination with mixed regional teleseismic benchmark datasets published by the NEIC (USGS) and GEOFON (GFZ Potsdam) enabled the retraining of the most popular picker neural network models (PhaseNet and EQTransformer) optimised for global monitoring applications in the benchmark study of Münchmeyer et al (2021, J. Geophys. Res.).
In this contribution we introduce a module scdlpicker, which connects SeisBench to SeisComP (https://www.seiscomp.de/) through a client submodule, which listens to new event detections from the regular SeisComP automatic detection system and triggers repicking of those events with any picker implemented in SeisBench, using the improved picks to trigger a relocation. The machine learning picks are subsequently available within the SeisComP GUI in case further manual refinement or checking is desired.
We demonstrate application of this system with the GEOFON global earthquake monitoring service (https://geofon.gfz-potsdam.de/eqexplorer), evaluating the benefits of using the machine learning picker with respect to the conventional workflow relying on traditional pickers with respect to timeliness of reporting earthquakes and reduction of manual work load, and improvement in the number of high quality picks available for each event.
The quantification of the uncertainty of machine learning picks is important when weighing the contribution of different picks in many location algorithms, yet this information is not readily available from machine learning pickers. They do return, however, a characteristic function (nominally the confidence in the pick), whose properties might correlate with the uncertainty of the pick. We will show whether and how the picking uncertainty correlates with properties of the characteristic function.
How to cite: Tilmann, F., Bornstein, T., Saul, J., Münchmeyer, J., and Beutel, M.: Employing machine learning pickers for routine global earthquake monitoring with SeisComP: What are the benefits and how can we quantify the uncertainty of picks?, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-10046, https://doi.org/10.5194/egusphere-egu23-10046, 2023.
