Enhanced performance of ISC focal mechanism computations as a result of automatic first-motion polarity picking optimization
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The International Seismological Centre (ISC) routinely calculates and makes available automatic earthquake focal mechanisms by combining reported parametric data (first-motion polarities) available in the reviewed ISC Bulletin and auto-picked first-motion polarities obtained from waveform data using a broadband automatic picker. In order to further enhance the robustness of the auto-picked polarities, we set up an optimization strategy which is carried out using the neighbourhood algorithm on a 24-processor mini computer cluster. The aim is to minimize an objective misfit function which takes into account the data uncertainties and compares the first P-wave arrival times and polarities of a large dataset of nearly 18,000 manual picks and the associated auto-picked waveform phase arrivals. The optimization yielded an overall increase of matching auto-picks from 15 to 30% in comparison with the default setup of the automatic picker. We then applied the optimized automatic picker to a set of earthquakes from the reviewed ISC Bulletin where we could not obtain well-constrained mechanism solutions using its default setup. As a result of using the optimized picker, we obtained well-trusted mechanism solutions for 28% of these cases by increasing the number of first motion auto-picked polarities, and hence minimizing the station azimuthal gap in some cases, and/or correcting some of the erroneous auto-picked polarities where possible.
KeywordsBody waves Computational seismology Earthquake source observations Optimization strategy
The authors wish to thank the editor Prof. Anastasia Kiratzi and two anonymous reviewers for their comments and suggestions which helped improve this manuscript. We gratefully acknowledge the availability of global seismograms from the IRIS and Orfeus data centres, as well as the availability of the FilterPicker source code from ALomax Scientific. This study makes use of the computer package neighbourhood algorithm which was made available with support from the Inversion Laboratory (ilab). ilab is a program for construction and distribution of data inference software in the geosciences supported by AuScope Ltd., a non-profit organization for Earth Science infrastructure funded by the Australian Federal Government. The figures in this study have been produced using the Generic Mapping Tools (GMT, Wessel et al. 2013) and the Matplotlib python library (Hunter 2007).
The authors acknowledge financial support from 67 member institutions and a National Science Foundation Award (NSF, EAR:1811737).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
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