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Comparing and integrating artificial intelligence and similarity search detection techniques for seismic sequences in Southern Italy
  • +2
  • Francesco Scotto di Uccio,
  • Antonio Scala,
  • Gaetano Festa,
  • Matteo Picozzi,
  • Gregory C. Beroza
Francesco Scotto di Uccio
Università di Napoli Federico II, Università di Napoli Federico II, Università di Napoli Federico II, Università di Napoli Federico II

Corresponding Author:[email protected]

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Antonio Scala
Università di Napoli Federico II, Università di Napoli Federico II, Università di Napoli Federico II, Università di Napoli Federico II
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Gaetano Festa
Università di Napoli Federico II, Università di Napoli Federico II, Università di Napoli Federico II, Università di Napoli Federico II
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Matteo Picozzi
University of Naples Federico II, University of Naples Federico II, University of Naples Federico II, University of Naples Federico II
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Gregory C. Beroza
Stanford University, Stanford University, Stanford University, Stanford University
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Abstract

Understanding mechanical processes occurring on faults requires detailed information on the microseismicity that can be today enhanced by advanced techniques for earthquake detection. This problem is more challenging when seismicity rate is low and most of the earthquakes occur at depth. In this study we compare three detection techniques, the autocorrelation FAST, the machine learning EQTransformer and the template matching EQCorrScan, for catalog improvement associated with seismic sequences in the normal fault system of Southern Apennines (Italy) using data from the Irpinia near fault observatory. We found that the integration of the machine learning and template matching detectors, the former providing templates for the cross-correlation, largely outperforms with respect to the techniques based on autocorrelation and machine learning alone, featuring an enrichment of the automatic and manual catalogs of factors 21 and 7 respectively. The output catalogs can be polluted by many false positives; so, we applied refined selection based on the cumulative distribution of the similarity level to clean up the detection lists and analyze final subsets dominated by real events. The magnitude of completeness decreases by more than one unit as compared to the reference value for the network. We report b-values smaller than the average, likely corresponding to larger differential stresses than for the background seismicity of the area. For all the analyzed sequences, we found that main events are anticipated by foreshocks, indicating a possible preparation process for mainshocks at sub-kilometric scales.