Comparing and integrating artificial intelligence and similarity search
detection techniques: application to seismic sequences in Southern Italy
Abstract
Understanding mechanical processes occurring on faults requires detailed
information on the microseismicity that can be enhanced today by
advanced techniques for earthquake detection. This problem is
challenging when the 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, to assess their ability to improve
catalogs associated with seismic sequences in the normal fault system of
Southern Apennines (Italy) using data from the Irpinia Near Fault
Observatory (INFO). We found that the integration of the machine
learning and template matching detectors, the former providing templates
for the cross-correlation, largely outperforms techniques based on
autocorrelation and machine learning alone, featuring an enrichment of
the automatic and manual catalogs of factors 21 and 7 respectively.
Since output catalogs can be polluted by many false positives, we
applied refined event selection based on the cumulative distribution of
their similarity level. We can thus clean up the detection lists and
analyze final subsets dominated by real events. The magnitude of
completeness decreases by more than one unit compared to the reference
value for the network. We report b-values associated with sequences
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.