Machine learning event detection workflows in practice: A case study from the 2019 Durrës aftershock sequence

Abstract

Machine Learning (ML) methods have demonstrated exceptional performance in recent years when applied to the task of seismic event detection. With numerous ML techniques now available for detecting seismicity, applying these methods in practice can help further highlight their advantages over more traditional approaches. Constructing such workflows also enables benchmarking comparisons of the latest algorithms on practical data. We combine the latest methods in seismic event detection to analyse an 18-day period of aftershock seismicity for the M w 6.4 2019 Durrës earthquake in Albania. We test two phase association-based event detection workflows methods, the EarthQuake Transformer (EQT; Mousavi et al., 2020) end-to-end seismic detection workflow, and the PhaseNet (Zhu & Beroza 2019) picker with the Hyperbolic Event eXtractor (Woollam et al. 2020) associator.Both ML approaches are benchmarked against a data set compiled by two independently operating seismic experts who processed a small subset of events of this 18-day period.In total, PhaseNet & HEX identifies 3,551 events, and EQT detects 1,110 events with the larger catalog (PhaseNet & HEX) achieving a magnitude of completeness of ∼1. By relocating the derived catalogs with the same minimum 1D velocity model, we calculate statistics on the resulting hypocentral locations and phase picks.