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Abstract

The task of association of seismic phases into events is particularly challenging in every workflow dealing with seismically active regions where dense networks are usually deployed. Additionally, recents developments of picking algorithms based on deep learning frameworks provide an increased number of phase onsets with respect to standard approach based on the computation of characteristic functions of the seismic waveforms. Especially the number of S-picks has been increased by orders of magnitude, which now requires the simultaneous association of P- and S-wave arrivals or even detecting events only based on S-wave arrivals. We present an improved version of HEX (Hyperbolic Event eXtractor), a new technique based on the logic of Random Sample Consensus here applied to association of seismic phases. Since this algorithm is particularly effective in dealing with high noise in the input data, we show the benefits of HEX2.0 to analyze picks from deep learning methods. These datasets are characterized by either detections of small amplitude earthquakes that, according to network geometry, may not show at a sufficient number of seismic stations to declare an event or, sometimes, to a high rate of false positives. The application of HEX2.0 on real data from a seismic sequence of Sannio-Matese (Southern Apennines, Italy) occurred in 2013-2014 show that: i) resulting events show a high number of phases compared to previous catalogs; ii) few phases are discarded in the event location. HEX2.0 provides an accurate, easy-to-use and computationally effective solution to the seismic phase association problem.