Single-trace detection and array-wide coincidence association of local earthquakes and explosions

Abstract

Local earthquakes and explosions can be recognized automatedly for the Bochum University Germany (BUG) small array by a sequence of knowledge-based approaches performed in the field and in the central hub. In single-trace detection, the recognition is based on sonogram patterns adapted for a wide variety of noise conditions on all array sites. The adaptation is performed by two steps: first each pattern is adjusted to the actual signal energy, second all those weaker phases that are below the new detection threshold are excluded. In the hub, a rule-based approach performs the coincidence evaluation. It is described by its 14 rules and the implicit assumptions. This scheme was tested on 1 month of data. The knowledge base consisted of 12 seismograms transformed automatically into the detector's internal knowledge representation of sonograms. The results show excellent performance for noise rejection and quarry blast recognition; for earthquakes clustering, a 85% success is achieved. The network success - usually below the best single performance - could be improved above any single-station optimum. Results of the rule-based approach are compared to the routine processing of the same data by Walsh-detection and the ‘2 of 4’ coincidence voting.