Supervised Learning for Automatic Source Type Discrimination of Seismic Events in Sweden

Distinguishing small earthquakes from man-made blasts at construction sites, in quarries and in mines is a non-trivial task during automatic event analysis and thus typically requires manual revision. We have developed station-specific classification models capable of both accurately assigning source type to seismic events in Sweden and filtering out spurious events from an automatic event catalogue. Our method divides all three components of the seismic records for each event into four non-overlapping time windows, corresponding to P-phase, P-coda, S-phase and S-coda, and computes the Root-Mean-Square (RMS) amplitude in each window. This process is repeated for a total of twenty narrow frequency bands. The resulting array of amplitudes is passed as inputs to fully connected Artificial Neural Network classifiers which attempt to filter out spurious events before distinguishing between natural earthquakes, industrial blasts and mining-induced events. The distinction includes e.g. distinguishing mining blasts from mining induced events, shallow earthquakes from blasts and differentiating between different types of mining induced events. The classifiers are trained on labelled seismic records dating from 2010 to 2021. They are already in use at the Swedish National Seismic Network where they serve as an aid to the routine manual analysis and as a tool for directly assigning preliminary source type to events in an automatic event catalogue. Initial results are promising and suggest that the method can accurately distinguish between different types of seismic events registered in Sweden and filter out the majority of spurious events.