Fig. 1. Comparison of earthquake detection methods in terms of three qualitative metrics: Detection sensitivity, general applicability, and computational efficiency.

STA/LTA scores high on general applicability because it finds unknown sources, scores high on computational efficiency because it detects earthquakes in real time, but scores low on detection sensitivity because it can miss low-snr seismic events. Template matching rates high on detection sensitivity because cross-correlation can find low-snr events, rates high on computational efficiency because we only need to cross-correlate continuous data with a small set of template waveforms, but rates low on general applicability because template waveforms need to be determined in advance. Autocorrelation has high detection sensitivity because it cross-correlates waveforms, and high general applicability because it can find unknown similar sources, but has very low computational efficiency that scales poorly with the size of the continuous data set. FAST performs well with respect to all three metrics, combining the detection sensitivity and general applicability of correlation-based detection with high computational efficiency and scalability.