Discrimination of seismic and non-seismic signal using SCOUTER

For areas with potential occurrence of blasting events, it is essential to distinguish them from natural earthquakes. An efficient processing method is needed to save manpower, especially under the current large amount of data records by seismic stations. We apply a SCOUTER algorithm to distinguish between the two types of events. The recognition precision of the trained model for natural earthquakes and blasts can reach 95% and 92.8%, respectively, and the recall can reach 93.4% and 94.6%, respectively. The testing results of data with different epicentral distances and SNR show that our method is stable, independent on regional waveform characteristics and insensitive to data of different SNR. The explanations for each classification at the final confidence also give us a profound enlightenment.