Discrimination of earthquakes and explosions by SAMC decision method

Based on dynamic time warping (DTW) algorithm, this paper proposes a novel recognition algorithm, stepwise accumulating minimal cost (SAMC) decision method. By this method, each cost function can well reflect the tendency of event’s features; moreover, SAMC is unsusceptible to the quality of features. The absolute value of overall cost function can be also served as the reliability of the results. Five features were extracted for recognition from 62 events of earthquakes (33) and explosions (29) which occurred in Beijing and its peripheral regions. The result respectively reached 90% recognition rate for five features and 92% for three features which are better features from the five ones. In another testing by U-test, another 13 events (eight earthquakes and five explosions) were randomly chosen from the same area, and the recognition rate was 92% for five features and 100% for three ones. These suggest that SAMC method can be useful to discriminate earthquakes and explosions effectively.