New Heuristics Based on Wavelet Analysis of a Single Sensor Record for Earthquake and Explosion Detection

Abstract

Recognition of a seismic event by the type of its phenomenon (earthquake or explosion, and if an explosion, then a subsurface or open pit explosion) at a regional scale on its seismogram is a problem that many researchers worldwide attempt to solve. A detailed review of Russian and global publications on this topic has been produced. This review made it possible to formulate the most promising directions on which research is underway. Thus, this study, which offers another approach to creating a discriminatory feature, may be useful for improving the results of recognition of a seismic event. The proposed method is based on continuous wavelet analysis of the seismogram from a single receiver. Two additional transformations (constructing the frequency envelopes to waveletogram and their cross-correlation at a given time) sequentially translate this result into a compact frequency-time portrait of the event. This technique was tested on seismograms of several events, the nature of which is a priori known. Recognition is possible both visually (including machine vision methods) and automatically. For the first option, the key features of frequency-time portraits of events to which attention should be paid are formulated. For the second case, a method for determining the numerical characteristics measured by the obtained images is defined. It is shown that these characteristics are naturally divided into clusters that correspond to the nature of the events.