Identification and classification of similar seismic events by using phase-only correlation technique.

Abstract

Identification and clustering of similar seismic events are important for precise estimation of source locations and for evaluation of subsurface structure. Phase-only correlation is well known as a real-time imagematching method for fingerprint identification. In this paper, I propose a method of cluster analysis using phase-only correlation (POC) to both identify and hierarchically classify similar seismic waveforms. The POC value of time-varying spectral representation is used to evaluate similarity between two images of a waveform, and cluster analysis is used to classify waveforms into groups according to a distance measure. The waveforms are first transformed into time-varying spectral representations to express frequency content in the time–frequency domain. The phase-only correlation function is calculated between two time-varying spectral representations and similarity is evaluated using the shape and peak value of the phase-only correlation function. This method is applied to arbitrarily selected waveforms from aftershocks of an earthquake in Japan and assessed its ability to identify similar waveforms perturbed by white noise. The results show that the phase-only correlation function exhibits a sharp peak that quantifies similarity and dissimilarity over a wide range of S/N. A matrix is defined using vectors consisting of the peak values of the POC functions, and hierarchical cluster analysis is applied based on the city-block distance and a Ward criterion to define groups. The feasibility of the method is demonstrated by applying it to aftershocks at an active fault.