clustering of seismic data using time-frequency analysis

In this paper, the clustering of seismic data is investigated where it is asubtle problem and the actual answer is always unknown. as seismic data are nonstationaryprocesses with complex variation in time and frequency (tf), the tf analysisis applied using windowed spectrograms, where in this paper, we apply the hanningwindowing. the method is based on the ambiguity spectrum matrix and its first leftand right singular vectors by means of singular vector decomposition. the superiorityof the method is its applicability to situations in which the process has time-frequencyand amplitude stochastic variation. the similarity of two signals is investigated bymeans of a suitable similarity assessment, and the results are provided via simulationsand real data analysis, where california earthquakes occurred during years 2007 to2019, are classified.