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Predicting disaster events from seismic data is of paramount importance andcan save thousands of lives, especially in earthquake-prone areas andhabitations around volcanic craters. The drastic rise in the number of seismicmonitoring stations in recent years has allowed the collection of a hugequantity of data, outpacing the capacity of seismologists. Due to the complexnature of the seismological data, it is often difficult for seismologists todetect subtle patterns with major implications. Machine learning algorithmshave been demonstrated to be effective in classification and prediction tasksfor seismic data. It has been widely known that some animals can sensedisasters like earthquakes from seismic signals well before the disasterstrikes. Mel spectrogram has been widely used for speech recognition as itscales the actual frequencies according to human hearing. In this paper, wepropose a variant of the Mel spectrogram to scale the raw frequencies ofseismic data to the hearing of such animals that can sense disasters fromseismic signals. We are using a Computer vision algorithm along with clusteringthat allows for the classification of unlabelled seismic data.

Animal inspired Application of a Variant of Mel Spectrogram for Seismic Data Processing