  Predicting disaster events from seismic data is of paramount importance and
can save thousands of lives, especially in earthquake-prone areas and
habitations around volcanic craters. The drastic rise in the number of seismic
monitoring stations in recent years has allowed the collection of a huge
quantity of data, outpacing the capacity of seismologists. Due to the complex
nature of the seismological data, it is often difficult for seismologists to
detect subtle patterns with major implications. Machine learning algorithms
have been demonstrated to be effective in classification and prediction tasks
for seismic data. It has been widely known that some animals can sense
disasters like earthquakes from seismic signals well before the disaster
strikes. Mel spectrogram has been widely used for speech recognition as it
scales the actual frequencies according to human hearing. In this paper, we
propose a variant of the Mel spectrogram to scale the raw frequencies of
seismic data to the hearing of such animals that can sense disasters from
seismic signals. We are using a Computer vision algorithm along with clustering
that allows for the classification of unlabelled seismic data.
