Categorizing Volcanic Seismic Events with Unsupervised Learning

Abstract

We explored three different clustering-based classifiers to categorize two different volcanic seismic events and to find possible overlapping signals that could occur at the same time or immediately after seismic events occurrence. The BFR classifier with k=2 was chosen as the best out of 27 explored models statistically (p$\lt$0.05), reaching a mean of accuracy score of 88%. This result represents a satisfactory and competitive classification performance when compared to the state of art methods. The CURE classifier with k=3 attained a mean of accuracy value of 87% at p$\lt$0.05, allowing it to be the only model capable of detecting seismic events with overlapping signals. Therefore, the proposed clustering-based exploration was effective in providing competitive models for seismic events classification and overlapped signal detection.