Self-Supervised Contrastive Learning in the Context of Volcano-Seismic Datasets

Abstract. Volcano-seismic datasets are expensive to label due to the requirement for expertise to understand the signals and the time-intensive nature of extracting and labeling different events that are occurring. This work evaluates whether self supervised methods can enable volcanologists to gain knowledge about the content of volcanic datasets without the use of labels, or reduce the amount of labels required. The aim of this work is to compare several common techniques and illustrate their usefulness for the volcanic community, where labeled data is an even more precious commodity than the wider seismic community. Experiments have been performed on three real-world datasets containing isolated volcano-seismic datasets from Llaima volcano, Colima volcano, and Mount Etna. Time-Series Representation Learning via Temporal and Contextual Contrasting (TS-TCC) shows particularly high performance in this task for finding structures in an self-supervised fashion. This indicates the untapped potential of self-supervised training to aid in different data analysis tasks within the volcano-seismology community.