A Time-Series-Based Self-Supervised Learning Approach for the Exploration of Complex Seismological Datasets: Application to the Fani Maoré Submarine Volcano

The exploration and characterization of complex continuous seismological datasets remain challenging, particularly for highly active and/or noisy environments. Recently, several artificial intelligence based approaches have been proposed to facilitate the analysis of seismological data, either by characterizing detected events or continuous streams. Among these, we introduced an image-based self-supervised learning framework to explore continuous seismic records without requiring prior labeling or event detection. However, image-based representations may result in a loss of information, as they are derived transformations of the original raw seismic time series and may impact the discrimination of seismic events.

In this study, we adapted a self-supervised learning based clustering workflow to operate directly on multichannel seismic time series. The main challenge when using self-supervised contrastive learning approaches with time series is adapting the data augmentation techniques to ensure sufficient transformation without losing the physics contained in the seismological records. We leveraged the contrastive learning framework to analyse two months of continuous records from Ocean Bottom Seismometers deployed near the Fani Maoré submarine volcano, using data augmentation strategies consistent with seismological records, such as channel masking and window masking in the time and frequency domains. The model was trained using four-channel time series derived from the raw data (three-component seismometer and one hydrophone) using 60 s sliding windows with a 50% overlap, enabling the network to learn meaningful latent representations of the data. Clustering was then performed directly within the learned latent space, allowing the identification of distinct signal groups. Applied to the Fani Maoré dataset, this approach revealed several families of clusters, including very rare and previously undocumented events likely associated with the activity of the Fani Maoré submarine volcano.