Understanding Volcanic Seismic Patterns with Unsupervised ML Clustering: Application at Mount Etna volcano

Mount Etna, unlike many volcanoes that experience prolonged calm intervals, exhibits persistent and continuous activity characterized by frequent strombolian bursts, lava fountains, and effusive events. This study aims to automatically identify recurrent and distinctive patterns in seismic signals by extracting clusters of waveforms with similar spectral characteristics using a fully data-driven, unsupervised machine learning framework, and to assess their correspondence with observed volcanic activity.

We analyzed daily seismic spectrograms from two summit seismic stations, ECPN and ECNE, spanning November 2020 to November 2021, a period encompassing both quiet intervals and two major lava fountain cycles. For dimensionality reduction and feature extraction, we employed AutoencoderZ, an encoder–decoder model with skip connections, convolutional and fully connected layers, a bottleneck latent space, and transposed convolutions. This architecture compresses inputs while preserving critical spectral features for unsupervised clustering. Extracted features are optimized using the Relative Bias metric and clustered via Deep Embedded Clustering (DEC), enabling data-driven anomaly detection and pattern recognition by clustering similar waveforms.

The resulting clusters were compared with independent observational datasets and seismic related metrics, including lava fountain records, volcano-tectonic and long-period (LP) event catalogs, and root mean square (RMS) amplitude of volcanic tremor. This comparison demonstrates the approach’s ability to uncover hidden structures in the seismic data and highlight key temporal transitions associated with underlying processes such as magma and fluid dynamics . To improve robustness and reduce potential spatial bias, analyses were conducted using both single-station and dual-station approaches, providing a more reliable characterization of seismic variability.

Overall, this study highlights AutoencoderZ’s versatility in revealing complex patterns in Etna’s seismic activity.