Unsupervised Clustering and Pattern Identification from Continuous Seismic and Strainmeter Data in Tectonic and Volcanic Settings

We developed a deep learning model for automatic dimensionality reduction and feature extraction from time series. The model employs an encoder-decoder architecture with skip connections, enabling efficient compression and reconstruction of input data while preserving essential features. These features are used for unsupervised clustering enabling anomaly detection, and pattern recognition.

We initially developed the model to analyze seismic data from the 2021 Geldingadalir volcanic eruption in Iceland, successfully identifying a weak yet important pre-eruptive tremor that commenced three days before the eruption. Advancing the architecture with additional layers and skip connections allowed for highly accurate input reconstruction. The latter version, named AutoencoderZ, demonstrated its ability to process different data types. We applied AutoencoderZ to investigate low-frequency patterns preceding the 2023 M_W 7.8 Kahramanmaraş Earthquake in Türkiye. The model identified tremor-like episodes linked to anthropogenic activities at cement plants near the earthquake’s epicenter. Additionally, we applied AutoencoderZ to strainmeter data from the Sea of Marmara, achieving accurate reconstructions and enabling the detection of distinct tectonic-related signals.

This study highlights AutoencoderZ’s potential as a powerful tool for uncovering patterns in continuous geophysical data, providing valuable insights for monitoring and interpreting seismic and strainmeter signals.