Volcanic Micro-Earthquake Classification With Spectral Manifolds in Low-Dimensional Latent Spaces

Abstract

Micro-earthquakes are frequently associated with volcanic activity and are vital indicators of volcanic processes. These minor seismic events occur within or near volcanic systems, yielding valuable insights into subsurface activities. Geologists meticulously record and analyze these events to monitor volcanoes and forecast eruptions. While recent years have seen several studies proposing automated detection and classification systems of seismic events, approaches based on Manifold Learning techniques could be beneficial in terms of information interpretability and transfer learning to other Machine Learning tasks. This paper presents a novel approach employing audio features and psychoacoustic scales to represent micro-earthquakes at Cotopaxi and Llaima Volcanoes, and these representations are then transformed into low-dimensional latent spaces. We implemented a multi-class classification system for events generated by these volcanoes, incorporating feature selection techniques based on audio-inspired features. This approach enhances the detection of volcanic phenomena triggering eruptions and improves interpretability. Our results indicated high accuracy, with rates of 94.44% for Llaima Volcano and 95.45% for Cotopaxi Volcano when utilizing mutual information to select the most relevant features. Spectral Roll-off Point and Spectral Flux dominate in classifying events for both volcanoes. These findings suggest that low-dimensional latent spaces, particularly when utilizing spectral features, can be a promising foundation for developing transfer learning schemes in general, and new multi-class classification systems in particular, for detecting volcanic micro-earthquakes.

Keywords

• Audio features
• latent space separability
• manifold learning
• micro-earthquake classification
• mutual information