Self-supervised Contrastive Learning for Volcanic Unrest Detection

TL;DR

This paper introduces a self-supervised contrastive learning approach using SimCLR to improve volcanic unrest detection from InSAR data, overcoming data labeling challenges and enhancing generalization over existing supervised methods.

Contribution

It presents a novel self-supervised learning pipeline for InSAR data that does not rely on labeled or synthetic data, achieving better accuracy and generalization in volcanic unrest detection.

Findings

Higher detection accuracy than state-of-the-art methods

Excellent generalization to out-of-distribution data

Effective detection of unrest episodes before eruptions

Abstract

Ground deformation measured from Interferometric Synthetic Aperture Radar (InSAR) data is considered a sign of volcanic unrest, statistically linked to a volcanic eruption. Recent studies have shown the potential of using Sentinel-1 InSAR data and supervised deep learning (DL) methods for the detection of volcanic deformation signals, towards global volcanic hazard mitigation. However, detection accuracy is compromised from the lack of labelled data and class imbalance. To overcome this, synthetic data are typically used for finetuning DL models pre-trained on the ImageNet dataset. This approach suffers from poor generalisation on real InSAR data. This letter proposes the use of self-supervised contrastive learning to learn quality visual representations hidden in unlabeled InSAR data. Our approach, based on the SimCLR framework, provides a solution that does not require a specialized architecture nor a large labelled or synthetic dataset. We show that our self-supervised pipeline achieves higher accuracy with respect to the state-of-the-art methods, and shows excellent generalisation even for out-of-distribution test data. Finally, we showcase the effectiveness of our approach for detecting the unrest episodes preceding the recent Icelandic Fagradalsfjall volcanic eruption.