  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.
