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.
翻译:从合成孔径雷达(InSAR)数据测得的地面变形,被认为是火山动乱的迹象,在统计上与火山爆发有联系。最近的研究显示,使用Sentinel-1 InSAR(Sentinel-1 InSAR)数据以及监督深学习(DL)方法探测火山变形信号的潜力,以减缓全球火山危害。然而,检测的准确性由于缺少贴标签的数据和阶级不平衡而受到损害。要克服这一点,合成数据通常用于微调在图像网数据集上预先训练过的DL模型。这种方法因真实的 InSAR数据不甚全面化而受到影响。本信建议使用自我监督的对比学习来学习隐含在未贴标签的SAR数据中的优质视觉表现。我们基于SimCLR框架的方法提供了一种不需要专门结构或大型贴标签或合成数据集的解决办法。我们表明,我们自我监督的管道在最新技术方法方面达到更高的准确度,并显示即使分配外测试数据也非常精准。最后,我们展示了我们用来探测近期的火山爆发之前的火山爆发情况的方法的有效性。