Deep Learning for Subtle Volcanic Deformation Detection With InSAR Data in Central Volcanic Zone

• Published in: IEEE transactions on geoscience and remote sensing 2023, Vol.61, p.1-20
• Main Authors: Beker, Teo, Ansari, Homa, Montazeri, Sina, Song, Qian, Zhu, Xiao Xiang
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Atmospheric modeling; Data models; Deep learning; Deep learning (DL); Deformable models; Deformation; Embedding; Explainable artificial intelligence; Interferometric synthetic aperture radar; interferometric synthetic aperture radar (InSAR); Machine learning; minimal deformation analysis; Salt lakes; SAR (radar); Sensitivity analysis; Slope processes; Spatial filtering; Time series analysis; volcanic deformation; volcanic deformation simulation; Volcanoes
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2023.3318469

Subtle volcanic deformations point to volcanic activities, and monitoring them helps predict eruptions. Today, it is possible to remotely detect volcanic deformation in mm/year scale thanks to advances in interferometric synthetic aperture radar (InSAR). This article proposes a framework based on a deep learning model to automatically discriminate subtle volcanic deformations from other deformation types in five-year-long InSAR stacks. Models are trained on a synthetic training set. To better understand and improve the models, explainable artificial intelligence (AI) analyses are performed. In initial models, Gradient-weighted Class Activation Mapping (Grad-CAM) linked new-found patterns of slope processes and salt lake deformations to false-positive detections. The models are then improved by fine-tuning (FT) with a hybrid synthetic-real data, and additional performance is extracted by low-pass spatial filtering (LSF) of the real test set. The t-distributed stochastic neighbor embedding (t-SNE) latent feature visualization confirmed the similarity and shortcomings of the FT set, highlighting the problem of elevation components in residual tropospheric noise. After fine-tuning, all the volcanic deformations are detected, including the smallest one, Lazufre, deforming 5 mm/year. The first time confirmed deformation of Cerro El Condor is observed, deforming 9.9-17.5 mm/year. Finally, sensitivity analysis uncovered the model's minimal detectable deformation of 2 mm/year.