Volcanic Deformation Monitoring utilizing Deep Learning and Wavelet Transform

Abstract

There are 20-50 new volcanic eruptions annually, which often do not have onsite monitoring. InSAR can be used to globally monitor volcanic deformations, even in hard-to-reach areas. With state-of-the-art persistent and distributed scatterer processing, InSAR data can even point to the volcanoes' subtle, few mm/year changes and deep learning (DL) models can red flag them. Our research leverages the practical application of DL with a classification architecture, InceptionResNet v2, to identify InSAR data containing volcanic deformations. We utilize 5-year-long deformation maps covering the Central Volcanic Zone in the South American Andes, reserving the area known for its volcanoes for testing. The remaining data, in combination with synthetic volcanic deformations, is used for training. The explainability tool, Grad-CAM, shows that due to the nature of subtle volcanic deformations observed by InSAR, the model is struggling to delineate and distinguish volcanic deformation signals. We use wavelet transformations and filtering to enhance the data and improve the DL model performance. Daubechies 2 wavelet transform accentuates subtle large-surface signals, which are often volcanic in nature while removing the subtle high-frequency patterns. The DL models are trained, and each is tested on the data with a different number of wavelet transforms from 0-4. The model trained and tested on original data achieved a 64.02% AUC ROC average over 3 runs, while when tested on data two times transformed by wavelet transform, it improved to 84.14% AUC ROC average over 3 runs. These findings prove that Daubechies 2 wavelet transform cleans data while exaggerating the volcanic deformation. It also enlarges the small point deformation sources large in intensity, which can be solved by filtering beforehand. The models trained and used in this way detect all 5 different subtle volcanic deformations in the region, with smallest being 5 mm/year.