Exploring Deep Learning for Volcanic Source Inversion

Machine learning has demonstrated potentiality for challenging physical tasks, such as inverting complex mechanisms with important data limitations. It is now competing with traditional methods that involve statistical and physical modeling. These methods face significant challenges, including long computation time, extensive prior knowledge requirements, and sensitivity to scarce and noisy data which limit their ability to generalize. Regarding these difficulties, this article aims to explore the potential deployment of a deep learning-based method to solve an inverse problem in volcanology, that is, to estimate the volume change and depth of a Mogi-type source model from surface displacement measurements. Simulated displacement samples are used to get rid of insufficient amounts of real data and a lack of ground truth. Particular efforts are devoted to proper data preparation, including proposing a semi-automatic technique for training, validation, and testing data sampling and investigating the impact of data distribution, data diversity, and noise. Real data over the Suswa volcano are also used to further assess the performance of the proposed deep learning method. Results with both synthetic and real data provide evidence to consider deep learning-based methods for geophysical inverse problems.