  Satellites enable widespread, regional or global surveillance of volcanoes
and can provide the first indication of volcanic unrest or eruption. Here we
consider Interferometric Synthetic Aperture Radar (InSAR), which can be
employed to detect surface deformation with a strong statistical link to
eruption. The ability of machine learning to automatically identify signals of
interest in these large InSAR datasets has already been demonstrated, but
data-driven techniques, such as convolutional neutral networks (CNN) require
balanced training datasets of positive and negative signals to effectively
differentiate between real deformation and noise. As only a small proportion of
volcanoes are deforming and atmospheric noise is ubiquitous, the use of machine
learning for detecting volcanic unrest is more challenging. In this paper, we
address this problem using synthetic interferograms to train the AlexNet. The
synthetic interferograms are composed of 3 parts: 1) deformation patterns based
on a Monte Carlo selection of parameters for analytic forward models, 2)
stratified atmospheric effects derived from weather models and 3) turbulent
atmospheric effects based on statistical simulations of correlated noise. The
AlexNet architecture trained with synthetic data outperforms that trained using
real interferograms alone, based on classification accuracy and positive
predictive value (PPV). However, the models used to generate the synthetic
signals are a simplification of the natural processes, so we retrain the CNN
with a combined dataset consisting of synthetic models and selected real
examples, achieving a final PPV of 82%. Although applying atmospheric
corrections to the entire dataset is computationally expensive, it is
relatively simple to apply them to the small subset of positive results. This
further improves the detection performance without a significant increase in
computational burden.
