Towards Systematic Modelling of Volcano Deformation Sources using Automatically-Generated InSAR Products

Volcano deformation measured through InSAR is ideal for volcano monitoring in many regions due to its global coverage, characteristic spatio-temporal patterns, and modelling insights into magmatic plumbing systems. Routinely acquired and processed Sentinel-1 InSAR datacubes provide the first opportunity to systematically catalogue, model and compare volcano deformation episodes globally. Here, we present a multi-step framework (GBIS-BULK) to systematically pre-process and model volcano deformation signals, designed to be applied to routinely processed InSAR datasets. This requires a robust (semi-) automated approach to approximate the signal location and footprint for effective pre-processing and modelling. Our approach combines 1) filtering and clustering using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to locate the signal centre; 2) noise reduction using Independent Component Analysis (ICA); and 3) image classification using Otsu thresholding to delimit the signal footprint. We invert for the best-fit point source model using constraints from existing global catalogues of volcano deformation characteristics. First, we examine the influence of downsampling schemes and image noise and coherence using synthetic interferograms. Then, we validate the approach using Sentinel-1 deformation images from the East African Rift System. The pre-processing steps reasonably locate the signal at 15/16 of the EARS volcanoes, and the signal footprint at 14/16. ICA reduces or approximately maintains the image RMS in all cases. Our systematic point source estimates showed consistency when directly compared with previous (bespoke) modelling studies. This approach has the potential to be integrated with existing toolkits for routinely processing and analysing Sentinel-1 InSAR data and hence applied globally.