Digital Holographic Microscopy and Machine Learning for Quantitative 3D Analysis and Automatic Classification of Volcanic Ash Particles

Determining the shapes, sizes and optical properties of volcanic ash presents a significant challenge in volcanology, the aviation industry and atmospheric models involving transport and dispersion of particles. Eruptive dynamics, including fragmentation mechanisms, magma viscosity and particle transport processes, among others, are encoded in the intricate shapes and sizes of these particles. Traditionally, the analysis of ash particles' morphology has relied on quantitative non-dimensional parameters, primarily derived from their 2D silhouette projected area, using conventional microscopy or particle analyzers. However, these fail to capture the 3D structure of their morphology. Additionally, atmospheric dispersion models often assume spherical particles with uniform refractive indices, introducing uncertainties in particle size estimations and dispersion calculations. In this study, we introduce a novel 3D characterization method for volcanic ash using digital holographic microscopy (DHM) combined with machine learning (ML). We implemented an off-axis interferometer to register holograms of volcanic ash samples. We show that segmented phase maps from the reconstructed holograms can be used to derive both 2D and 3D phase-based morphological parameters for individual ash particles or to estimate their refractive index. To illustrate the potential of this technique, we analyzed morphological differences between ashes acccording to their transport mechanism: fallout and flow. A ML algorithm based on support vector machine (SVM) was trained to classify particles into one of these two categories, achieving an average accuracy of 76%. These results show that the proposed approach serves as a valuable tool for monitoring volcanic eruptions providing insights on their characteristics and associated environmental impact.