Damià Benet, Fidel Costa, Christina Widiwijayanti, John Pallister, Gabriela Pedreros, Patrick Allard, Hanik Humaida, Yosuke Aoki

Volcanic ash provides unique pieces of information that can help understand the progress of volcanic activity at the early stages of unrest and possible transitions towards different eruptive styles. Ash contains different types of particles that are indicative of eruptive styles and magma ascent-related processes. However, classifying ash particles into its main components is not straightforward. Diagnostic observations vary depending on the magma composition and the style of eruption, which leads to ambiguities in assigning a given particle to a given class. Moreover, there is no standardized methodology for particle classification, and thus different observers may infer different interpretations. In order to help improving this situation, we created the web-based platform Volcanic ash DataBase (VolcashDB). The database contains > 6,300 multi-focused high-resolution images of ash particles as seen under the binocular microscope from a wide range of magma compositions and eruptive styles. We quantitatively extracted multiple features of shape, texture, and color in each particle image, and petrologically classified each particle into one of the four main categories: free crystal, altered material, lithic, and juvenile. VolcashDB is publicly available and enables users to browse, obtain visual summaries, and download the images with their corresponding labels, and thus could be used for comparative studies. The classified images could also be used to train Machine Learning models to automatically classify particles and minimize observer biases.