VolcAshDB: a volcanic ash database of classified particle images and features

• Main Authors: Benet, Damià, Costa, Fidel, Widiwijayanti, Christina, Pallister, John, Pedreros, Gabriela, Allard, Patrick, Humaida, Hanik, Aoki, Yosuke, Maeno, Fukashi
• Other Authors: Asian School of the Environment
• Format: Article
• Language: English
• Published: 2024
• Subjects: Earth and Environmental Sciences; Volcanic ash; Database
• Online Access: https://hdl.handle.net/10356/179992
• Institution: Nanyang Technological University

Volcanic ash provides unique pieces of information that can help to 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 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. To improve 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 types of volcanic activity. For each particle image, we quantitatively extracted 33 features of shape, texture, and color, and petrographically classified each particle into one of the four main categories: free crystal, altered material, lithic, and juvenile. VolcAshDB (https://volcash.wovodat.org) is publicly available and enables users to browse, obtain visual summaries, and download the images with their corresponding labels. The classified images could be used for comparative studies and to train Machine Learning models to automatically classify particles and minimize observer biases.