VolcAshDB: a Volcanic Ash DataBase of classified particle images and features

IF 3.6 2区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Damià Benet, Fidel Costa, Christina Widiwijayanti, John Pallister, Gabriela Pedreros, Patrick Allard, Hanik Humaida, Yosuke Aoki, Fukashi Maeno
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Abstract

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.

Abstract Image

VolcAshDB:包含分类颗粒图像和特征的火山灰数据库
火山灰提供了独特的信息,有助于了解动乱初期火山活动的进展,以及可能向不同喷发方式的过渡。火山灰含有不同类型的颗粒,可显示喷发方式和岩浆上升过程。然而,将火山灰颗粒分为其主要成分并不简单。由于岩浆成分和喷发方式的不同,诊断观测结果也各不相同,这就导致了将特定颗粒归入特定类别的模糊性。此外,颗粒分类没有标准化的方法,因此不同的观察者可能会推断出不同的解释。为了改善这种情况,我们创建了基于网络的火山灰数据库(VolcAshDB)平台。该数据库包含 6,300 张双目显微镜下看到的火山灰颗粒的多焦点高分辨率图像,这些图像来自各种岩浆成分和火山活动类型。对于每个颗粒图像,我们定量提取了 33 个形状、纹理和颜色特征,并通过岩石学方法将每个颗粒分为四大类:游离晶体、蚀变材料、石质和幼体。VolcAshDB (https://volcash.wovodat.org) 可供公开使用,用户可以浏览、获取可视化摘要,并下载带有相应标签的图像。分类图像可用于比较研究和训练机器学习模型,以自动对颗粒进行分类,并最大限度地减少观察者的偏差。
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来源期刊
Bulletin of Volcanology
Bulletin of Volcanology 地学-地球科学综合
CiteScore
6.40
自引率
20.00%
发文量
89
审稿时长
4-8 weeks
期刊介绍: Bulletin of Volcanology was founded in 1922, as Bulletin Volcanologique, and is the official journal of the International Association of Volcanology and Chemistry of the Earth’s Interior (IAVCEI). The Bulletin of Volcanology publishes papers on volcanoes, their products, their eruptive behavior, and their hazards. Papers aimed at understanding the deeper structure of volcanoes, and the evolution of magmatic systems using geochemical, petrological, and geophysical techniques are also published. Material is published in four sections: Review Articles; Research Articles; Short Scientific Communications; and a Forum that provides for discussion of controversial issues and for comment and reply on previously published Articles and Communications.
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