Classification of volcanic ash particles using a convolutional neural network and probability

TL;DR

This paper presents a CNN-based method for classifying volcanic ash particles into four shape categories, enabling quantitative analysis of complex particles without subjective categorization or predefined shape parameters.

Contribution

The study introduces a CNN approach that accurately classifies ash particle shapes and estimates their mixing ratios, offering a new quantitative taxonomy for volcanic ash analysis.

Findings

CNN recognized basal shapes with over 90% accuracy.

Classified ash particles based on mixing ratios aligned with eruption types.

Quantitative classification correlates with eruption mechanisms.

Abstract

Analyses of volcanic ash are typically performed either by qualitatively classifying ash particles by eye or by quantitatively parameterizing its shape and texture. While complex shapes can be classified through qualitative analyses, the results are subjective due to the difficulty of categorizing complex shapes into a single class. Although quantitative analyses are objective, selection of shape parameters is required. Here, we applied a convolutional neural network (CNN) for the classification of volcanic ash. First, we defined four basal particle shapes (blocky, vesicular, elongated, rounded) generated by different eruption mechanisms (e.g., brittle fragmentation), and then trained the CNN using particles composed of only one basal shape. The CNN could recognize the basal shapes with over 90% accuracy. Using the trained network, we classified ash particles composed of multiple basal shapes based on the output of the network, which can be interpreted as a mixing ratio of the four basal shapes. Clustering of samples by the averaged probabilities and the intensity is consistent with the eruption type. The mixing ratio output by the CNN can be used to quantitatively classify complex shapes in nature without categorizing forcibly and without the need for shape parameters, which may lead to a new taxonomy.