  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.
