# Shape recognition of volcanic ash by simple convolutional neural network

## TL;DR

This paper demonstrates that a simple convolutional neural network can effectively recognize volcanic ash shapes from images with about 90% accuracy, bypassing complex parameter selection.

## Contribution

It applies a CNN model originally designed for handwritten digits to tephra shape recognition, simplifying the process and improving accuracy.

## Key findings

- Achieved approximately 90% recognition accuracy.
- Eliminated need for complex shape parameter selection.
- Validated CNN effectiveness on volcanic ash images.

## Abstract

Shape analyses of tephra grains result in understanding eruption mechanism of volcanoes. However, we have to define and select parameter set such as convexity for the precise discrimination of tephra grains. Selection of the best parameter set for the recognition of tephra shapes is complicated. Actually, many shape parameters have been suggested. Recently, neural network has made a great success in the field of machine learning. Convolutional neural network can recognize the shape of images without human bias and shape parameters. We applied the simple convolutional neural network developed for the handwritten digits to the recognition of tephra shapes. The network was trained by Morphologi tephra images, and it can recognize the tephra shapes with approximately 90% of accuracy.