Pilot study of eruption forecasting with muography using convolutional neural network
Yukihiro Nomura, Mitsutaka Nemoto, Naoto Hayashi, Shouhei Hanaoka, Masaki Murata, T. Yoshikawa, Yoshitaka Masutani, Eriko Maeda, Osamu Abe, Hiroyuki Tanaka
Abstract
Muography is a novel method of visualizing the internal structures of active volcanoes by using high-energy near-horizontally arriving cosmic muons. The purpose of this study is to show the feasibility of muography to forecast the eruption event with the aid of the convolutional neural network (CNN). In this study, seven daily consecutive muographic images were fed into the CNN to compute the probability of eruptions on the eighth day, and our CNN model was trained by hyperparameter tuning with the Bayesian optimization algorithm. By using the data acquired in Sakurajima volcano, Japan, as an example, the forecasting performance achieved a value of 0.726 for the area under the receiver operating characteristic curve, showing the reasonable correlation between the muographic images and eruption events. Our result suggests that muography has the potential for eruption forecasting of volcanoes.