STUDY ON MONITORING EFFICIENCY OF ACTIVE VOLCANO BY DEEP LEARNING

Abstract

 In Japan, there are 111 active volcanoes that account for about 7% of the world. Once a volcano erupts, devastating damage occurs due to eruption events such as volcanic cinders, pyroclastic flows and debris flows. Therefore, it is important to promptly detect signs of eruption and take countermeasures through regular observation and monitoring of active volcanoes.

 In this study, we considered the method using deep learning of AI technology to improve the efficiency of active volcano monitoring. Specifically, by using CNN(Convolutional Neural Network) of the deep learning model, a model that removes noise such as clouds and fog that hinders volcano monitoring and a model that detects eruption events such as smoke of volcano and debris flows were constructed. The target volcano was Yakedake, one of the 50 active volcanoes that the Japan Meteorological Agency is constantly monitoring. As a result, it was shown that deep learning could be an effective technique for improving the efficiency of active volcano monitoring.