Читать книгу Muography - Группа авторов - Страница 59

The application of machine learning techniques on remote sensing data has opened new possibilities in automated classification and forecasting of geophysical phenomena, including those triggering natural hazards, such as landslides or volcano eruptions. Muographic imaging can reveal the structural and density changes in geological bodies in connection with subsurface geophysical phenomena. We present the recent progress in machine learning‐based muographic image processing and discuss how it may reveal subsurface volcanic phenomena and contribute to volcano eruption forecasting. Our analysis is based on data sets collected by the Multi‐Wire‐Proportional‐Chamber‐based Muography Observation System during the eruption episodes of Minamidake crater of Sakurajima volcano between October 2018 and July 2020. The area under the curve score of 0.761, provided by the receiver operating characteristic curve analysis of a convolutional neural network, shows correlation between the impending vulcanian eruptions of Minamidake crater and the muographic images captured on the previous days.