Volcanoes with shallow hydrothermal systems are often accompanied by background volcanic activity such as fumarolic activity, microseismicity, and ground deformation even in the non-eruptive phase. When elevated, they are said to be in a state of “unrest.” It is not difficult to imagine that such events of unrest reflect changes in the state of the shallow hydrothermal system beneath a volcano. However, there is currently no method by which these events can be used to quantitatively evaluate eruption imminency or predict eruption intensity based on physical and/or chemical models. A potentially useful application of such unrest events for probabilistically forecasting eruptions is discussed. First, the method proposed by Hashimoto et al. (2019) for compiling and evaluating the sources of unrest events, such as thermal demagnetization, is described. Then, the volcanic unrest index (VUI) of Potter et al. (2015a) is proposed as another key tool. Finally, a concept is proposed for integrating the VUI and the unrest data to make probabilistically forecasting eruptions feasible. Also described is a recent attempt to introduce the VUI for evaluating a volcano in Japan. Information on sources of unrest in the form of the scatter plot of Hashimoto et al. (2019) can be used as one of the rating criteria on the VUI worksheet. The key idea is to divide the source diagram into regions based on the probability of posterior eruptions given unrest events and to assign VUI scores to these regions. Such a procedure may augment the VUI's function, partially enabling probability-based eruption forecasting. Irrespective of whether the VUI is applied or not, it is essential to obtain temporally homogeneous monitoring data during both eruptive and non-eruptive periods for a quantitative evaluation of unrest events. Surveys and analyses carried out regularly over long time periods also play an equally important role. Therefore, to realize of probabilistic eruption forecasting, it is fundamentally important that monitoring networks are run properly and the data are shared appropriately.
