Abstract
Eruption forecasting is a challenging task because of the inherent complexity of volcanic systems. Despite remarkable efforts to develop complex models in order to explain volcanic processes prior to eruptions, the material Failure Forecast Method (FFM) is one of the very few techniques that can provide a forecast time for an eruption. However, the method requires testing and automation before being used as a real-time eruption forecasting tool at a volcano. We developed an automatic algorithm to issue forecasts from volcanic tremor increase episodes recorded by Real-time Seismic Amplitude Measurement (RSAM) at one station and optimised this algorithm for the period August 2011–January 2014 which comprises the recent unrest period at White Island volcano (Whakaari), New Zealand. A detailed residual analysis was paramount to select the most appropriate model explaining the RSAM time evolutions. In a hindsight simulation, four out of the five small eruptions reported during this period occurred within a failure window forecast by our optimised algorithm and the probability of an eruption on a day within a failure window was 0.21, which is 37 times higher than the probability of having an eruption on any day during the same period (0.0057). Moreover, the forecasts were issued prior to the eruptions by a few hours which is important from an emergency management point of view. Whereas the RSAM time evolutions preceding these four eruptions have a similar goodness-of-fit with the FFM, their spectral characteristics are different. The duration–amplitude distributions of the precursory tremor episodes support the hypothesis that several processes were likely occurring prior to these eruptions. We propose that slow rock failure and fluid flow processes are plausible candidates for the tremor source of these episodes. This hindsight exercise can be useful for future real-time implementation of the FFM at White Island. A similar methodology could also be tested at other volcanoes even if only a limited network is available.