The Piton de la Fournaise volcano exhibits frequent eruptions preceded by seismic swarms and is a good target to test hypotheses about magmatically induced variations in seismic wave properties. We use a permanent station network and a portable broadband network to compare seismic anisotropy measured via shear wave splitting with geodetic displacements, ratios of compressional to shear velocity (Vp/Vs), earthquake focal mechanisms, and ambient noise correlation analysis of surface wave velocities and to examine velocity and stress changes from 2000 through 2012. Fast directions align radially to the central cone and parallel to surface cracks and fissures, suggesting stress-controlled cracks. High Vp/Vs ratios under the summit compared with low ratios under the flank suggest spatial variations in the proportion of fluid-filled versus gas-filled cracks. Secular variations of fast directions (ϕ) and delay times (dt) between split shear waves are interpreted to sense changing crack densities and pressure. Delay times tend to increase while surface wave velocity decreases before eruptions. Rotations of ϕ may be caused by changes in either stress direction or fluid pressure. These changes usually correlate with GPS baseline changes. Changes in shear wave splitting measurements made on multiplets yield several populations with characteristic delay times, measured incoming polarizations, and fast directions, which change their proportion as a function of time. An eruption sequence on 14 October 2010 yielded over 2000 shear wave splitting measurements in a 14 h period, allowing high time resolution measurements to characterize the sequence. Stress directions from a propagating dike model qualitatively fit the temporal change in splitting.
Volcanoes with crater lakes and/or extensive hydrothermal systems pose significant challenges with respect to monitoring and forecasting eruptions, but they also provide new opportunities to enhance our understanding of magmatic-hydrothermal processes. Their lakes and hydrothermal systems serve as reservoirs for magmatic heat and fluid emissions, filtering and delaying the surface expressions of magmatic unrest and eruption, yet they also enable sampling and monitoring of geochemical tracers. Here, we describe the outcomes of a highly focused international experimental campaign and workshop carried out at Kawah Ijen volcano, Indonesia, in September 2014, designed to answer fundamental questions about how to improve monitoring and eruption forecasting at wet volcanoes.
[1] Volcanic eruptions impact on societal risk, and volcanic hazard assessment is a necessary ingredient for decision-makers. However, the prediction of volcanic eruptions remains challenging due to the complexity and the non-linearity of volcanic processes. Identified forerunners such as increasing seismicity or deformation of the volcanic edifice prior to eruption are not deterministic. In this study, we use statistical methods to identify and discriminate precursory patterns to eruptions, on three sets of observables of Piton de la Fournaise volcano. We analyzed the short-term (i.e. the inter-eruptive period) time series of the seismicity rate, the deformation and the seismic velocity changes (deduced from seismic noise cross-correlations) over the period 1999-2006, with two main goals. First, we characterize the average pre-eruptive time patterns before 22 eruptions using superposed epoch analysis for the three observables. Using daily rate values, we resolve (1) a velocity change within 100-50 days from the eruptions onsets, then a plateau value up to eruption onset; (2) a power law increase in seismicity rate from noise level 15-10 days before eruption time; (3) an increase of displacement rate on the eruption day. These results support a three step mechanism leading to magma transfers toward the surface. Second we use pattern recognition techniques and the formalization of error diagrams to quantify the predictive power of each forerunner either as used independently or as combined to each other. We show that when seismicity rate alone performs the best prediction in the failure to predict versus alarm duration space, the combination of the displacement and seismicity data reduces the false alarm rate. We further propose a tool which explores the prediction results in order to optimize prediction strategy for decision-makers, as a function of the risk value.
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