ALBERT

Description: In this study, we use a doubly stochastic model to develop a short-term eruption forecasting method based on precursory signals. The method enhances the Failure Forecast Method (FFM) equation, which represents the potential cascading of signals leading to failure. The reliability of such forecasts is affected by uncertainty in data and volcanic system behavior and, sometimes, a classical approach poorly predicts the time of failure. To address this, we introduce stochastic noise into the original ordinary differential equation, converting it into a stochastic differential equation, and systematically characterize the uncertainty. Embedding noise in the model can enable us to have greater forecasting skill by focusing on averages and moments. In our model, the prediction is thus perturbed inside a range that can be tuned, producing probabilistic forecasts. Furthermore, our doubly stochastic formulation is particularly powerful in that it provides a complete posterior probability distribution, allowing users to determine a worst-case scenario with a specified level of confidence. We verify the new method on simple historical datasets of precursory signals already studied with the classical FFM. The results show the increased forecasting skill of our doubly stochastic formulation. We then present a preliminary application of the method to more recent and complex monitoring signals.

Description: Published

Description: San Francisco (CA)

Description: 6V. Pericolosità vulcanica e contributi alla stima del rischio

Description: Probabilistic hazard maps are used to graphically represent forecasts of potentially hazardous volcanic processes associated with an eruption. The construction of a probabilistic hazard map requires the characterization of all possible scenarios (aleatoric variability) that might lead to an event of interest. These scenarios then must be “fed in” to a physical model of the geophyiscal process which are typically computationally expensive to exercise. We present a hazard-mapping tool for the Long Valley region of California. This tool utilizes statistical surrogates of the physical model (in this demonstration, TITAN2D simulations of pyroclastic density currents) to perform rapid hazard assessment. It effectively replaces simulations that take O(min)-O(hours) with function evaluation which take a fraction of a second to exercise. This speed up enables tremendous flexibility in scenario modeling as we can quickly construct and compare probabilistic hazard maps under a variety of scenario models. Furthermore, we can quickly update a probabilistic hazard map as new data or emergent situations arise.

Description: Published

Description: Portland (OR)

Description: 6V. Pericolosità vulcanica e contributi alla stima del rischio

Description: The Long Valley volcanic region is an active volcanic area situated at the east base of the Sierra Nevada escarpment, and dominated by a 32-km wide resurgent caldera of ~760 ka. Eruptions during the last 180 ka have been localized at Mammoth Mountain on the western rim of the caldera, and along the Mono-Inyo Craters volcanic chain stretching about 45 km northward. The past eruption record is characterized by significant acceleration during the last 6 ka. In 1325 - 1350 AD there was a ~1 km3 eruption along a 25 km section of the Mono-Inyo Craters chain. The most recent eruption in ~1700 AD created Paoha Island in Mono Lake. The last eruption in the southern part of the system was ~10 ka (Red Cones), but continuous CO2 degassing, potential precursory signals and recent geophysical studies suggest that the Mammoth Mountain area could be active again. Multiple spatial probability models were developed, based on past vents locations. One of the models couples this information with pre-existing faults, sampling a fault outcrop site as a parameter of proximity to the vent location forecast. Similarly, different Poisson-type models have been developed for modeling the temporal sequence of eruptions and making estimates for the current volcanic intensity of the system (i.e. the expected rate of eruptions per year). The models implement various self-excitement features, assuming that the expected volcanic intensity is increased by past events and is instead decreased by prolonged periods of quiescence. All the available models can be considered as different “experts”, and this has significant analogies with “Structured Expert Judgment” problems. “Bayesian Model Averaging” is presented as a flexible technique for combining the results of multiple models, relying on their performance in hindcasting the past record. The analysis is setup in a doubly stochastic framework, enabling us to incorporate some of the main sources of epistemic uncertainty - these include the effects of the unknown relevance of Mammoth Mountain area, the incompleteness of the past record and mapped faults, and the uncertain age (and location) of past events. Our findings provide a rational basis for hazard mapping of the next eruption in the Long Valley volcanic region, suggesting that the hazard associated with Mammoth Mountain volcanism should be carefully reevaluated.

Description: Published

Description: Portland (OR)

Description: 6V. Pericolosità vulcanica e contributi alla stima del rischio

Description: We present two models using monitoring data in the production of volcanic eruption forecasts. The first model enhances the well-established failure forecast method introducing an SDE in its formulation. In particular, we developed new method for performing short-term eruption timing probability forecasts, when the eruption onset is well represented by a model of a significant rupture of materials. The method enhances the well-known failure forecast method equation. We allow random excursions from the classical solutions. This provides probabilistic forecasts instead of deterministic predictions, giving the user critical insight into a range of failure or eruption dates. Using the new method, we describe an assessment of failure time on present-day unrest signals at Campi Flegrei caldera (Italy) using either seismic count and ground deformation data. The new formulation enables the estimation on decade-long time windows of data, locally including the effects of variable dynamics. The second model establishes a simple method to update prior vent opening spatial maps. The prior reproduces the two-dimensional distribution of past vent distribution with a Gaussian Field. The likelihood relies on a one-dimensional variable characterizing the chance of material failure locally, based, for instance, on the horizontal ground deformation. In other terms, we introduce a new framework for performing short-term eruption spatial forecasts by assimilating monitoring signals into a prior (“background”) vent opening map. To describe the new approach, first we summarize the uncertainty affecting a vent opening map pdf of Campi Flegrei by defining an appropriate Gaussian random field that replicates it. Then we define a new interpolation method based on multiple points of central symmetry, and we apply it on discrete GPS data. Finally, we describe an application of the Bayes’ theorem that combines the prior vent opening map and the data-based likelihood product-wise. We provide examples based on either seismic count and interpolated ground deformation data collected in the Campi Flegrei volcanic area.

Description: Published

Description: San Francisco

Description: 6V. Pericolosità vulcanica e contributi alla stima del rischio

Description: Eruption probability assessments in the Long-Valley volcanic region (CA) Project Hazard SEES: Persistent volcanic crises resilience in the face of prolonged and uncertain risk, National Science Foundation, 2015 - 2018. Andrea Bevilacqua(1), Marcus Bursik(1), Abani K. Patra(2), E. Bruce Pitman(3), Qingyuan Yang(1) (1) University at Buffalo, Department of Geology (2) University at Buffalo, Department of Mechanical and Aerospace Engineering (3) University at Buffalo, Department of Materials Design and Innovation GLY 597SEM - Volcanology Seminar, 22 September 2017, Buffalo (NY)

Description: Published

Description: Buffalo (NY)

Description: 6V. Pericolosità vulcanica e contributi alla stima del rischio

Description: We present two models using precursory information in the production of volcanic eruption forecasts. The first model enhances the well-established failure forecast method introducing an SDE in its formulation. The second model establishes a simple method to update prior spatial maps. The prior reproduce the two-dimensional distribution of past activity with a Gaussian Field. The likelihood relies on a one-dimensional variable characterizing the chance of material failure locally, based on the horizontal ground deformation.

Description: Published

Description: Valencia

Description: 6V. Pericolosità vulcanica e contributi alla stima del rischio