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Bursik, Marcus
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- PublicationOpen AccessA SDE Framework for Volcanic Precursors(2019)
; ; ; ; ; ; ; ; ; ; ; ; ; ; ;; ; ;; ; ; 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.74 18 - PublicationOpen AccessVolcanic eruption time eruption forecasting using a stochastic enhancement of the Failure Forecasting Method(2018-12)
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; 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.70 18 - PublicationOpen AccessUtilizzo preliminare del failure forecast method sui dati GPS di spostamento orizzontale registrati nella caldera dei Campi Flegrei dal 2011 al 2020(2020-12)
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; In questo studio, usando il failure forecast method si fornisce una stima preliminare del failure time dei segnali di unrest della caldera dei Campi Flegrei, concentrandoci sul dato di deformazione orizzontale registrato in 11 stazioni GPS nel periodo [2011, 2020]. In particolare, si applica un approccio probabilistico che modifica il metodo classico incorporando un rumore stocastico nelle equazioni linearizzate e una proprietà di mean reversion per modularne gli effetti. La formulazione stocastica permette di analizzare i dati registrati in ca. 10 anni di monitoraggio, includendo, tramite il rumore, gli effetti della dinamica variabile che caratterizza l’unrest della caldera. Si forniscono previsioni temporali con quantificazione dell’incertezza, cioè informazioni su un intervallo di possibili failure times.975 73 - PublicationOpen AccessEruption probability assessments in the Long-Valley volcanic region (CA)(2017)
; ; ; ; ; ; ; ; ;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)32 10 - PublicationOpen AccessValidation data for manuscript "Novel statistical emulator construction for volcanic ash transport model Ash3d with physically-motivated measures"This database presents the testing and validation data used and presented in the manuscript titled "Novel statistical emulator construction for volcanic ash transport model Ash3d with physically-motivated measures". The manuscript is submitted to the Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences. Please refer to the readme file for more details of the data.
54 6 - PublicationOpen AccessProbabilistic hazard maps of pyroclastic density currents in the Long Valley Volcanic RegionThis is a repository of the maps and graphs of PDC inundation probability distributions and related uncertainty indices for the Long Valley volcanic region, according to the physical and statistical model in: Regis Rutarindwa, Elaine T. Spiller, Andrea Bevilacqua, Marcus I. Bursik, and Abani K. Patra (2019) Dynamic probabilistic hazard mapping in the Long Valley Volcanic Region CA: integrating vent opening maps and statistical surrogates of physical models of pyroclastic density currents, Journal of Geophysical Research, Solid Earth, https://doi.org/10.1029/2019JB017352 All the maps are available in ASCII text-file format, UTM coordinates (Northing, Easting), Zone 11 North, WGS84 horizontal datum. They are included in the zip archive. Each ASCII file is identified with a figure in the paper. In detail: Figure 4a: dynamic probabilistic hazard maps conditioned on a PDC event of volume of 1 km3, and based on a uniform vent opening probability distribution. Figure 4b: dynamic probabilistic hazard maps conditioned on a PDC event of volume of 1 km3, and based on a Gaussian mixture model based on known past vent locations. Figure 4c: dynamic probabilistic hazard maps conditioned on a PDC event of volume of 1 km3, and based on the mean values of the doubly stochastic model in Bevilacqua, Bursik, et al. (2017). Figure 5c: uncertainty index of the PHM related to the values of the doubly stochastic model in Bevilacqua, Bursik, et al. (2017). Figure 8ab: mean probabilistic hazard based on the model of Bevilacqua, Bursik, et al. (2017) and conditioned on a PDC event of volume of 0.01 km3. Figure 8c: uncertainty index of the probabilistic hazard based on the model of Bevilacqua, Bursik, et al. (2017), conditioned on a PDC event of volume of 0.01 km3.
64 3 - PublicationRestrictedLate Quaternary Eruption Record and Probability of Future Volcanic Eruptions in the Long Valley Volcanic Region (CA, USA)(2018)
; ; ; ; ; ; ; ; ; ; ; ; ;The Long Valley volcanic region, eastern California, has been characterized by recurrent and generally explosive eruptions in the past 180,000 years, originating from a N/S elongated area ~50 km in extent, including the Mammoth Mountain lava dome complex, the Mono-Inyo volcanic chain, and their peripheries. Several temporal clusters of activity have been observed in a relatively well-preserved time-stratigraphic record, which is nevertheless affected by uncertainties. This study has two main objectives: (1) to fully describe the past eruption record by using a stochastic model capable of combining radiometric ages and stratigraphic constraints and, (2) based on the uncertainty assessment, to develop a doubly stochastic, long-term temporal model based on the current situation. Multiple approaches are described and compared, and multimodel forecasts are also presented. Our findings provide fundamental information for hazard assessment and forecasting of the next eruption in the Long Valley volcanic region, of which the mean probability of occurrence is estimated to be ~2.5% in the next 10 years and ~22.5% in the next 100 years.320 3 - PublicationOpen AccessProbabilistic Enhancement of the Failure Forecast Method Using a Stochastic Differential Equation and Application to Volcanic Eruption ForecastsWe introduce a doubly stochastic method for performing material failure theory based forecasts of volcanic eruptions. The method enhances the well known Failure Forecast Method equation, introducing a new formulation similar to the Hull-White model in financial mathematics. In particular, we incorporate a stochastic noise term in the original equation, and systematically characterize the uncertainty. The model is a stochastic differential equation with mean reverting paths, where the traditional ordinary differential equation defines the mean solution. Our implementation allows the model to make excursions from the classical solutions, by including uncertainty in the estimation. The 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 apply the new method on historical datasets of precursory signals, across a wide range of possible values of convexity in the solutions and amounts of scattering in the observations. The results show the increased forecasting skill of the doubly stochastic formulation of the equations if compared to statistical regression.
675 23 - PublicationOpen AccessA Bayesian Framework for Rheology Model Combination and UQ in Simulation of Geophysical Mass Flows(2017)
; ; ; ; ; ; ;In hazard assessment for geophysical mass flows, we seek to construct accurate and reliable maps that show regions with high hazard. •Acceptably accurate numerical simulation of complex geophysical mass flows is of crucial importance. •Modeling mechanical behavior of such flows or the flow rheology presents a major difficulty. •TITAN2D v. 4.0, the geophysical mass flow simulator, offers multiple well-known choices for flow rheology – Mohr-Coulomb, Pouliquen-Forterre, Voellmy-Salm. •In this contribution, we present a Bayesian framework to combine the simulation results of alternative models and quantify the uncertainty in rheology models for both experimental and natural terrain flows.42 17 - PublicationOpen AccessHistory of LVVR explosive eruptions Vent opening probability maps(2016)
; ; ; ; ; ; ;11 August (Thursday) Morning – review proposal, introduction of all team-members, short talks on discipline specific tasks by investigators with initial ideas on outputs. Venue: SNARL conference room. 8:00 – Vans to SNARL 8:30-9:00 – Welcome and introduction to SNARL and region 9:00-10:00 – Update on Kilauea case study science – Chris G 10:00-10:30 – CalVO: new results and ongoing research – Maggie M 10:30-11:00 – Constraining subsurface processes at Long Valley – Michael M/Befus/Black 11:00-11:30 – History of LVVR explosive eruptions – Marcus/Andrea/Yang 11:30-12:00 – Overview of LVVR crisis communications and social science aspects – Chris G/Michael L 12:00-12:30 – Hazards/statistics update and ideas – Elaine/Robert/James/Bruce P 12:30-14:00 – Lunch with discussion Afternoon – field trip: Long Valley, Mammoth (Marcus)42 11
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