PyBetVH: A Python tool for probabilistic volcanic hazard assessment and for generation of Bayesian hazard curves and maps

Tonini, Roberto; Sandri, Laura; Thompson, Mary Anne

doi:10.1016/j.cageo.2015.02.017

Cited by 31 publications

(34 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Three valuable hazard products are derived from our assessment (e.g., Tonini et al, ): (1) hazard curves; (2) hazard maps; and (3) probability maps (see section 3).…”

Section: Methodsmentioning

confidence: 99%

Towards Quantitative Volcanic Risk of Pyroclastic Density Currents: Probabilistic Hazard Curves and Maps Around Somma‐Vesuvius (Italy)

et al. 2018

View full text Add to dashboard Cite

Pyroclastic density currents (PDCs) are hot flowing mixtures of gas and pyroclasts that can cause widespread loss of life and structural damage around the erupting volcano. Hazard assessments that include quantification of aleatory and epistemic uncertainty are a necessary step toward calculating volcanic risk of PDCs in an accurate and complete manner. We develop a three‐stage procedure to quantify such uncertainties for dense PDCs. First, the TITAN2D model is parameterized to simulate the PDC phenomenology at the target volcano. Second, TITAN2D is coupled with Polynomial Chaos Quadrature to propagate aleatory uncertainty from model parameters to hazard intensity measures (flow depth and speed). Third, the TITAN2D‐PCQ analysis is merged with the Bayesian Event Tree for Volcanic Hazard to include other volcano‐specific aleatory uncertainty and estimates of epistemic uncertainty. A comprehensive collection of probabilistic hazard curves and maps for flow depth and speed around the volcano is obtained through this methodology and its application is illustrated at Somma‐Vesuvius (Italy). Our results indicate that, given an eruption from the current central crater, exceedance probabilities are around 30% (aleatory uncertainty only) and between 10% and 60% (aleatory and epistemic uncertainty), for flow depth = 1 m and flow speed = 2 m/s, over the first 2–3 km around the vent. Dense PDCs faster than 30 m/s may cover areas about 50 km2 around the vent, on average, 1 every 10 eruptions. This type of probabilistic hazard assessment represents a crucial step toward quantitative volcanic risk of dense PDCs at Somma‐Vesuvius and worldwide.

show abstract

“…Three valuable hazard products are derived from our assessment (e.g., Tonini et al, ): (1) hazard curves; (2) hazard maps; and (3) probability maps (see section 3).…”

Section: Methodsmentioning

confidence: 99%

Towards Quantitative Volcanic Risk of Pyroclastic Density Currents: Probabilistic Hazard Curves and Maps Around Somma‐Vesuvius (Italy)

et al. 2018

View full text Add to dashboard Cite

show abstract

“…The inclusion of the nonmagmatic unrest allows for a more comprehensive probabilistic analysis of volcanic systems, with the aim of integrating information derived from multidisciplinary observations. The software has been implemented in the Python programming language and includes a Graphical User Interface to support the analysis, following the same approach used for the PyBetVH software [ Tonini et al ., ]. The software calculates probabilities in a runtime of a few seconds (it can increase to a few minutes if the number of vent locations is greater than several hundreds) and allows interactively selecting and visualizing the results corresponding to different branches of the event tree.…”

Section: Discussionmentioning

confidence: 99%

Application of the probabilistic model BET_UNREST during a volcanic unrest simulation exercise in Dominica, Lesser Antilles

Constantinescu

Robertson

Lindsay

et al. 2016

Geochem Geophys Geosyst

Self Cite

View full text Add to dashboard Cite

We report on the first “real‐time” application of the BET_UNREST (Bayesian Event Tree for Volcanic Unrest) probabilistic model, during a VUELCO Simulation Exercise carried out on the island of Dominica, Lesser Antilles, in May 2015. Dominica has a concentration of nine potentially active volcanic centers and frequent volcanic earthquake swarms at shallow depths, intense geothermal activity, and recent phreatic explosions (1997) indicate the region is still active. The exercise scenario was developed in secret by a team of scientists from The University of the West Indies (Trinidad and Tobago) and University of Auckland (New Zealand). The simulated unrest activity was provided to the exercise's Scientific Team in three “phases” through exercise injects comprising processed monitoring data. We applied the newly created BET_UNREST model through its software implementation PyBetUnrest, to estimate the probabilities of having (i) unrest of (ii) magmatic, hydrothermal or tectonic origin, which may or may not lead to (iii) an eruption. The probabilities obtained for each simulated phase raised controversy and intense deliberations among the members of the Scientific Team. The results were often considered to be “too high” and were not included in any of the reports presented to ODM (Office for Disaster Management) revealing interesting crisis communication challenges. We concluded that the PyBetUnrest application itself was successful and brought the tool one step closer to a full implementation. However, as with any newly proposed method, it needs more testing, and in order to be able to use it in the future, we make a series of recommendations for future applications.

show abstract

“…The software implementation is very similar to the one adopted for the PyBetVH tool (https://vhub.org/tools/betvh) and for details on technical features and input formatting one can refer to Tonini et al . [, see, in particular, Section 4, Appendices and Electronic Supplementary Material] or to the tool's link on Vhub (https://vhub.org/resources/betunrest and https://vhub.org/wiki/PyBetToolsUserGuide).…”

Section: Methodsmentioning

confidence: 99%

A new Bayesian Event Tree tool to track and quantify volcanic unrest and its application to Kawah Ijen volcano

Tonini

Sandri

Rouwet

et al. 2016

Geochem Geophys Geosyst

Self Cite

View full text Add to dashboard Cite

Although most of volcanic hazard studies focus on magmatic eruptions, volcanic hazardous events can also occur when no migration of magma can be recognized. Examples are tectonic and hydrothermal unrest that may lead to phreatic eruptions. Recent events (e.g., Ontake eruption on September 2014) have demonstrated that phreatic eruptions are still hard to forecast, despite being potentially very hazardous. For these reasons, it is of paramount importance to identify indicators that define the condition of nonmagmatic unrest, in particular for hydrothermal systems. Often, this type of unrest is driven by movement of fluids, requiring alternative monitoring setups, beyond the classical seismic‐geodetic‐geochemical architectures. Here we present a new version of the probabilistic BET (Bayesian Event Tree) model, specifically developed to include the forecasting of nonmagmatic unrest and related hazards. The structure of the new event tree differs from the previous schemes by adding a specific branch to detail nonmagmatic unrest outcomes. A further goal of this work consists in providing a user‐friendly, open‐access, and straightforward tool to handle the probabilistic forecast and visualize the results as possible support during a volcanic crisis. The new event tree and tool are here applied to Kawah Ijen stratovolcano, Indonesia, as exemplificative application. In particular, the tool is set on the basis of monitoring data for the learning period 2000–2010, and is then blindly applied to the test period 2010–2012, during which significant unrest phases occurred.

show abstract

PyBetVH: A Python tool for probabilistic volcanic hazard assessment and for generation of Bayesian hazard curves and maps

Cited by 31 publications

References 20 publications

Towards Quantitative Volcanic Risk of Pyroclastic Density Currents: Probabilistic Hazard Curves and Maps Around Somma‐Vesuvius (Italy)

Towards Quantitative Volcanic Risk of Pyroclastic Density Currents: Probabilistic Hazard Curves and Maps Around Somma‐Vesuvius (Italy)

Application of the probabilistic model BET_UNREST during a volcanic unrest simulation exercise in Dominica, Lesser Antilles

A new Bayesian Event Tree tool to track and quantify volcanic unrest and its application to Kawah Ijen volcano

Contact Info

Product

Resources

About