Bayesian inversion of data from effusive volcanic eruptions using physics‐based models: Application to Mount St. Helens 2004–2008

Anderson, K. R.; Segall, P.

doi:10.1002/jgrb.50169

Cited by 105 publications

(125 citation statements)

References 74 publications

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“…Anderson and Segall, 2013). In our case, we started by inverting the Integrated TGSD made of the weighted Field and Radar distributions.…”

Section: Inversion Modelling Strategymentioning

confidence: 99%

Reconstructing volcanic plume evolution integrating satellite and ground-based data: Application to the 23rd November 2013 Etna eruption

Poret¹,

Corradini²,

Merucci³

et al. 2018

Preprint

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Abstract. Recent explosive eruptions recorded from different volcanoes worldwide (e.g. Hekla in 2000, Eyjafjallajökull in 152010, Cordón-Caulle in 2011) demonstrated the necessity of a better assessment of the eruption source parameters (e.g. column height, mass eruption rate and especially the Total Grain-Size Distribution -TGSD) to reduce the uncertainties associated with the far-travelling airborne ash mass. To do so, volcanological studies started to integrate observations in order to use more realistic numerical inputs, crucial for taking robust volcanic risk mitigation actions. On 23 rd November 2013, Etna volcano (Italy) erupted producing a 10-km height plume, from which two volcanic clouds were observed at two different altitudes from 20 satellite (MSG-SEVIRI, MODIS). One was described as mainly composed by very fine ash (i.e. PM20), whereas the second one as made of ice/SO2 droplets (i.e. not measurable in terms of ash mass). Atypical north-easterly winds transported the tephra from Etna towards the Puglia region (southern Italy), permitting tephra sampling in proximal (i.e. ~5-25 km from source) and medial areas (i.e. Calabria region, ~160km). Based on the field data analysis, we estimated the TGSD but the paucity of data (especially related to the fine ash fraction) prevented it from being entirely representative of the initial magma fragmentation. 25To better estimate the TGSD covering the entire grain-size spectrum, we integrated the available field data with X-band weather radar and satellite retrievals. The resulting TGSD is used as input for the FALL3D tephra dispersal numerical model to reconstruct the tephra loading and the far-travelling airborne ash mass. The optimal TGSD is selected by solving an inverse problem through a best-fit with the field, ground-based and satellite-based measurements. The results suggest a total erupted mass of 1.2 × 10 9 kg, which is very similar to the field-derived value of 1.3 × 10 9 kg, and a TGSD with a PM20 fraction between 30 3.6 and 9.0 wt%.

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“…Anderson and Segall, 2013). In our case, we started by inverting the Integrated TGSD made of the weighted Field and Radar distributions.…”

Section: Inversion Modelling Strategymentioning

confidence: 99%

Reconstructing volcanic plume evolution integrating satellite and ground-based data: Application to the 23rd November 2013 Etna eruption

Poret¹,

Corradini²,

Merucci³

et al. 2018

Preprint

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“…Bayesian approaches are often used in conjunction with Event Trees (e.g., Newhall and Hoblitt 2002;Marzocchi et al 2004Marzocchi et al , 2008Newhall and Pallister 2015, and references therein), that represent the complex ramification of possible outcomes, each one quantified as a probability distribution which is allowed to evolve as long as new information is added (e.g., when new observations are available). To-date, Bayesian approaches have been employed in a large number of situations in volcanology, including forecasts of volcanic hazards over the shortterm (Aspinall et al 2003(Aspinall et al , 2006Marzocchi et al 2008; Lindsay et al 2010;Brancato et al 2011Brancato et al , 2012Bell and Kilburn 2012;Marzocchi and Bebbington 2012;Sandri et al 2009Sandri et al , 2012Selva et al 2012Selva et al , 2014Garcia-Aristizabal et al 2013;Anderson and Segall 2013;Rouwet et al 2014;Aspinall and Woo 2014;Sobradelo et al 2015;Boue et al 2015;Tonini et al 2016;Bartolini et al 2016) as well as over the long-term (Martin et al 2004;Baxter et al 2008;Neri et al 2008;Orsi et al 2009;Marzocchi et al 2008Marzocchi et al , 2010Sobradelo and Martì 2010;Passarelli et al 2010a, b;Selva et al 2012;Marzocchi and Bebbington 2012;Sandri et al 2012Sandri et al , 201...…”

Section: Rational Volcanic Hazard Forecastsmentioning

confidence: 99%

Rational volcanic hazard forecasts and the use of volcanic alert levels

Papale

2017

J Appl. Volcanol.

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Volcanologists make hazard forecasts in order to contribute to volcanic risk assessments and decision-making, in areas where volcanic phenomena have the potential to impact societal assets. Present-day forecasts related to the potential occurrence of an eruption mostly take the form of alert levels, that are established by volcano scientists with the aim of communicating the state of a volcano and its possible short-term evolution. Here I analyse current alert level systems and their role in decision-making processes. I show that the use of such systems implies predictive capabilities not supported by corresponding levels of confidence in the knowledge of the volcano. Their use also implies an assumption of volcano scientist responsibility for decisions that goes beyond the expertise of the scientist, and which, in most countries, is not granted by a corresponding societal mandate. A rational volcanic hazard forecast system accepts instead the uncertain nature of volcanic processes and the consequent limited predictive capabilities; forecasts expressed as probabilities, or better as probability distributions, reflect a rational attitude by scientists and assure clear roles that reflect both expertise and societal mandate to any group involved in the management of a volcanic crisis. Exceptions where the use of volcanic alert levels may lead to efficient management are also discussed.

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“…Kennedy and O'Hagan, 2001;Craig et al, 2001), constructing posterior probability distributions by refining specified prior distributions using observations (see e.g. Denlinger et al, 2012;Anderson and Segall, 2013;Madankan et al, 2014). For a model with a large number of inputs, the calculation of the posterior probability distribution can be computationally demanding.…”

Section: Analyses Of Sensitivity and Uncertaintymentioning

confidence: 99%

A global sensitivity analysis of the PlumeRise model of volcanic plumes

Woodhouse

Hogg

Phillips

2016

Journal of Volcanology and Geothermal Research

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Integral models of volcanic plumes allow predictions of plume dynamics to be made and the rapid estimation of volcanic source conditions from observations of the plume height by model inversion. Here we introduce PlumeRise, an integral model of volcanic plumes that incorporates a description of the state of the atmosphere, includes the effects of wind and the phase change of water, and has been developed as a freely available web-based tool. The model can be used to estimate the height of a volcanic plume when the source conditions are specified, or to infer the strength of the source from an observed plume height through a model inversion. The predictions of the volcanic plume dynamics produced by the model are analysed in four case studies in which the atmospheric conditions and the strength of the source are varied. A global sensitivity analysis of the model to a selection of model inputs is performed and the results are analysed using parallel coordinate plots for visualisation and variance-based sensitivity indices to quantify the sensitivity of model outputs. We find that if the atmospheric conditions do not vary widely then there is a small set of model inputs that strongly influence the model predictions. When estimating the height of the plume, the source mass flux has a controlling influence on the model prediction, while variations in the plume height strongly effect the inferred value of the source mass flux when performing inversion studies. The values taken for the entrainment coefficients have a particularly important effect on the quantitative predictions. The dependencies of the model outputs to variations in the inputs are discussed and compared to simple algebraic expressions that relate source conditions to the height of the plume.

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Bayesian inversion of data from effusive volcanic eruptions using physics‐based models: Application to Mount St. Helens 2004–2008

Cited by 105 publications

References 74 publications

Reconstructing volcanic plume evolution integrating satellite and ground-based data: Application to the 23rd November 2013 Etna eruption

Reconstructing volcanic plume evolution integrating satellite and ground-based data: Application to the 23rd November 2013 Etna eruption

Rational volcanic hazard forecasts and the use of volcanic alert levels

A global sensitivity analysis of the PlumeRise model of volcanic plumes

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