Linear inverse problem for inferring eruption source parameters from sparse ash deposit data as viewed from an atmospheric dispersion modeling perspective

Moiseenko, K. B.; Malik, Nataliya

doi:10.1007/s00445-019-1281-1

Cited by 6 publications

(5 citation statements)

References 69 publications

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“…Numerical models of tephra dispersal and sedimentation attempt to address potential bias using the advection-diffusion equation to estimate ESPs, by matching observed deposit features with numerical model output [18][19][20][21][22] . Using inversion techniques, deposit data (i.e., mass per unit area, thickness, local grain-size distribution) are used to estimate the erupted mass, plume height, and total grain-size distribution 5,18,19,[23][24][25][26] . One advantage of these models is that they can better estimate ESPs with uncertainty quantification 25,27 .…”

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confidence: 99%

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The radius of the umbrella cloud helps characterize large explosive volcanic eruptions

Constantinescu

Hopulele-Gligor²,

Connor

et al. 2021

Commun Earth Environ

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Eruption source parameters (in particular erupted volume and column height) are used by volcanologists to inform volcanic hazard assessments and to classify explosive volcanic eruptions. Estimations of source parameters are associated with large uncertainties due to various factors, including complex tephra sedimentation patterns from gravitationally spreading umbrella clouds. We modify an advection-diffusion model to investigate this effect. Using this model, source parameters for the climactic phase of the 2450 BP eruption of Pululagua, Ecuador, are different with respect to previous estimates (erupted mass: 1.5–5 × 1011 kg, umbrella cloud radius: 10–14 km, plume height: 20–30 km). We suggest large explosive eruptions are better classified by volume and umbrella cloud radius instead of volume or column height alone. Volume and umbrella cloud radius can be successfully estimated from deposit data using one numerical model when direct observations (e.g., satellite images) are not available.

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confidence: 99%

“…Success in estimating ESPs is evaluated based on how well the model, statistical or numerical, fits the observed data 18,[23][24][25][26][27][28][29][30] . However, model assumptions, again either statistical or numerical, also may lead to biased estimates of ESPs.…”

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confidence: 99%

The radius of the umbrella cloud helps characterize large explosive volcanic eruptions

Constantinescu

Hopulele-Gligor²,

Connor

et al. 2021

Commun Earth Environ

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“…Input parameters were obtained from published data and when unavailable through the solution of the inverse problem, i.e., by best-fitting simulation results with field data to reconstruct the pumice fall deposit (cf. Macedonio et al, 2008;Scollo et al, 2008;Bonasia et al, 2010Bonasia et al, , 2011Moiseenko and Malik, 2019). Three tephra fallout scenarios were simulated to obtain the eruptive source parameters, wind conditions and produce volcanic hazard maps.…”

Section: Tephra Fallout Simulationsmentioning

confidence: 99%

Biased Volcanic Hazard Assessment Due to Incomplete Eruption Records on Ocean Islands: An Example of Sete Cidades Volcano, Azores

et al. 2019

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“…Previous workers have presented different methods to implement inversion to obtain ESPs from the characteristics of tephra deposits, such as deposit thickness and grain size. The simplex search algorithm, grid-search method, matrix inversion with Tikhonov regularization, and a regularized form of the Levenburg-Marquardt algorithm have been proposed (Connor and Connor 2006;Klawonn et al 2012;Johnston et al 2012;White et al 2017;Moiseenko and Malik 2019;Mannen et al 2020). The efficiency and ability to characterize uncertainty with various simplifications (such as those used to avoid solving ill-posed problems) are the main concerns in proposing these algorithms as alternatives to classical inversion.…”

Section: Introductionmentioning

confidence: 99%

Tephra deposit inversion by coupling Tephra2 with the Metropolis-Hastings algorithm: algorithm introduction and demonstration with synthetic datasets

et al. 2021

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In this work we couple the Metropolis-Hastings algorithm with the volcanic ash transport model Tephra2, and present the coupled algorithm as a new method to estimate the Eruption Source Parameters of volcanic eruptions based on mass per unit area or thickness measurements of tephra fall deposits. Outputs of the algorithm are presented as sample posterior distributions for variables of interest. Basic elements in the algorithm and how to implement it are introduced. Experiments are done with synthetic datasets. These experiments are designed to demonstrate that the algorithm works from different perspectives, and to show how inputs affect its performance. Advantages of the algorithm are that it has the ability to i) incorporate prior knowledge; ii) quantify the uncertainty; iii) capture correlations between variables of interest in the estimated Eruption Source Parameters; and iv) no simplification is assumed in sampling from the posterior probability distribution. A limitation is that some of the inputs need to be specified subjectively, which is designed intentionally such that the full capacity of the Bayes’ rule can be explored by users. How and why inputs of the algorithm affect its performance and how to specify them properly are explained and listed. Correlation between variables of interest in the posterior distributions exists in many of our experiments. They can be well-explained by the physics of tephra transport. We point out that in tephra deposit inversion, caution is needed in attempting to estimate Eruption Source Parameters and wind direction and speed at each elevation level, because this could be unnecessary or would increase the number of variables to be estimated, and these variables could be highly correlated. The algorithm is applied to a mass per unit area dataset of the tephra deposit from the 2011 Kirishima-Shinmoedake eruption. Simulation results from Tephra2 using posterior means from the algorithm are consistent with field observations, suggesting that this approach reliably reconstructs Eruption Source Parameters and wind conditions from deposits.

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Linear inverse problem for inferring eruption source parameters from sparse ash deposit data as viewed from an atmospheric dispersion modeling perspective

Cited by 6 publications

References 69 publications

The radius of the umbrella cloud helps characterize large explosive volcanic eruptions

The radius of the umbrella cloud helps characterize large explosive volcanic eruptions

Biased Volcanic Hazard Assessment Due to Incomplete Eruption Records on Ocean Islands: An Example of Sete Cidades Volcano, Azores

Tephra deposit inversion by coupling Tephra2 with the Metropolis-Hastings algorithm: algorithm introduction and demonstration with synthetic datasets

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