2019
DOI: 10.1007/s00445-019-1281-1
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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

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Cited by 6 publications
(5 citation statements)
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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 .…”
mentioning
confidence: 99%
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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 .…”
mentioning
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.…”
mentioning
confidence: 99%
“…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%
“…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%