Polynomial Chaos Quadrature-based minimum variance approach for source parameters estimation
We present a polynomial chaos based minimum variance formulation to solve inverse problems. The utility of the proposed approach is evaluated by considering the ash transport problem arising due to volcanic eruption. Volcanic ash advisory centers generally makes use of mathematical models for column...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.642.39 2023-05-15T16:50:42+02:00 Polynomial Chaos Quadrature-based minimum variance approach for source parameters estimation P. Webleye The Pennsylvania State University CiteSeerX Archives application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.642.39 http://www.dddas.org/iccs2012/papers/singla.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.642.39 http://www.dddas.org/iccs2012/papers/singla.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://www.dddas.org/iccs2012/papers/singla.pdf inverse problem source parameter estimation polynomial chaos minimum variance estimator text ftciteseerx 2016-01-08T16:01:18Z We present a polynomial chaos based minimum variance formulation to solve inverse problems. The utility of the proposed approach is evaluated by considering the ash transport problem arising due to volcanic eruption. Volcanic ash advisory centers generally makes use of mathematical models for column eruption and advection and diffusion of ash cloud in atmosphere. These models require input data on source conditions such as vent radius, vent velocity and distribution of ash-particle size. The inputs are usually not well constrained, and estimates of the uncertainty in the inputs is needed to make accurate predictions of cloud motion. The recent eruption of Eyjafjallajökull, Iceland in April 2010 is considered as test example. For validation, the puff advection and diffusion model is used to hindcast the motion of the ash cloud through time concentrating on the period 14-16 April 2010. Variability in the height and loading of the eruption is introduced through the volcano column model bent. Output uncertainty due to uncertain input parameters is determined with a polynomial chaos quadrature (PCQ)-based sampling of the multidimensional puff input vector space. Furthermore, the posterior distribution for input parameters is obtained by assimilating satellite imagery data with PCQ predictions using a minimum variance approach. Text Iceland Unknown |
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inverse problem source parameter estimation polynomial chaos minimum variance estimator |
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inverse problem source parameter estimation polynomial chaos minimum variance estimator P. Webleye Polynomial Chaos Quadrature-based minimum variance approach for source parameters estimation |
topic_facet |
inverse problem source parameter estimation polynomial chaos minimum variance estimator |
description |
We present a polynomial chaos based minimum variance formulation to solve inverse problems. The utility of the proposed approach is evaluated by considering the ash transport problem arising due to volcanic eruption. Volcanic ash advisory centers generally makes use of mathematical models for column eruption and advection and diffusion of ash cloud in atmosphere. These models require input data on source conditions such as vent radius, vent velocity and distribution of ash-particle size. The inputs are usually not well constrained, and estimates of the uncertainty in the inputs is needed to make accurate predictions of cloud motion. The recent eruption of Eyjafjallajökull, Iceland in April 2010 is considered as test example. For validation, the puff advection and diffusion model is used to hindcast the motion of the ash cloud through time concentrating on the period 14-16 April 2010. Variability in the height and loading of the eruption is introduced through the volcano column model bent. Output uncertainty due to uncertain input parameters is determined with a polynomial chaos quadrature (PCQ)-based sampling of the multidimensional puff input vector space. Furthermore, the posterior distribution for input parameters is obtained by assimilating satellite imagery data with PCQ predictions using a minimum variance approach. |
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The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
P. Webleye |
author_facet |
P. Webleye |
author_sort |
P. Webleye |
title |
Polynomial Chaos Quadrature-based minimum variance approach for source parameters estimation |
title_short |
Polynomial Chaos Quadrature-based minimum variance approach for source parameters estimation |
title_full |
Polynomial Chaos Quadrature-based minimum variance approach for source parameters estimation |
title_fullStr |
Polynomial Chaos Quadrature-based minimum variance approach for source parameters estimation |
title_full_unstemmed |
Polynomial Chaos Quadrature-based minimum variance approach for source parameters estimation |
title_sort |
polynomial chaos quadrature-based minimum variance approach for source parameters estimation |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.642.39 http://www.dddas.org/iccs2012/papers/singla.pdf |
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Iceland |
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Iceland |
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http://www.dddas.org/iccs2012/papers/singla.pdf |
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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.642.39 http://www.dddas.org/iccs2012/papers/singla.pdf |
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Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
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