Spectral Simulation of Gaussian Vector Random Fields on the Sphere
Author
dc.contributor.author
Alegría, Alfredo
Author
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Emery, Xavier Mathieu
Author
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Freulon, Xavier
Author
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Lantuéjoul, Christian
Author
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Porcu, Emilio
Author
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Renard, Didier
Admission date
dc.date.accessioned
2024-03-12T18:44:45Z
Available date
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2024-03-12T18:44:45Z
Publication date
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2023
Cita de ítem
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En: Avalos Sotomayor, S.A., Ortiz, J.M., Srivastava, R.M. (eds) Geostatistics Toronto 2021. Cham, Switzerland: Springer, 2023. pp 51–59. ISBN 978-3-031-19845-8
es_ES
Identifier
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10.1007/978-3-031-19845-8_5
Identifier
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https://repositorio.uchile.cl/handle/2250/197392
Abstract
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Isotropic Gaussian random fields on the sphere are used in astronomy, geophysics, oceanography, climatology and remote sensing applications. However, to date, there is a lack of simulation algorithms that reproduce the spatial covariance structure without any approximation and, at the same time, are parsimonious in terms of computation time and memory storage requirements. This work presents two such algorithms that rely on the spectral representation of isotropic covariances on the sphere. Both algorithms are illustrated with synthetic examples.
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Lenguage
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en
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Publisher
dc.publisher
Springer
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Serie
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Springer Proceedings in Earth and Environmental Sciences;
Type of license
dc.rights
Attribution-NonCommercial-NoDerivs 3.0 United States