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Authordc.contributor.authorArroyo, Daisy 
Authordc.contributor.authorEmery, Xavier 
Cita de ítemdc.identifier.citationStochastic Environmental Research and Risk Assessment, Volumen 32, Issue 11, 2018, Pages 3245-3255
Abstractdc.description.abstractIntrinsic random fields of order k, defined as random fields whose high-order increments (generalized increments of order k) are second-order stationary, are used in spatial statistics to model regionalized variables exhibiting spatial trends, a feature that is common in earth and environmental sciences applications. A continuous spectral algorithm is proposed to simulate such random fields in a d-dimensional Euclidean space, with given generalized covariance structure and with Gaussian generalized increments of order k. The only condition needed to run the algorithm is to know the spectral measure associated with the generalized covariance function (case of a scalar random field) or with the matrix of generalized direct and cross-covariances (case of a vector random field). The algorithm is applied to synthetic examples to simulate intrinsic random fields with power generalized direct and cross-covariances, as well as an intrinsic random field with power and spline generalized direct covariances and Mate´rn generalized cross-covariance.
Publisherdc.publisherSpringer New York LLC
Type of licensedc.rightsAttribution-NonCommercial-NoDerivs 3.0 Chile
Link to Licensedc.rights.uri
Sourcedc.sourceStochastic Environmental Research and Risk Assessment
Keywordsdc.subjectGeneralized direct and cross-covariances
Keywordsdc.subjectGeneralized increments of order k
Keywordsdc.subjectNon-stationary random fields
Keywordsdc.subjectSpectral density
Títulodc.titleSimulation of intrinsic random fields of order k with a continuous spectral algorithm
Document typedc.typeArtículo de revista
Indexationuchile.indexArtículo de publicación SCOPUS

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Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Chile