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Titre3D modelling of geological surfaces using generalized interpolation with radial basis functions
AuteurHillier, M J; Schetselaar, E M; de Kemp, E A; Perron, G
SourceEarth Modelling 2013 meeting, presentations; 2013 p. 1-18
Séries alt.Secteur des sciences de la Terre, Contribution externe 20140419
RéunionEarth Modelling 2013 meeting; Vancouver; CA; Octobre 21-24, 2013
Mediapapier; en ligne; numérique
Sujetsétablissement de modèles; modèles; géomathématique
Illustrations3-D models
ProgrammeÉtude des gîtes de SEDEX, Initiative géoscientifique ciblée (IGC-4)
ProgrammeDeveloppements methodologie, Initiative géoscientifique ciblée (IGC-4)
LiensPresentation - présentation
Résumé(disponible en anglais seulement)
A generalized interpolation framework using radial basis functions (RBF) is presented that implicitly models three-dimensional continuous geological surfaces from scattered multivariate structural data. Generalized interpolants can use multiple types of independent geological constraints by deriving for each, linearly independent functionals. This framework does not suffer from the limitations of previous RBF approaches developed for geological surface modelling that requires additional offset points to ensure uniqueness of the interpolant. A particularly useful application of generalized interpolants is that they allow augmenting on-contact constraints with gradient constraints as defined by strike-dip data with assigned polarity. This interpolation problem yields a linear system that is analogous in form to the previously developed potential field implicit interpolation method based on co-kriging of contact increments using parametric isotropic covariance functions. The general form of the mathematical framework presented herein allows us to further expand on solutions by: (1) including stratigraphic data from above and below the target surface as inequality constraints (2) modelling anisotropy by data-driven eigen analysis of gradient constraints and (3) incorporating additional constraints by adding linear functionals to the system, such as fold axis constraints. Case studies are presented that demonstrate the advantages and general performance of the surface modelling method in sparse data environments where the contacts that constrain geological surfaces are rarely exposed but structural and off-contact stratigraphic data can be plentiful.