Local spline refinement driven by fault jump estimates for scattered data approximation

Cesare Bracco, Carlotta Giannelli, Francesco Patrizi and Alessandra Sestini

Mathematics and Computers in Simulation (MATCOM), 2025, vol. 228, issue C, 103-123

Abstract: We present new fault jump estimates to guide local refinement in surface approximation schemes with adaptive spline constructions. The proposed approach is based on the idea that, since discontinuities in the data should naturally correspond to sharp variations in the reconstructed surface, the location and size of jumps detected in the input point cloud should drive the mesh refinement algorithm. To exploit the possibility of inserting local meshlines in one or the other coordinate direction, as suggested by the jump estimates, we propose a quasi-interpolation (QI) scheme based on locally refined B-splines (LR B-splines). Particular attention is devoted to the construction of the local operator of the LR B-spline QI scheme, which properly adapts the spline approximation according to the nature and density of the scattered data configuration. A selection of numerical examples outlines the performance of the method on synthetic and real datasets characterized by different geographical features.

Keywords: Fault detection; Gradient fault detection; Fault jump estimate; Scattered data; Adaptive surface reconstruction; LR B-splines; Quasi-interpolation (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:228:y:2025:i:c:p:103-123

DOI: 10.1016/j.matcom.2024.08.031

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