Detecting and approximating fault lines from randomly scattered data

DOI10.1007/s11075-004-3624-yzbMath1068.65032OpenAlexW2070870862MaRDI QIDQ1776173

John C. Mason, Andrew Crampton

Publication date: 20 May 2005

Published in: Numerical Algorithms (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11075-004-3624-y

zbMATH Keywords

numerical examplessupport vector machinesperformancesdata triangulationsdetection and approximation algorithmsdiscrete Gaussian curvaturediscretely defined surfacesfault lines

Mathematics Subject Classification ID

Numerical aspects of computer graphics, image analysis, and computational geometry (65D18) Complexity and performance of numerical algorithms (65Y20)

Related Items (6)

A regularization-correction approach for adapting subdivision schemes to the presence of discontinuities ⋮ Edge detection methods based on RBF interpolation ⋮ CS-RBF interpolation of surfaces with vertical faults from scattered data ⋮ On the reconstruction of discontinuous functions using multiquadric RBF-WENO local interpolation techniques ⋮ On a new characterization of finite jump discontinuities and its application to vertical fault detection ⋮ Fault and gradient fault detection and reconstruction from scattered data

Uses Software

• LOQO
• Algorithm 478

Cites Work

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• An interior-point algorithm for nonconvex nonlinear programming
• Approximating scattered data with discontinuities
• Detection of discontinuities in scattered data approximation
• A result about scale transformation families in approximation: application to surface fitting from rapidly varying data
• Vertical fault detection from scattered data
• LOQO:an interior point code for quadratic programming

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