Crampton, Andrew and Mason, John C. (2005) Detecting and approximating fault lines from randomly scattered data. Journal of Numerical Algorithms, 39 (1-3). pp. 115-130. ISSN 1017-1398
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Discretely defined surfaces that exhibit vertical displacements across unknown fault lines can be difficult to approximate accurately unless a representation of the faults is known. Accurate representations of these faults enable the construction of constrained approximation models that can successfully overcome common problems such as over-smoothing.
In this paper we review an existing method for detecting fault lines and present a new detection approach based on data triangulations and discrete Gaussian curvature (DGC). Furthermore, we show that if the fault line can be described non-parametrically, then accurate support vector machine (SVM) models can be constructed that are independent of the type of triangulation used in the detection algorithms. We shall also see that SVM models are particularly effective when the data produced by the detection algorithms are noisy. We compare the performances of the various new and established models.
|Additional Information:||UoA 23 (Computer Science and Informatics) © Springer 2005|
|Subjects:||Q Science > Q Science (General)|
Q Science > QE Geology
Q Science > QA Mathematics
|Schools:||School of Computing and Engineering|
School of Computing and Engineering > Automotive Engineering Research Group
School of Computing and Engineering > Informatics Research Group
School of Computing and Engineering > Informatics Research Group > Knowledge Engineering and Intelligent Interfaces
School of Computing and Engineering > Informatics Research Group > Software Engineering Research Group
School of Computing and Engineering > Informatics Research Group > XML, Database and Information Retrieval Research Group
 G. Allasia, R. Besenghi and A. De Rossi, A scattered data approximation scheme for the detection
|Depositing User:||Sara Taylor|
|Date Deposited:||18 Jun 2007|
|Last Modified:||10 Dec 2010 13:26|
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