A Nonparametric Approach for Assessing Precision in Georeferenced Point Clouds Best Fit Planes: Toward More Reliable Thresholds
The fitting of a plane to data points is essential to the geosciences. However, it is recognized that the reliability of these best fit planes depends upon the point set distribution and geometry, evaluated in terms of the eigen-based parameters derived from the moment of inertia analysis. Despite i...
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Blackwell Publishing Ltd
2018
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| 100 | 1 | |a Gallo, L.C. | |
| 245 | 1 | 2 | |a A Nonparametric Approach for Assessing Precision in Georeferenced Point Clouds Best Fit Planes: Toward More Reliable Thresholds |
| 260 | |b Blackwell Publishing Ltd |c 2018 | ||
| 270 | 1 | 0 | |m Gallo, L.C.; Instituto de Geociencias Básicas, Aplicadas y Ambientales de Buenos Aires, Departamento de Ciencias Geológicas, Facultad de Ciencias Exactas y Naturales, Ciudad UniversitariaArgentina; email: len.gallo@gmail.com |
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| 506 | |2 openaire |e Política editorial | ||
| 520 | 3 | |a The fitting of a plane to data points is essential to the geosciences. However, it is recognized that the reliability of these best fit planes depends upon the point set distribution and geometry, evaluated in terms of the eigen-based parameters derived from the moment of inertia analysis. Despite its significance, few studies have addressed the uncertainties of the analysis, which can adversely affect the reproduction of results one of the cornerstones of scientific endeavor. Aiming to contribute toward the neglected issue of the moment of inertia precision, we have developed a bootstrap resampling scheme to empirically discover the distribution of uncertainties in the orientation of best fit planes. Dispersion of the bootstrapped normal vectors to the best fit plane is regarded as a measure of precision, evaluated with the maximum angular distance from the optimal solution. This rationale was tested using Monte Carlo-generated samples covering a comprehensive range of shape parameters to assess the dependence between eigen parameters and their inherent bias. Our results show that the oblateness of the point cloud is a robust parameter to assess the reliability of the best fit plane. Given this, the method was then applied to a publicly available lidar data set. We argue that georeferenced point clouds with an oblateness parameter greater than 3 and 1.5 may be placed at 95% confidence levels of 5° and 10°, respectively. We propose using these values as thresholds to obtain robust best fit planes, guaranteeing reproducible results for scientific research. ©2018. American Geophysical Union. All Rights Reserved. |l eng | |
| 536 | |a Detalles de la financiación: Universidad de Buenos Aires | ||
| 536 | |a Detalles de la financiación: Consejo Nacional de Investigaciones Científicas y Técnicas | ||
| 536 | |a Detalles de la financiación: We thank the Universidad de Buenos Aires (UBA) and CONICET for the funding that enabled the conduction of these studies and L.C.G. Doctoral Grant. E. O. C. acknowledges founding from PICT 2013-1309 and BACYT 20020130100613BA. T. D. Seers provided an insightful and helpful review; we are very grateful. We also thank the Editor, Paul Tregoning; the Associate Editor, Andy Hooper; and an anonymous reviewer. The authors would like to thank Renata N. Tomezzoli for constructive discussions. The data used are available in tables, cited references, and supporting information. | ||
| 593 | |a Instituto de Geociencias Básicas, Aplicadas y Ambientales de Buenos Aires, Departamento de Ciencias Geológicas, Facultad de Ciencias Exactas y Naturales, Ciudad Universitaria, Buenos Aires, Argentina | ||
| 593 | |a Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina | ||
| 593 | |a Laboratorio de Modelado Geológico, Instituto de Estudios Andinos, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina | ||
| 593 | |a Laboratorio de Termocronología (La.Te Andes), Consejo Nacional de Investigaciones Científicas y Técnicas, Salta, Argentina | ||
| 593 | |a Departamento de Matemática y Ciencias, Universidad de San AndrésVIC, Argentina | ||
| 650 | 1 | 7 | |2 spines |a PRECISION |
| 690 | 1 | 0 | |a BEST FIT PLANE |
| 690 | 1 | 0 | |a BOOTSTRAP STATISTICS |
| 690 | 1 | 0 | |a MOMENT OF INERTIA ANALYSIS |
| 690 | 1 | 0 | |a MONTE CARLO SIMULATION |
| 690 | 1 | 0 | |a ORIENTATION OF STRUCTURAL HETEROGENEITIES |
| 690 | 1 | 0 | |a BOOTSTRAPPING |
| 690 | 1 | 0 | |a INERTIA |
| 690 | 1 | 0 | |a MONTE CARLO ANALYSIS |
| 690 | 1 | 0 | |a SIMULATION |
| 690 | 1 | 0 | |a THRESHOLD |
| 700 | 1 | |a Cristallini, Ernesto Osvaldo | |
| 700 | 1 | |a Svarc, M. | |
| 773 | 0 | |d Blackwell Publishing Ltd, 2018 |g v. 123 |h pp. 10,297-10,308 |k n. 11 |p J. Geophys. Res. Solid Earth |x 21699313 |w (AR-BaUEN)CENRE-151 |t Journal of Geophysical Research: Solid Earth | |
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| 856 | 4 | 0 | |u https://doi.org/10.1029/2018JB016319 |y DOI |
| 856 | 4 | 0 | |u https://hdl.handle.net/20.500.12110/paper_21699313_v123_n11_p10,297_Gallo |y Handle |
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