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A method for local approximation of a planar deformation field

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
We present a method of approximation of a deformation eld based on the local ane transformations constructed based on n nearest neighbors with respect to points of adopted grid. The local ane transformations are weighted by means of inverse distance squared between each grid point and observed points (nearest neighbors). This work uses a deformation gradient, although it is possible to use a displacement gradient instead – the two approaches are equivalent. To decompose the deformation gradient into components related to rigid motions (rotations, translations are excluded from the deformation gradient through dierentiation process) and deformations, we used a polar decomposition and decomposition into a sum of symmetric and an anti-symmetric matrices (tensors). We discuss the results from both decompositions. Calibration of a local ane transformations model (i.e., number of nearest neighbors) is performed on observed points and is carried out in a cross-validation procedure. Verication of the method was conducted on simulated data-grids subjected to known (functionally generated) deformations, hence, known in every point of a study area.
Rocznik
Tom
Strony
1--8
Opis fizyczny
Bibliogr. 15 poz., rys., tab.
Twórcy
autor
  • Department of Integrated Geodesy and Cartography, Faculty of Mining Surveying and Environmental Engineering, AGH University of Science and Technology, 30 A. Mickiewicza Avenue, 30-059, Cracow, Poland
autor
  • Institute of Technical Engineering, Bronisław Markiewicz State Higher School of Technology and Economics in Jarosław, 16 Czarnieckiego Street, 37-500, Jarosław, Poland
  • Department of Mine Areas Protection, Geoinformatics and Mine Surveying, Faculty of Mining Surveying and Environmental Engineering, AGH University of Science and Technology, 30 A. Mickiewicza Avenue, 30-059, Cracow, Poland
Bibliografia
  • 1. Altiner, Y. (2013). Analytical surface deformation theory: for detection of the Earth’s crust movements. Springer, Berlin, Heidelberg, doi:10.1007/978-3-662-03935-9.
  • 2. Bayly, P. V., Cohen, T., Leister, E., Ajo, D., Leuthardt, E., and Genin, G. (2005). Deformation of the human brain induced by mild acceleration. Journal of Neurotrauma, 22(8):845–856, doi:10.1089/neu.2005.22.845.
  • 3. Berber, M., Kutoglu, H., Dare, P., and Vanícek, P. (2012). Combining surface deformation parameters referred to different terrestrial coordinate systems. Survey Review, 44(324):23–29, doi:10.1179/1752270611Y.0000000005.
  • 4. Caspary, W., Haen, W., and Borutta, H. (1990). Deformation analysis by statistical methods. Technometrics, 32(1):49–57.
  • 5. Chaves, E. W. V. (2013). Notes on continuum mechanics. Springer, Dordrecht, doi:10.1007/978-94-007-5986-2.
  • 6. Dermanis, A. and Kotsakis, C. (2006). Estimating crustal deformation parameters from geodetic data: Review of existing methodologies, open problems and new challenges. In Sansò, F. and Gil, A. J., editors, Geodetic deformation monitoring: from geophysical to engineering roles, pages 7–18. Springer, Heidelberg, doi:10.1007/978-3-540-38596-7_2.
  • 7. Dermanis, A. and Livieratos, E. (1983). Applications of deformation analysis in geodesy and geodynamics. Reviews of Geophysics, 21(1):41–50, doi:10.1029/RG021i001p00041.
  • 8. Gander, W. (1990). Algorithms for the polar decomposition. SIAM Journal on Scienti_c and Statistical Computing, 11(6):1102–1115, doi:10.1137/0911062.
  • 9. Goudarzi, M. A., Cocard, M., and Santerre, R. (2015). Geostrain: An open source software for calculating crustal strain rates. Computers & Geosciences, 82:1–12, doi:10.1016/j.cageo.2015.05.007.
  • 10. Higham, N. J. (1986). Computing the polar decomposition - with applications. SIAM Journal on Scienti_c and Statistical Computing, 7(4):1160–1174, doi:10.1137/0907079.
  • 11. Markley, F. L. and Mortari, D. (1999). How to estimate attitude from vector observations. AIAA/AAS Paper, pages 99–427.
  • 12. Osada, E. and Sergieieva, K. (2010). O badaniu zniekształcen modeli transformacji map na podstawie elipsy Tissota – długosci, pola lub katy. Magazyn geoinformacyjny Geodeta, (1):176.
  • 13. Shoemake, K. and Du_, T. (1992). Matrix animation and polar decomposition. In Proceedings of the conference on Graphics interface, volume 92, pages 258–264.
  • 14. Szafarczyk, A. and Gawalkiewicz, R. (2016). Case study of the tensor analysis of ground deformations evaluated from geodetic measurements in a landslide area. Acta Geodynamica et Geomaterialia, 13(2):213–222, doi:10.13168/AGG.2015.0003.
  • 15. Tanaka, M., Wada, S., and Nakamura, M. (2012). Computational biomechanics: theoretical background and biological/biomedical problems, volume 3. Springer, Tokyo, doi:10.1007/978-4-431-54073-1.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b771b13b-c84b-4f95-bedb-a9500a2f51db
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