Support vector machine for 3D modelling from sparse geological information of various origins

Computers and Geosciences

Volumn 34, Issue 2, 2008, Pages 127-143

  Smirnoff, Alex ^a   Boisvert, Eric ^a   Paradis, Serge J ^a  

^a NATURAL RESOURCES CANADA   (Canada)

Author keywords

3D modelling; Geological reconstruction; Gocad; Support vector machine; SVM

Indexed keywords

DATA PROCESSING; GEOGRAPHIC INFORMATION SYSTEMS; INFORMATION ANALYSIS; SUPPORT VECTOR MACHINES; THREE DIMENSIONAL;

GEOLOGICAL INFORMATION; GEOLOGICAL RECONSTRUCTION;

GEOLOGIC MODELS;

DATA PROCESSING; GEOGRAPHIC INFORMATION SYSTEMS; GEOLOGIC MODELS; INFORMATION ANALYSIS; SUPPORT VECTOR MACHINES; THREE DIMENSIONAL;

GEOLOGICAL MAPPING; THREE-DIMENSIONAL MODELING; VISUALIZATION;

EID: 36148931874     PISSN: 00983004     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cageo.2006.12.008     Document Type: Article

References (20)

1. Abe S. Support Vector Machines for Pattern Classification (2005), Springer, London 343pp
2. Bolduc A.M., Paradis S.J., Riverin M.-N., Lefebvre R., and Michaud Y. A 3D esker geomodel for groundwater research: the case of the Saint-Mathieu-Berry esker, Abitibi, Quebec, Canada. In: Russell H., Berg R.C., and Thorleifson L.H. (Eds). Three-Dimensional Geological Mapping for Groundwater Applications (2005), Geological Survey of Canada, Ottawa, Ont. 17-20 (Open File 5048)
3. Chang, C.-C., Lin C.-J., 2001. LIBSVM: a Library for Support Vector Machines. 〈http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf〉.
4. Chiles, J.P., Aug, C., Guillen, A., Lees, T., 2004. Modelling the geometry of geological units and its uncertainty in 3D from structural data: the potential-field method. In: Dimitrakopoulos, R., Ramazan, S. (Eds.), Proceedings of Orebody Modelling and Strategic Mine Planning, Perth, WA, pp. 313-320.
5. Courrioux G., Nullans S., Guillen A., Boissonnat J.D., Repusseau P., Renaud X., and Thibaut M. 3D volumetric modelling of Cadomian terranes (Northern Brittany, France): an automatic method using Voronoi diagrams. Tectonophysics 331 (2001) 181-196
6. Cristianini N., and Shawe-Taylor J. Support Vector Machines (2000), Cambridge University Press, Cambridge 189pp
7. El-Naqa I., Yang Y., Wernik M.N., Galatsanos N.P., and Nishikawa R. Support vector machine learning for the detection of microcalcifications in mammograms. IEEE Transactions on Medical Imaging 21 (2002) 1552-1563
8. Gilardi, N., Kanevski, M., Maignan, M., Mayoraz, E., 1999. Environmental and pollution spatial data classification with support vector machines and geostatistics. In: Proceedings of Workshop W07 "Intellegent techniques for Spatio-Temporal Data Analysis in Environmental Applications", ACAI99, July, Greece, pp. 43-51.
9. Girard F. Architecture et hydrostratigraphie d'un complexe morainique et deltaïque dans la région de Saint-Raymond de Portneuf, Québec (Architecture and Hydrostratigraphy of a Morainic and Deltaic system in the Saint-Raymond de Portneuf Region, Québec) (2001), Mémoire de maîtrise, Université du Québec, Québec, Canada 185pp
10. Hsu C.-W., and Lin C.-J. A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks 13 2 (2002) 415-425
11. Hsu, C.-W., Chang, C.-C., Lin C.-J., 2004. A Practical Guide to Support Vector Classification. 〈http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf〉.
12. Lajaunie C., Courrioux G., and Manuel L. Foliation fields and 3D cartography in geology: principles of a method based on potential interpolation. Mathematical Geology 29 (1997) 571-584
13. Mallet J.-L. Discrete smooth interpolation. ACM Transactions on Graphics 8 2 (1989) 121-144
14. Noble W.S., Kuehn S., Thurman R., Yu M., and Stamatoyannopoulos J. Predicting the in vivo signature of human gene regulatory sequences. Bioinformatics 21 1 (2005) 338-343
15. Sharifzadeh M., Shahabi C., and Knoblock C.A. Learning approximate thematic maps from labeled geospatial data. In: Agouris P., and Croitoru A. (Eds). Next Generation Geospatial Information (2005), Taylor & Francis Group, London 129-141
16. Treece, G.M., Prager, R.W., Gee, A.H., Berman, L., 1998. Fast surface and volume estimation from non-parallel cross-sections, for freehand 3-D ultrasound. Technical Report CUED/F-INFENG/TR 326, Cambridge University, Cambridge, England, 68pp.
17. Treece, G.M., Prager, R.W., Gee, A.H., Berman, L., 1999. Surface interpolation from sparse cross-sections using region correspondence. Technical Report CUED/F-INFENG/TR 342, Cambridge University, Cambridge, England, 24pp.
18. Treece, G.M., Prager, R.W., Gee, A.H., 2000. Volume-based three-dimentional metamorphosis using region correspondence. Technical Report CUED/F-INFENG/TR 379, Cambridge University, Cambridge, England, 24pp.
19. Vapnik V. The Nature of Statistical Learning Theory (1995), Springer, New York 311pp
20. Yu X., Liong. S.-Y., and Babovic V. EC-SVM approach for real-time hydrologic forecasting. Journal of Hydroinformatics 06 3 (2004) 209-223