logo Idiap Research Institute        
 [BibTeX] [Marc21]
Support Vector Machines for Classification and Mapping of Reservoir Data
Type of publication: Idiap-RR
Citation: AdvancedSVM:rr-01-04
Number: Idiap-RR-04-2001
Year: 2001
Institution: IDIAP
Abstract: The report deals with the novel application of Support Vector Machines (Support Vectore Classification and Support Vector Regression) for the analysis and modelling of reservoir data. 2 problems are considered: classification and mapping of porosity data. Results are compared with geostatistical models - indicator kriging and ordinary kriging. Variography is widely used to control the performance of the machines. Geostatistical explanations for the SVR hyperparameters are discussed. Obtained results demonstrate flexibility and efficiency of SVM application for the reservoir characterisation.
Userfields: ipdmembership={learning},
Projects Idiap
Authors Kanevski, Mikhail
Pozdnoukhov, Alexei
Canu, St├ęphane
Maignan, Michel
Wong, Patrick
Shibli, S.
Added by: [UNK]
Total mark: 0
  • kanevski-rr-01-04.pdf