Improving pore type identification from thin section images using an integrated fuzzy fusion of multiple classifiers
| Autor: | Hossein Memarian, Javad Ghiasi-Freez, Amir Mollajan |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
business.industry
0208 environmental biotechnology Energy Engineering and Power Technology Pattern recognition 02 engineering and technology Image segmentation Function (mathematics) 010502 geochemistry & geophysics Geotechnical Engineering and Engineering Geology 01 natural sciences Fuzzy logic 020801 environmental engineering Support vector machine Fuel Technology Sugeno integral Polynomial kernel Point (geometry) Artificial intelligence business Cluster analysis 0105 earth and related environmental sciences Mathematics |
| Zdroj: | Journal of Natural Gas Science and Engineering. 31:396-404 |
| ISSN: | 1875-5100 |
| Popis: | A semi-automatic algorithm for identification of five pore types in carbonate rocks of a gas field, including inter-particle, intra-particle, oomoldic, biomoldic, and vuggy, from thin section images is presented. The proposed algorithm involves four main steps. Firstly, a color-based image segmentation procedure is carried out using K-means clustering algorithm to separate regions corresponding to the considered pores. Secondly, six geometrical shape parameters of 384 pores are extracted from each segmented region to obtain the distinctive features for each pore type. Three classifiers (i.e. kNN, RBF, and SVM) are considered for classification to identify the type and percentage of interested porosities. Experimental results show that SVM with polynomial kernel function yields the highest accuracy and can effectively identify the pores with average accuracy of 94.4% whereas the kNN gives the lowest accuracy. The final step include combination of outputs of three employed classifiers that are trained separately on same dataset to recognize each pore space using Fuzzy Sugeno Integral (FSI) method. As expected, the fuzzy fusion of single classifiers improves the results of classification up to 9.4%, which is a considerable improvement in the classification from the stand point of petroleum geology. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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