Fuzzy classifier fusion: an application to reservoir facies identification

An application of classifier fusion technique is presented to improve the performance of automated reservoir facies identification system. The algorithm presented in this study uses three well-known classifiers, namely Bayesian, k -nearest neighbor (kNN), and support vector machine (SVM) to automati...

Full description

Saved in:
Bibliographic Details
Published in:Neural computing & applications 2018-08, Vol.30 (3), p.825-834
Main Authors: Mollajan, Amir, Memarian, Hossein, Nabi-Bidhendi, Majid
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Bayesian analysis
Classifiers
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Image Processing and Computer Vision
Original Article
Performance enhancement
Probability and Statistics in Computer Science
Support vector machines
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:An application of classifier fusion technique is presented to improve the performance of automated reservoir facies identification system. The algorithm presented in this study uses three well-known classifiers, namely Bayesian, k -nearest neighbor (kNN), and support vector machine (SVM) to automatically identify four defined facies of Asmari Formation from log-derived amplitude versus offset (AVO) attributes. Fuzzy Sugeno integral (FSI) method is then employed to combine the outputs of three investigated classifiers and increase the consistency of reservoir facies identification process. The experimental results obtained from applying the presented algorithm on data related to three wells drilled in Asmari Formation provide evidence of the effectiveness of the proposed algorithm regarding true positive (TP), false positive (FP), and classification accuracy criteria.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-016-2690-0