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Neural Computing and Applications

, Volume 20, Issue 7, pp 955–963 | Cite as

FMI image based rock structure classification using classifier combination

  • Xu-Cheng Yin
  • Qian Liu
  • Hong-Wei HaoEmail author
  • Zhi-Bin Wang
  • Kaizhu Huang
ICONIP2009

Abstract

Formation Micro Imager (FMI) can directly reflect changes of wall stratums and rock structures, and is an important factor to classify stratums and identify lithology for the oil and gas exploration. Conventionally, people analyze FMI images mainly with manual processing, which is, however, extremely inefficient and incurs a heavy workload for experts. In this paper, we propose an automatic rock structure classification system using image processing and pattern recognition technologies. We investigate the characteristics of rock structures in FMI images carefully. We also develop an effective classification framework with classifier combination that can integrate the domain knowledge from experienced geologists successfully. Our classification system includes three main steps. First, various effective features, specially designed for FMI images, are calculated and selected. Then, the corresponding single classifier associated with each feature is constructed. Finally, all these classifiers are combined as an effective cascade recognition system. We test our rock structure classification system with real FMI rock images. In experiments, with only one training sample per class, the average recognition accuracy of our proposed system is 81.11%. The accuracy is 15.55 percent higher than the traditional 1-nearest neighborhood method. Moreover, this automatic system can significantly reduce the complexity and difficulty in the rock structure analysis task for the oil and gas exploration.

Keywords

FMI Rock structure classification Feature extraction Classifier combination Oil and gas exploration 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China under Grant No. 60675006 and thanks Fei Wu for his contributions to this paper. We also are thankful to T. Chow and anonymous reviewers for their valuable comments and suggestions.

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Copyright information

© Springer-Verlag London Limited 2010

Authors and Affiliations

  • Xu-Cheng Yin
    • 1
  • Qian Liu
    • 1
  • Hong-Wei Hao
    • 1
    Email author
  • Zhi-Bin Wang
    • 1
  • Kaizhu Huang
    • 2
  1. 1.Department of Computer Science, School of Information EngineeringUniversity of Science and Technology BeijingBeijingChina
  2. 2.Institute of AutomationChinese Academy of ScienceBeijingChina

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