FMI image based rock structure classification using classifier combination

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. © 2010 Springer-Verlag London Limited.

Duke Scholars

Author Kaizhu Huang DKU Faculty