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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 15, issue 6
Nonlin. Processes Geophys., 15, 863–871, 2008
https://doi.org/10.5194/npg-15-863-2008
© Author(s) 2008. This work is distributed under
the Creative Commons Attribution 3.0 License.
Nonlin. Processes Geophys., 15, 863–871, 2008
https://doi.org/10.5194/npg-15-863-2008
© Author(s) 2008. This work is distributed under
the Creative Commons Attribution 3.0 License.

  21 Nov 2008

21 Nov 2008

Gas chimney detection based on improving the performance of combined multilayer perceptron and support vector classifier

H. Hashemi1,2, D. M. J. Tax2, R. P. W. Duin2, A. Javaherian1, and P. de Groot3 H. Hashemi et al.
  • 1Institute of Geophysics, University of Tehran, P.O. Box 14155-6466, Tehran, I. R. Iran
  • 2ICT, Faculty of EEMCS, Delft University of Technology, P.O. Box 5031, 2600 GA, Delft, The Netherlands
  • 3dGB Earth Sciences BV, Nijverheidstraat 11-2, 7511 JM, Enschede, The Netherlands

Abstract. Seismic object detection is a relatively new field in which 3-D bodies are visualized and spatial relationships between objects of different origins are studied in order to extract geologic information. In this paper, we propose a method for finding an optimal classifier with the help of a statistical feature ranking technique and combining different classifiers. The method, which has general applicability, is demonstrated here on a gas chimney detection problem. First, we evaluate a set of input seismic attributes extracted at locations labeled by a human expert using regularized discriminant analysis (RDA). In order to find the RDA score for each seismic attribute, forward and backward search strategies are used. Subsequently, two non-linear classifiers: multilayer perceptron (MLP) and support vector classifier (SVC) are run on the ranked seismic attributes. Finally, to capitalize on the intrinsic differences between both classifiers, the MLP and SVC results are combined using logical rules of maximum, minimum and mean. The proposed method optimizes the ranked feature space size and yields the lowest classification error in the final combined result. We will show that the logical minimum reveals gas chimneys that exhibit both the softness of MLP and the resolution of SVC classifiers.

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