Neural network accuracy measures and data transforms applied to the seismic parameter estimation problem

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The accuracy of an artificial neural network (ANN) algorithm is a crucial issue in the estimation of an oil field reservoir`s properties from remotely sensed seismic data. This paper demonstrates the use of the k-fold cross validation technique to obtain confidence bounds on an ANN`s accuracy statistic from a finite sample set. In addition, we also show that an ANN`s classification accuracy is dramatically improved by transforming the ANN`s input feature space to a dimensionally smaller, new input space. The new input space represents a feature space that maximizes the linear separation between classes. Thus, the ANN`s convergence time and ... continued below

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8 p.

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Glover, C.W.; Barhen, J.; Aminzadeh, F. & Toomarian, N.B. January 1, 1997.

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The accuracy of an artificial neural network (ANN) algorithm is a crucial issue in the estimation of an oil field reservoir`s properties from remotely sensed seismic data. This paper demonstrates the use of the k-fold cross validation technique to obtain confidence bounds on an ANN`s accuracy statistic from a finite sample set. In addition, we also show that an ANN`s classification accuracy is dramatically improved by transforming the ANN`s input feature space to a dimensionally smaller, new input space. The new input space represents a feature space that maximizes the linear separation between classes. Thus, the ANN`s convergence time and accuracy are improved because the ANN must merely find nonlinear perturbations to the starting linear decision boundaries. These techniques for estimating ANN accuracy bounds and feature space transformations are demonstrated on the problem of estimating the sand thickness in an oil field reservoir based only on remotely sensed seismic data.

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8 p.

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OSTI as DE97001920

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  • 3. international conference on neural networks and their applications, Marseilles (France), 12-14 Mar 1997

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  • Other: DE97001920
  • Report No.: CONF-970348--2
  • Grant Number: AC05-96OR22464
  • Office of Scientific & Technical Information Report Number: 461420
  • Archival Resource Key: ark:/67531/metadc676245

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Office of Scientific & Technical Information Technical Reports

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  • January 1, 1997

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  • July 25, 2015, 2:21 a.m.

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  • Jan. 22, 2016, 12:23 p.m.

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Glover, C.W.; Barhen, J.; Aminzadeh, F. & Toomarian, N.B. Neural network accuracy measures and data transforms applied to the seismic parameter estimation problem, article, January 1, 1997; Tennessee. (digital.library.unt.edu/ark:/67531/metadc676245/: accessed December 18, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.