Evolution of neural networks for the prediction of hydraulic conductivity as a function of borehole geophysical logs: Shobasama site, Japan.

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This report describes the methodology and results of a project to develop a neural network for the prediction of the measured hydraulic conductivity or transmissivity in a series of boreholes at the Tono, Japan study site. Geophysical measurements were used as the input to EL feed-forward neural network. A simple genetic algorithm was used to evolve the architecture and parameters of the neural network in conjunction with an optimal subset of geophysical measurements for the prediction of hydraulic conductivity. The first attempt was focused on the estimation of the class of the hydraulic conductivity, high, medium or low, from the ... continued below

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54 p.

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Reeves, Paul C. & McKenna, Sean Andrew June 1, 2004.

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Description

This report describes the methodology and results of a project to develop a neural network for the prediction of the measured hydraulic conductivity or transmissivity in a series of boreholes at the Tono, Japan study site. Geophysical measurements were used as the input to EL feed-forward neural network. A simple genetic algorithm was used to evolve the architecture and parameters of the neural network in conjunction with an optimal subset of geophysical measurements for the prediction of hydraulic conductivity. The first attempt was focused on the estimation of the class of the hydraulic conductivity, high, medium or low, from the geophysical logs. This estimation was done while using the genetic algorithm to simultaneously determine which geophysical logs were the most important and optimizing the architecture of the neural network. Initial results showed that certain geophysical logs provided more information than others- most notably the 'short-normal', micro-resistivity, porosity and sonic logs provided the most information on hydraulic conductivity. The neural network produced excellent training results with accuracy of 90 percent or greater, but was unable to produce accurate predictions of the hydraulic conductivity class. The second attempt at prediction was done using a new methodology and a modified data set. The new methodology builds on the results of the first attempts at prediction by limiting the choices of geophysical logs to only those that provide significant information. Additionally, this second attempt uses a modified data set and predicts transmissivity instead of hydraulic conductivity. Results of these simulations indicate that the most informative geophysical measurements for the prediction of transmissivity are depth and sonic log. The long normal resistivity and self potential borehole logs are moderately informative. In addition, it was found that porosity and crack counts (clear, open, or hairline) do not inform predictions of hydraulic transmissivity.

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54 p.

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  • Report No.: SAND2003-2950
  • Grant Number: AC04-94AL85000
  • DOI: 10.2172/918374 | External Link
  • Office of Scientific & Technical Information Report Number: 918374
  • Archival Resource Key: ark:/67531/metadc884300

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Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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  • June 1, 2004

Added to The UNT Digital Library

  • Sept. 22, 2016, 2:13 a.m.

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  • Nov. 28, 2016, 1:48 p.m.

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Reeves, Paul C. & McKenna, Sean Andrew. Evolution of neural networks for the prediction of hydraulic conductivity as a function of borehole geophysical logs: Shobasama site, Japan., report, June 1, 2004; United States. (digital.library.unt.edu/ark:/67531/metadc884300/: accessed September 24, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.