Optimization and data mining for fracture prediction in geoscience
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The application of optimization and datamining in databases in geosciences is becoming promising, though still at an early stage. We present a case study of the application of datamining and optimization in the prediction of fractures using well-logging data. We compare various approaches, including multiple regression analysis (MRA), back-propagation neural network (BPNN), and support vector machine (SVM). The modelling problem in datamining is formulated as a minimization problem, showing that we can reduce an 8-D problem to a 4-D problem by dimension reduction. The MRA, BPNN and SVM methods are used as optimization techniques for knowledge discovery in data. The calculations for both the learning samples and prediction samples show that both BPNN and SVM can have zero residuals, which suggests that these combined data-mining techniques are practical and efficient.
(from publisher's website)
|Keywords (uncontrolled):||Multiple regression analysis; Back-propagation neural network; Support vector machine; Optimization; Dimension-reduction; Knowledge discovery; Well-logging|
|Research Areas:||A. > School of Science and Technology > Design Engineering and Mathematics|
|Depositing User:||Teddy ~|
|Date Deposited:||19 Nov 2012 11:27|
|Last Modified:||13 Oct 2016 14:25|
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