Optimization and data mining for fracture prediction in geosciences
Article
Shi, G. and Yang, X. 2010. Optimization and data mining for fracture prediction in geosciences. Procedia Computer Science. 1 (1), pp. 1359-1366. https://doi.org/10.1016/j.procs.2010.04.151
Type | Article |
---|---|
Title | Optimization and data mining for fracture prediction in geosciences |
Authors | Shi, G. and Yang, X. |
Abstract | 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. |
Keywords | multiple regression analysis; back-propagation neural network; support vector machine; optimization; dimension-reduction; knowledge discovery; well-logging |
Publisher | Elsevier |
Journal | Procedia Computer Science |
ISSN | 1877-0509 |
Publication dates | |
May 2010 | |
Publication process dates | |
Deposited | 19 Nov 2012 |
Output status | Published |
Additional information | Procedia Computer Science, Volume 1, Issue 1 : ICCS 2010 - International Conference on Computational Science, Proceedings |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.procs.2010.04.151 |
Web of Science identifier | WOS:000281951600150 |
Language | English |
https://repository.mdx.ac.uk/item/83w85
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