| 人工神经网络在铀矿测井解释中的应用 |
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| 引用本文： | 祖秀兰,周蓉生,马英杰. 人工神经网络在铀矿测井解释中的应用[J]. 铀矿地质, 2001, 17(4): 239-244 |
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| 作者姓名： | 祖秀兰 周蓉生 马英杰 |
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| 作者单位： | 中国工程物理研究院核物理与化学研究所 |
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| 摘 要： | 本文探讨了运用人工神经网络方法完成铀矿测井解释任务的有关问题。采用了改进的BP算法，提高了网络收敛速度，优化了网络结构。研究使用了一种基于统计的学习样本生成方法，提高了样本的质量。实际应用网络进行岩性识别和孔隙度预测，取得了令人满意的结果。
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| 关 键 词： | 人工神经网络 测井资料解释 学习样本 岩性识别 孔隙度预测 铀矿床 |
| 文章编号： | 1000-0658(2001)04-0239-06 |
| 修稿时间： | 2000-09-28 |
Application of artificial neural network technique to uranium logging datainterpretation |
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| ZU Xiu lan ZHOU Rong sheng MA Ying jie. Application of artificial neural network technique to uranium logging datainterpretation[J]. Uranium Geology, 2001, 17(4): 239-244 |
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| Authors: | ZU Xiu lan ZHOU Rong sheng MA Ying jie |
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| Abstract: | This article describes the application of B P technique to pertinent questions in uranium logging data interpretation. Adopting the improved B P algorithm to establish network structure and thoroughly investigating the process, and the method constructing learning sample has been improved. Good results have been achieved through applying the B P model to recognize lithologies and forecast porosity. |
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| Keywords: | artificial neural network logging data interpretation learning sample lithology recognition porosity forecasting |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
