刘明军, 李恒堂, 姜在炳. GA-BP神经网络模型在彬长矿区测井岩性识别中的应用[J]. 煤田地质与勘探, 2011, 39(4): 8-12. DOI: 10.3969/j.issn.1001-1986.2011.04.003
引用本文: 刘明军, 李恒堂, 姜在炳. GA-BP神经网络模型在彬长矿区测井岩性识别中的应用[J]. 煤田地质与勘探, 2011, 39(4): 8-12. DOI: 10.3969/j.issn.1001-1986.2011.04.003
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LIU Mingjun, LI Hengtang, JIANG Zaibing. Application of genetic-BP neural network model in lithology identification by logging data in Binchang mining area[J]. COAL GEOLOGY & EXPLORATION, 2011, 39(4): 8-12. DOI: 10.3969/j.issn.1001-1986.2011.04.003
Citation: LIU Mingjun, LI Hengtang, JIANG Zaibing. Application of genetic-BP neural network model in lithology identification by logging data in Binchang mining area[J]. COAL GEOLOGY & EXPLORATION, 2011, 39(4): 8-12. DOI: 10.3969/j.issn.1001-1986.2011.04.003
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GA-BP神经网络模型在彬长矿区测井岩性识别中的应用

Application of genetic-BP neural network model in lithology identification by logging data in Binchang mining area

    摘要
  • 摘要: 为提高测井岩性识别的自动化程度和地质解释精度，在分析遗传算法(Genetic Algorithm，简称GA)与误差反向传播算法(Back-Propagation，简称BP)各自特性的基础上，针对BP算法在反演中测井数据识别样本大以及BP算法本身存在的缺陷，提出了利用GA算法来同时优化BP神经网络的结构和连接权值的解决方案，建立了基于GA优化BP神经网络的测井数据岩性识别模型。该模型通过彬长矿区实际数据的检验，获得了较高的识别速度和准确率。

     

    Abstract: Based on the analysis of features of Genetic Algorithm (GA) and Back-Propagation Algorithm (BP), it is concluded that the disadvantage of BP algorithm includes large identification specimen in inversion, so a methodology to optimize BP network structure and link weights with GA is proposed and the lithology identification model based on GA optimized BP algorithm is established.Using the basic data from Binchang mining area, the lithology identification function is tested, and the result indicates that the GA-BP neural network model has good identification speed and accuracy.

     

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