自组织神经网络在测井资料解释中的应用

Application of Self-Organizing Neural Network to Logging Data Interpretation

侯俊胜尉中良：自组织神经网络在测并资料解释中的应用，测井技术，１９９６（３）２０，１９７～２００。本文描述了自组织神经网络──改进的ＡＲＴ模型（包氏神经网络）的基本结构及其学习算法；给出了应用该网络进行测井资料解释的计算步骤；最后，以人工合成数据的自动分类和某煤田综合测井数据的煤层自动识别为例，检验了该方法的正确性与有效性。

Firstly,the basic structure of improved ART model (Pao's neural net-work)-self-organizing neural network and its learning algorithm are discussed. Secondly, the computing procedures for log data interpretation with the above networkare presented. Finally, based on the auto-classification of synthetic data and autorecognition of coal beds by use of this network, the reliability and effectiveness ofself-organizing neural network have been examined.