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。
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.
出处
《铀矿地质》
CAS
CSCD
2001年第4期239-244,共6页
Uranium Geology
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
artificial neural network
logging data interpretation
learning sample
lithology recognition
porosity forecasting