摘要
将人工神经网络(ANN)模式识别技术应用于对测井曲线进行单井划相,加快了划相速度,大大提高了工作效率。根据某地区的河流微相特征标志,给出了一种基于神经网络的沉积相识别方法。神经网络采用带有动量项和自适应学习率的反向传播算法(BP)进行训练。
Application of the Artificial Neural Network（ANN） to divide facies of single well with well-log can speed up dividing facies and make us get more satisfied results. Based on the sedimentary microfacies characteristic symbol of river in an area, this paper presents a kind of sedimentary facies recognition method based on neural network. The system adopts back-propagation learning algorithm with momentum.
出处
《计算机应用与软件》
CSCD
北大核心
2007年第3期132-134,共3页
Computer Applications and Software
关键词
人工神经网络(ANN)
沉积微相解释
模式识别
反向传播算法
Artificial neural network（ANN） Sedimentary microfacies identification Pattern recognition BP algorithm
