摘要
为了更好地解决测井岩性识别问题,引入一种快速实用的BP算法Resilient Backpropaga tion (RPROP)算法。在说明RPROP算法的基础上,结合某地的实际测井资料,建立基于RPROP算法的BP网络岩性识别模型,进行岩性识别的应用研究。结果表明,应用RPROP算法进行测井资料岩性识别,识别的准确率较高,与基本BP算法及其一些改进算法相比,训练速度快,具有很好的应用前景。
A fast and practical backpropagation algorithmresilient backpropagation (RPROP) has been introduced to better solve lithologic identification problems using well logging data. A backpropagation neural network model of lithologic identification based on the RPROP algorithm is established to study a real well logging data. The results indicate that the accuracy of identification is high and the RPROP algorithm is fast and practical compared with conventional backpropagation algorithm and some other modified backpropagation algorithm.
出处
《吉林大学学报（地球科学版）》
EI
CAS
CSCD
北大核心
2005年第3期389-393,共5页
Journal of Jilin University：Earth Science Edition
基金
国家"863"计划项目(2001AA135120 1)
关键词
RPROP算法
BP神经网络
测井资料
岩性识别
resilient backpropagation(RPROP) algorithm
backpropagation neural network
logging data
lithologic identification
