摘要
在可地浸砂岩型铀矿测井工作中,铀矿地球物理测井方法提供了电阻率、密度、声波等多种地球物理参数。通过采用神经网络方法,利用上述参数,进行钻孔岩性识别的有关研究,确定了B-P网络结构及算法,并进行了初步应用和对比。
The well logging explanation for the uranium deposits of leaching sandstone-type need to distinguish the lithology. Based upon the multi-parameters about resistivity, density, sonic wave and so on, we select a three-layer B-P network and adopt advanced B-P algorithm to finish the lithology discrimination. This method has been applied in a testing area.
出处
《物探化探计算技术》
CAS
CSCD
2004年第3期220-223,共4页
Computing Techniques For Geophysical and Geochemical Exploration
基金
核工业地质局地质科技资助项目"可地浸砂岩型铀矿地球物理测井工作站研究"。
关键词
神经网络方法
岩性识别
铀矿测井
B-P算法
uranium deposits
neural network
logging explanation
lithology discrimination
B-P algorithm
