Lithological Stratification from Logging Curves Based on Principal Component Self-Organizing Neural Network Method
It is crucial to improve the efficiency and accuracy of lithological stratification from logging curves in the exploration of sandstone type uranium deposits.In order to improve the lithological stratification effect of sandstone type uranium deposits,this work conducted principal component analysis to reduce the dimensionality of multiple logging curves.The first principal component,second principal component,and third principal component of the principal component analysis method were used as sample data for self-organizing neural network training.The trained network model was used for automated stratification of sandstone type uranium deposits.The experimental results show that the principal component self-organizing neural network method has a lithological stratification accuracy of over 85%,which is higher than the traditional self-organizing neural network algorithm's stratification accuracy of 78%,and has better logging lithological stratification effect.Therefore,the lithological stratification method of principal component self-organizing neural network algorithm effectively reduces the types of input samples,simplifies the structure of self-organizing neural network,and its automated stratification effect is better than traditional self-organizing neural network algorithms.The research results indicate that the principal component self-organizing neural network algorithm has good application effects in lithological identification of sandstone type uranium deposits.
万方数据
维普
