Application of RPROP Algorithm to Well Logging Lithologic Identification
ZHANG Zhi-guo, YANG Yi-heng, XIA Li-xian (Institute of Mineral Resources Prediction of Synthetic Information, Jilin University, Changchun130026,China)
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.