Radial Basis Function Network for Well Log Data Inversion

• Main Author: 翁立昇
• Other Authors: 黃國源
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/09272389141671652713

碩士 === 國立交通大學 === 資訊學院資訊學程 === 98 === We adopt the supervised radial basis function network (RBF), and propose the modified two-layer RBF and the modified three-layer RBF for well log data inversion. The input of the network is the apparent conductivity (Ca), and the output is the true formation conductivity (Ct). For the well log data inversion, the number of input nodes is the same as the number of output nodes. We have experiments in simulation and real data application. In simulation, there are 31 sets of simulated well log data. 25 sets are used for training, and 6 sets are used for testing. In the modified two-layer RBF, the first layer is the unsupervised clustering for the training samples. We use K-means clustering algorithm with pseudo F-statistics test to determine the optimal number of clusters that becomes the node number. The second layer is the supervised perceptron. We use the sigmoidal activation function instead of the linear activation function. The delta learning rule replaces the Widrow-Hoff learning rule. It becomes non-linear mapping. Comparing the testing results of different input data length, 10-27-10 can get the smallest error in two-layer RBF. The best 10-27-10 two-layer RBF is expanded to three-layer RBF. We expand the one-layer perceptron to a two-layer perceptron. That can get more non-linear mapping. The number of hidden node is determined by the theorem of Mirchandani and Cao. The best 10-27-9-10 RBF can get the smallest error in the testing. Also, 10-27-9-10 three-layer RBF has smaller error than 10-27-10 two-layer RBF. After the training of the best 10-27-9-10 modified three-layer RBF, we apply it for the inversion of the real field well log data, and the result is acceptable. It shows that the proposed RBF can perform the task of well log data inversion.