標題: | Higher Order Neural Networks for Well Log Data Inversion |
作者: | Huang, Kou-Yuan Shen, Liang-Chi Chen, Chun-Yu 資訊工程學系 Department of Computer Science |
公開日期: | 2008 |
摘要: | Multilayer perceptron is adopted for well log data inversion. The input of the neural network is the apparent resistivity (Ra) of the well log and the desired output is the true formation resistivity (Rt). The higher order of the input features and the original features are the network input for training. Gradient descent method is used in the back propagation learning rule. From our experimental results, we find the expanding input features can get fast convergence in training and decrease the mean absolute error between the desired output and the actual output. The multilayer perceptron network with 10 input features, the expanding input features to the third order, 8 hidden nodes, and 10 output nodes can get the smallest average mean absolute error on simulated well log data. And then the system is applied on the real well log data. |
URI: | http://hdl.handle.net/11536/2841 |
ISBN: | 978-1-4244-1820-6 |
ISSN: | 1098-7576 |
期刊: | 2008 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-8 |
起始頁: | 2545 |
結束頁: | 2550 |
顯示於類別: | 會議論文 |