DocumentCode :
1902842
Title :
Well log data inversion using radial basis function network
Author :
Huang, Kou-Yuan ; Shen, Liang-Chi ; Weng, Li-Sheng
Author_Institution :
Dept. of Comput. Sci., Nat. Chiao Tung Univ., Hsinchu, Taiwan
fYear :
2011
fDate :
24-29 July 2011
Firstpage :
4439
Lastpage :
4442
Abstract :
We use the radial basis function network (RBF) for well log data inversion. The first step of the network is the K-means clustering. For the second step, we adopt the 2-layer perceptron instead of conventional 1-layer perceptron. The 2-layer perceptron can do the more nonlinear mapping. The gradient descent method is used in the back propagation learning rule at the second step. The input of the network is the apparent conductivity (Ca) and the output of the network is the true formation conductivity (Ct). The original features are the network input for training process. According to our experimental results, the three-layer radial basis function can get smaller error between the desired output and the actual output. The network with 10 input features, first layer with 27 nodes, second layer with 10 hidden node, and 10 output nodes can get the smallest average mean absolute error on simulated well log data. After simulation, we apply the network to the real field data. The result is good. It shows that the RBF can do the well log data inversion.
Keywords :
geophysics computing; inverse problems; learning (artificial intelligence); perceptrons; radial basis function networks; well logging; apparent conductivity; backpropagation learning rule; gradient descent method; k-means clustering; nonlinear mapping; radial basis function network; three layer radial basis function; true formation conductivity; two layer perceptron; well log data inversion; Clustering algorithms; Conductivity; Educational institutions; Entropy; Radial basis function networks; Training; Radial basis function network; multilayer perceptron; well log inversion;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International
Conference_Location :
Vancouver, BC
ISSN :
2153-6996
Print_ISBN :
978-1-4577-1003-2
Type :
conf
DOI :
10.1109/IGARSS.2011.6050217
Filename :
6050217
Link To Document :
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