| Summary: | 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.
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