Application of Neural Network With New Hybrid Algorithm in Volcanic Rocks Seismic Prediction

Application of Neural Network With New Hybrid Algorithm in Volcanic Rocks Seismic Prediction

Yanchao Liu (School of Automation, Baotou Light Industry Vocational Technical College, Baotou, China), Limei Yan (College of Electrical Information Engineering, Northeast Petroleum University, Daqing, China) and Jianjun Xu (College of Electrical Information Engineering, Northeast Petroleum University, Daqing, China)
DOI: 10.4018/IJCINI.2018100103

Abstract

This article has studied the application design and implementation of neural network with new hybrid algorithm in volcanic rocks prediction. It is considered that the convergence rate of EBP algorithm is slow, and the local minimum value can be obtained by EBP algorithm, and the approximation of global optimal value can be obtained by EBP algorithm. Therefore, genetic algorithm and EBP algorithm are proposed. The weight of the multilayer feed-forward neural network is determined by using the genetic BP algorithm. The new hybrid algorithm is applied to the neural network and volcanic oil and gas identification and compared with the traditional BP neural network. In contrast, using the new hybrid genetic algorithm to calculate the neural network is very small; you can quickly get the global optimal value.
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Extraction And Processing Of Seismic Characteristic Parameters

The extraction of seismic characteristic parameters includes data acquisition, data analysis, variable selection and data preprocessing. Only through these steps can we realize the effective learning and training of neural network.

Normalization

After analyzing the original data we collected, we have determined 20 input variables and 2 output variables on the basis of seismic characteristic parameters (including the actual thickness and the effective thickness of sandstone). Aside from the input variables and output variables, we need to normalize the data, which makes it easier to study and training the neural network. Before the normalization, we have to check whether there are outliers which must be removed.

Normalization transforms the data samples to the range of [-1, 1] or [0, 1] in general.

Assume that and represent the ith input variable and output variable respectively, represents the total number of samples, the number of input variables is , the number of output variables is . Transform the input variable to the range of [-1, 1], then:

(1)

Considering the output of Sigmoid function is [0, 1], so transform the output variable to the range of [0, 1], then:

(2)

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