Title :
Recognition and Predicting Lava Underground Based on High Speed and Precise Genetic Algorithm Neural Network
Author :
Gao, Meijuan ; Tian, Jingwen ; Li, Jin
Author_Institution :
Beijing Union Univ., Beijing
Abstract :
Deep lava maybe contain petroleum and natural gas or CO2 gas, it is very important to find lava but the lava is in deep stratum and has complex structure, so it is difficult to find lava stratum. There are some methods to predict where the lava is but some problem also is in them such as the precision of recognition and forecasting is not high. A generic algorithm with adaptive and floating-point code is proposed to overcome disadvantages of the genetic algorithm and BP algorithm. This algorithm is combined with BP to give GA-BP mixed algorithm which has higher accuracy and faster convergence speed. The new algorithm also provides improved predict accuracy of lava reservoir. An example shows the validity and feasibility of this algorithm.
Keywords :
genetic algorithms; geophysical techniques; geophysics computing; neural nets; volcanology; BP algorithm; adaptive floating-point code; carbon dioxide gas; generic algorithm; genetic algorithm neural network; lava prediction; lava recognition; lava reservoir; natural gas; petroleum; Accuracy; Character recognition; Chemical technology; Convergence; Genetic algorithms; Hydrocarbon reservoirs; Neural networks; Neurons; Petroleum; Technology forecasting; Characteristic parameter; Genetic algorithms; Lava reservoir; Neural networks; Recognition and predicting;
Conference_Titel :
Information Acquisition, 2006 IEEE International Conference on
Conference_Location :
Shandong
Print_ISBN :
1-4244-0528-9
Electronic_ISBN :
1-4244-0529-7
DOI :
10.1109/ICIA.2006.305895