In this paper, the fundamentals of predicting karst-fractured zones using both seismic attribute technique and pattern recognition method are introduced. Ordovician limestone karst-fractured zones in the First Mining ...In this paper, the fundamentals of predicting karst-fractured zones using both seismic attribute technique and pattern recognition method are introduced. Ordovician limestone karst-fractured zones in the First Mining Area of Wutongzhuang Coal Mine were forecast by using practical seismic data. The result shows that both seismic attribute technique and pattern recognition method are effective in predicting karst-fractured zones.展开更多
Seismic signal is generally employed in moving target monitoring due to its robust characteristic.A recognition method for vehicle and personnel with seismic signal sensing system was proposed based on improved neural...Seismic signal is generally employed in moving target monitoring due to its robust characteristic.A recognition method for vehicle and personnel with seismic signal sensing system was proposed based on improved neural network.For analyzing the seismic signal of the moving objects,the seismic signal of person and vehicle was acquisitioned from the seismic sensor,and then feature vectors were extracted with combined methods after filter processing.Finally,these features were put into the improved BP neural network designed for effective signal classification.Compared with previous ways,it is demonstrated that the proposed system presents higher recognition accuracy and validity based on the experimental results.It also shows the effectiveness of the improved BP neural network.展开更多
文摘In this paper, the fundamentals of predicting karst-fractured zones using both seismic attribute technique and pattern recognition method are introduced. Ordovician limestone karst-fractured zones in the First Mining Area of Wutongzhuang Coal Mine were forecast by using practical seismic data. The result shows that both seismic attribute technique and pattern recognition method are effective in predicting karst-fractured zones.
基金Project(61201028)supported by the National Natural Science Foundation of ChinaProject(YWF-12-JFGF-060)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(2011ZD51048)supported by Aviation Science Foundation of China
文摘Seismic signal is generally employed in moving target monitoring due to its robust characteristic.A recognition method for vehicle and personnel with seismic signal sensing system was proposed based on improved neural network.For analyzing the seismic signal of the moving objects,the seismic signal of person and vehicle was acquisitioned from the seismic sensor,and then feature vectors were extracted with combined methods after filter processing.Finally,these features were put into the improved BP neural network designed for effective signal classification.Compared with previous ways,it is demonstrated that the proposed system presents higher recognition accuracy and validity based on the experimental results.It also shows the effectiveness of the improved BP neural network.
