Method to Predict Coal Seam Thickness and Small Fault Using RS and NN

• 责任者：Wang Xin (School of Computer Science ; China University of Mining and Technology ; Xuzhou 221116 ; China) ; Cui Ruofei
• 刊名：Coal Geology & Exploration
• 出版年：2010
• 卷期：38(3)
• 关键词：神经网络系统 ; neural network system ; 煤层厚度 ; coal thickness
• 页码：p.66-68,72
• 图表：3 illus., 2 tables, 4 refs.
• 分类号：1900
• issnNo：ISSN1001-1986
• isbnNo：CN61-1155/P

The paper put forward a new method of Rough Sets（RS）and Neural Network（NN）technique to detect small faults and coal seam thickness by analyzing 3D seismic data. This method uses RS to reduce seismic data noise, and after reduction, low noise seismic data can be hold. After inputting those reduced data to NN, a predicting model which can detect small faults and predict coal seam thickness can be achieved after NN’s training. After this step, this model was used to detect small fault by 3D seismic data. The authors find that this method has high precision.