Method to Predict Coal Seams Thickness and Fine Fault Using RS and NN

摘要：

This paper puts forward a new method of Rough Sets (RS) and Neural Network (NN) which is used to detect fine faults and coal seam thickness by analyzing 3D seismic data. This method uses RS to reduce seismic data containing noise, and after reduction, low noise seismic data can be hold. Then input those reduced data to NN, a predicting model which can detect fine faults and predict coal seams thickness can be achieved after NN training. After this step, this model was used to detect fine fault of 3D seismic data. We find that this method has a higher precision.

关键词： rough sets neural network predicting fine fault coalbed thickness

作者: Wang Xin Cui Ruo-fei Chen Tong-jun

作者单位: School of Computer Science China University Of Mining and Technology Xuzhou, China School of Research and Earth Science China University Of Mining and Technology Xuzhou, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 447-450

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）