基于GIS与ANN的金川二矿地表移动预测
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摘要
利用金川二矿区多年的GPS监测数据,探讨了GIS与人工神经网络技术相结合进行地表移动定量预测的方法和思路。借助Avenue语言,实现了在GIS平台下进行地表移动神经网络预测的整个过程,包括样本设计、网络设计与训练、网络测试与网络预测等;其中,数据处理、测试结果对比和预测结果分析由GIS软件完成,采用VB调用Matlab6.5实现Elman神经网络预测模型,并通过Avenue编程将其集成到GIS系统中。研究结果表明,利用GIS支持下的神经网络模型对地表移动进行预测,具有理论上的可行性和现实意义,说明GIS和人工神经网络技术在开采沉陷预计领域中具有广阔的应用前景。
Based on GPS monitoring data on No.2 Nickel Mine Area in Jinchuan,a new idea of quantitative prediction of ground surface movement by means of GIS and ANN(Artificial Neural Network) was put forward and the specific method was given.The whole process of ground surface movement prediction based on ANN may be carried out in GIS platform by Avenue programming,including designing data mode,designing and training network framework,testing network framework,forecasting by network,and so on.Such works as data processing,contrasting testing results and analysing forecasting results were achieved by GIS.The Elman ANN prediction model was realized by the technique of Matlab 6.5 program with VB6.0,which was integrated in GIS by Avenue programming.The results stated that the ANN prediction model supported by GIS had a theoretical feasibility and realistic significance in predicting ground surface movement,and GIS and ANN had a wide application prospect in prediction of exploitation subsidence.
Based on GPS monitoring data on No.2 Nickel Mine Area in Jinchuan,a new idea of quantitative prediction of ground surface movement by means of GIS and ANN(Artificial Neural Network) was put forward and the specific method was given.The whole process of ground surface movement prediction based on ANN may be carried out in GIS platform by Avenue programming,including designing data mode,designing and training network framework,testing network framework,forecasting by network,and so on.Such works as data processing,contrasting testing results and analysing forecasting results were achieved by GIS.The Elman ANN prediction model was realized by the technique of Matlab 6.5 program with VB6.0,which was integrated in GIS by Avenue programming.The results stated that the ANN prediction model supported by GIS had a theoretical feasibility and realistic significance in predicting ground surface movement,and GIS and ANN had a wide application prospect in prediction of exploitation subsidence.
引文
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[11]顾国华,王若柏,孙慧娟,等.华北地区GPS形变测量结果及其地震地质意义[J].地震地质,1999,21(2):98-104.
[12]冯仲科,张志良,郭学林.GPS基本原理及其在水土保持工作中的应用[J].中国水土保持,1999(10):36-37.
[13]全斌.GPS在矿区滑坡治理工程中的应用[J].测绘通报,2000(9):35-36.
[14]郑国忠,徐嘉谟,马凤山,等.GPS技术在金川露天矿边坡变形监测中的应用[J].工程地质学报,1998,6(3):282-288.
[15]周科平.GPS和GIS在矿山工程地质灾害监测中的应用[J].采矿技术,2003,3(2):5-9.
[16]杨玉访.GPS在矿山控制测量中的应用[J].金属矿山,2009(6):117-162.
[2]高玮,郑颖人.岩土工程中的几种位移预测方法探讨[C]//中国岩石力学与工程学会第六次学术大会论文集.北京:中国科学技术出版社,2000.
[3]Tomam AmbromiW,Goran Turk.Prediction of subsidence due tounderground mining by artificial neural networks[J].Computers&Geosciences,2003,29:627-637.
[4]麻凤海,王泳嘉,范学理.利用神经网络预测开采引起地表沉陷[J].阜新矿业学院学报:自然科学版,1995,14(3):46-49.
[5]曹丽文,姜振泉.人工神经网络在煤矿开采沉陷预计中的应用研究[J].中国矿业大学学报,2002,31(1):23-26.
[6]栾元重.神经网络在矿山地表移动参数辨识中的应用[J].矿山测量,1998(2):42-44.
[7]姚大勇,邵军.节理岩体地层沉陷的进化神经网络预测方法研究[J].矿业安全与环保,2002,29(3):11-13.
[8]Hiroshi P.Sato,Kaoru Abe,Osamu Ootaki.GPS-measured landsubsidence in Ojiya City,Niigata Prefecture,Japan[J].Engineer-ing Geology,2003,67:379-390.
[9]Yang Guohua,Han Yueping et al.Determination of active unitswith different kinematic property and their activity pattern in NorthChina based on the data from GPS remeasurements[J].Acta Seis-mologica Sinica,2001,14(1):1-11.
[10]帅平,吴云,周硕愚.用GPS测量数据模拟中国大陆现今地壳水平速度场及应变场[J].地壳形变与地震,1999,19(2):1-8.
[11]顾国华,王若柏,孙慧娟,等.华北地区GPS形变测量结果及其地震地质意义[J].地震地质,1999,21(2):98-104.
[12]冯仲科,张志良,郭学林.GPS基本原理及其在水土保持工作中的应用[J].中国水土保持,1999(10):36-37.
[13]全斌.GPS在矿区滑坡治理工程中的应用[J].测绘通报,2000(9):35-36.
[14]郑国忠,徐嘉谟,马凤山,等.GPS技术在金川露天矿边坡变形监测中的应用[J].工程地质学报,1998,6(3):282-288.
[15]周科平.GPS和GIS在矿山工程地质灾害监测中的应用[J].采矿技术,2003,3(2):5-9.
[16]杨玉访.GPS在矿山控制测量中的应用[J].金属矿山,2009(6):117-162.
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