爆破震害预测的神经网络模型
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摘要
人工神经网络是近期发展最快的人工智能领域研究成果之一.通过分析国内外爆破震害预测研究现状和不足,提出一种基于BP神经网络模型的爆破地震效应预测方法,该方法能克服基于最小二乘法的回归公式的局限性,可选取影响爆破振动的多个影响因素作为输入层参数,达到爆破峰值和主频同步预测之目的.利用该方法对实际爆破监测数据进行预测,结果表明人工神经网络方法在爆破地震效应预测中应用是可行的并且是有效的.这为爆破震害预测研究提供了新途径.
The artificial neural network is one of the research result of the artificial intelligence field with quickest development in the near future. Through analysis of the present condition and shortage of forecasting the hazards by blasting vibrations, this article proposes a new forecast method. Based on the BP neural network model of blasting vibration, efficiency, the method can overcomes the return formula's limitation based on the least squares method. It may choose the factors which play important roles in the blasting vibration as the input layer' s parameters, and achieve simultaneously forecasting the peak value and main frequency. The result of real data indicates that the method is feasible and effective. This way will be a new approach to forecasting the hazards by blasting vibrations.
The artificial neural network is one of the research result of the artificial intelligence field with quickest development in the near future. Through analysis of the present condition and shortage of forecasting the hazards by blasting vibrations, this article proposes a new forecast method. Based on the BP neural network model of blasting vibration, efficiency, the method can overcomes the return formula's limitation based on the least squares method. It may choose the factors which play important roles in the blasting vibration as the input layer' s parameters, and achieve simultaneously forecasting the peak value and main frequency. The result of real data indicates that the method is feasible and effective. This way will be a new approach to forecasting the hazards by blasting vibrations.
引文
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[9] 张治国,杨毅恒,夏立显.自组织特征映射神经网络在测井岩性识别中的应用[J].地球物理学进展,2005,20(2) :332-336． Zhang Z G,Yang Y H,Xia L X.The application of self organizing feature map neural network to logging lithological identification[J].Progress in Geophysics,2005,20 (2) : 332-336．
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[12] 赵海娟,王家龙,宗位国,等.用径向基函数神经网络方法预报太阳黑子数平滑月均值[J].地球物理学报,2008,51(1) ,31-35． Zhao H J,Wang J L,Zong W G,et al.Prediction of the smoothed monthly mean sunspot numbers by means of radial basis function neural networks [J].Chinese J.Geophys.,2008,51(1) ,31-35．
[13] 党建武.神经网络技术及其应用[M].北京:中国铁道出版社,2000． Dang J W.Neural networks and its applications [M].Beiiing: China Railway Press,2000．
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[15] 盂召平,郭彦省,王赘,等.基于地震属性的煤层厚度预测模型及其应用[J].地球物理学报,2006,49(2) :512-517． Meng Z P,Guo Y S,Wang Y,et al.Prediction models of coal thickness based on seismic attributions and their applications [J].Chinese J.Geophys.,2006,49(2) :512-517．
[16] GB 50011-2001建筑抗震设计规范[S].GB 50011-2001 Code for seismic design of buildings[S].
[17] 飞思科技产品研发中心.神经网络理论与MATLAB7实现[M].北京:电子工业出版社,2005． Fei Shi technical product R&D center,neural network theory and MATLAB7 realize[M] Beijing : Elect ronic Industry Press,2005．
[18] 王永青,王建宙,汪旭光.露天矿台阶爆破振动频率对临近建筑物的影响[J].有色金属,2002,(5) :39-41． Wang Y Q,Wang J Z,Wang X G.Open-pit mine bench blasting frequency on the impact of nearby buildings [J].Nonferrous Metals,2002,(5) :39-41．
[2] 胡宗武.工程振动分析基础[M].上海:上海交通大学出版社,1985． Hu Z W.Basis of engineering vibration analysis [M].Shanghai: Shanghai Jiao Tong University Press,1985．
[3] 丁柱.杨长春,陶宏根,等.神经网络反演双侧向电阻率测井曲线的物理约束[J].地球物理学进展,2002,17(2) :331-336． Ding Z,Yang C C,Tao H G,et al.Physical restriction on model selection of neural in dual-lateral log inversion[J].Progress in Geophysics,2002,17(2) :331-336．
[4] 敬荣中,鲍光淑.林剑,等. 一种基于数据融合的地球物理数据联合反演方法[J].地球物理学报,2004,47(1) :143-150． Jing R Z,Bao G S,Lin J,et al.A geophysical integration inversion method based on data fusion [J].Chinese J.Geophys.,2004,47(1) : 143-150．
[5] 杨立强,宋海滨,郝天珧.基于BP神经网络的波阻抗反演及应用[J].地球物理学进展,2005,20(1) :34-37． Yang L Q,Song H B,Yao T Q.Application of impedance inversion based on BP neural network [J].Progress in Geophysics ,2005,20(1) :34-37．
[6] 徐海浪,吴小平.电阻率二维神经网络反演[J]地球物理学报,2006,49(2) :584-589． Xu H L,Wu X P.2-D resistivity inversion using the neural network method[J].Chinese J.Geophys.,2006,49 (2) : 584-589．
[7] 黄世奇,刘代志,陈亮.光滑地表面毁伤榆测方法研究[J].地球物理学报,2007,50(4) :1261-1267． Huang S Q,Liu D Z,Chen L.A study of the damage detection method for smooth earth surface [J].Chinese J.Geophys.,2007,50(4) : 1261-1267．
[8] 蔡水库,王有学.人工神经网络系统在接收函数反演中的应用[J].地球物理学进展,2007,22(6) ,1916-1920． Cai S K,Wang Y X.New inversion method of artificial neural network in receiver functions inversion [J].Progress in Geophysics ,2007,22(6) ,1916-1920．
[9] 张治国,杨毅恒,夏立显.自组织特征映射神经网络在测井岩性识别中的应用[J].地球物理学进展,2005,20(2) :332-336． Zhang Z G,Yang Y H,Xia L X.The application of self organizing feature map neural network to logging lithological identification[J].Progress in Geophysics,2005,20 (2) : 332-336．
[10] 郑庆生,韩大匡.高阶神经网络在储层分布参数定量预测中的应用[J].地球物理学进展,2007,22(2) :552-555． Zheng Q S,Han D K.Estimation of reservoir distribution parameters using higher-order neural network spproach[J].Progress in Geophysics ,2007,22(2) : 552-555．
[11] 唐秋华,刘保华,陈永奇,等.结合遗传算法的LVQ神经网络在声学底质分类中的应用,地球物理学报,2007,50(1) ,313-319． Tang Q H,Lu B H,Chen Y Q,et al.Application of LVQ neural network combined with the genetic algorithm in acoustic seafloor classification[J].Chinese J.Geophys.,2007,50(1) ,313-319．
[12] 赵海娟,王家龙,宗位国,等.用径向基函数神经网络方法预报太阳黑子数平滑月均值[J].地球物理学报,2008,51(1) ,31-35． Zhao H J,Wang J L,Zong W G,et al.Prediction of the smoothed monthly mean sunspot numbers by means of radial basis function neural networks [J].Chinese J.Geophys.,2008,51(1) ,31-35．
[13] 党建武.神经网络技术及其应用[M].北京:中国铁道出版社,2000． Dang J W.Neural networks and its applications [M].Beiiing: China Railway Press,2000．
[14] 杨建刚.人工神经网络实用教程[M].杭州:浙江大学出版社,2001． Yang J G.Practical course of artificial neural network[M].Hangzhou: Zhejiang University Press,2001．
[15] 盂召平,郭彦省,王赘,等.基于地震属性的煤层厚度预测模型及其应用[J].地球物理学报,2006,49(2) :512-517． Meng Z P,Guo Y S,Wang Y,et al.Prediction models of coal thickness based on seismic attributions and their applications [J].Chinese J.Geophys.,2006,49(2) :512-517．
[16] GB 50011-2001建筑抗震设计规范[S].GB 50011-2001 Code for seismic design of buildings[S].
[17] 飞思科技产品研发中心.神经网络理论与MATLAB7实现[M].北京:电子工业出版社,2005． Fei Shi technical product R&D center,neural network theory and MATLAB7 realize[M] Beijing : Elect ronic Industry Press,2005．
[18] 王永青,王建宙,汪旭光.露天矿台阶爆破振动频率对临近建筑物的影响[J].有色金属,2002,(5) :39-41． Wang Y Q,Wang J Z,Wang X G.Open-pit mine bench blasting frequency on the impact of nearby buildings [J].Nonferrous Metals,2002,(5) :39-41．
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