基于自适应进化相关向量机的盲矿预测模型
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摘要
近年来,找盲矿法从原生晕叠加理论发展到构造叠加晕理论,显示高矿区的成矿过程具有多期、多阶段叠加的特征,这增加了盲矿信息源、提高了找矿预测精准度。同时,构造叠加晕法在盲矿预测模型建立过程中存在非线性、稀疏化及结果可靠性评价等难点问题亟待解决,本文结合相关向量机的优点,对盲矿预测的构造叠加晕法进行参数改进,提出一种新的基于自适应进化相关向量机的构造叠加晕盲矿预测模型,以期提高深部盲矿预测的准确性。
In recent years,the blind mine method has been developed from the original halo superposition theory to the structural superimposed halo theory,which shows that the mineralization process in the high mining area has multi-period and multi-stage superposition characteristics,which increases the blind mine information source and improves the prospecting prediction accuracy degree. At the same time,the construction superimposed halo method is difficult to solve in the process of establishing blind mine prediction model, such as nonlinearity,sparseness and reliability evaluation. The paper combines and improves the correlation vector machine and blind mine prediction structure. Based on adaptive evolution correlation vector machine to construct a superimposed bluff mine prediction model,in order to improve the accuracy of deep blind mine prediction.
In recent years,the blind mine method has been developed from the original halo superposition theory to the structural superimposed halo theory,which shows that the mineralization process in the high mining area has multi-period and multi-stage superposition characteristics,which increases the blind mine information source and improves the prospecting prediction accuracy degree. At the same time,the construction superimposed halo method is difficult to solve in the process of establishing blind mine prediction model, such as nonlinearity,sparseness and reliability evaluation. The paper combines and improves the correlation vector machine and blind mine prediction structure. Based on adaptive evolution correlation vector machine to construct a superimposed bluff mine prediction model,in order to improve the accuracy of deep blind mine prediction.
引文
[1]张凡.国内外成矿预测研究进展和趋势[J].矿物学报,2015,(S1):1090.
[2]李惠,禹斌,李德亮,等.构造叠加晕法预测盲矿的关键技术[J].物探与化探,2014,38(2):189-193.
[3]李惠,张国义,高延龙,等.秦岭金矿集中区深部第二富集带预测的构造叠加晕模型[J].物探与化探,2008,32(10):25-52.
[4]李瑞金.沂河健康评价与生态修复对策[D].扬州大学,2009.
[5] Price K,Storn R,Lampinen J. Differential evolution:A practical approach to global Optimization[M]. Berlin Heidelberg:Springer,2005.
[6]刘学艺,李平,郜传厚.极限学习机的快速留一交叉验证算法[J].上海交通大学学报,2011,45(8):1140-1146.
[7] Wang F F,Zhang Y R. Relevance vector machine technique for the inverse scattering problem[J]. Chinese Physics B,2012,21(5):19-24.
[8]李惠,禹斌,李德亮.构造叠加晕找盲矿法及找矿效果[M].北京:地质出版,2011:64-68.
[9]李惠,禹斌,李德亮,等.构造叠加晕法预测盲矿的关键技术[J].物探与化探,2014,38(2):189-193.
[10]李惠,禹斌,马久菊,等.构造叠加晕预测侧伏矿体深部盲矿的方法及实用模型[J].矿产勘查,2016,7(6):971-976.
[11]王一大,马久菊,李惠,等.黑龙江三道湾子金矿深部盲矿预测的构造叠加晕研究[J].地质找矿论丛,2015,12(5):623-624.
[12]李惠,禹斌,李德亮,等.不同类型金矿深部盲矿预测的构造叠加晕模型[J].矿产与地质,2015,10(5):648-651.
[13]苑进.贝叶斯学习框架下非线性制造过程建模及多目标优化关键技术研究[D].上海大学.
[14]李惠,岑况,沈镛立,等.危机矿山深部及其外围盲矿预测的化探新方法及其最佳组合[J].地质与勘探,2006,(4):62-68.
[15]罗亦泳,张豪,张立亭.基于自适应进化相关向量机的耕地面积预测模型[J].农业工程学报,2015,31(9):257-263.
[16]楼俊钢,蒋云良,申情,等.软件可靠性预测中不同核函数的预测能力评估[J].计算机学报2013,36(6):1303-1311.
[17]Arsanjani J J,Kainza H,Kainza W. Integration of logistic regression,Markov chain and cellular automata models to simulate urban expansion[J]. International Journal of Applied Earth Observation and Geoinformation,2013,21(3):265-275.
[18]刘学艺,李平,郜传厚.极限学习机的快速留一交叉验证算法[J].上海交通大学学报,2011,45(8):1140-1146.
[19]徐继红.基于自适应进化相关向量机的城市需水量预测模型研究[J].水资源开发与管理,2016,(1):45-52.
[20]马久菊,张永强,望开磊,等.甘肃天水宽沟金矿床构造叠加晕研究及盲矿预测[J].黄金科学技术,2012,20(1):27-31.
[2]李惠,禹斌,李德亮,等.构造叠加晕法预测盲矿的关键技术[J].物探与化探,2014,38(2):189-193.
[3]李惠,张国义,高延龙,等.秦岭金矿集中区深部第二富集带预测的构造叠加晕模型[J].物探与化探,2008,32(10):25-52.
[4]李瑞金.沂河健康评价与生态修复对策[D].扬州大学,2009.
[5] Price K,Storn R,Lampinen J. Differential evolution:A practical approach to global Optimization[M]. Berlin Heidelberg:Springer,2005.
[6]刘学艺,李平,郜传厚.极限学习机的快速留一交叉验证算法[J].上海交通大学学报,2011,45(8):1140-1146.
[7] Wang F F,Zhang Y R. Relevance vector machine technique for the inverse scattering problem[J]. Chinese Physics B,2012,21(5):19-24.
[8]李惠,禹斌,李德亮.构造叠加晕找盲矿法及找矿效果[M].北京:地质出版,2011:64-68.
[9]李惠,禹斌,李德亮,等.构造叠加晕法预测盲矿的关键技术[J].物探与化探,2014,38(2):189-193.
[10]李惠,禹斌,马久菊,等.构造叠加晕预测侧伏矿体深部盲矿的方法及实用模型[J].矿产勘查,2016,7(6):971-976.
[11]王一大,马久菊,李惠,等.黑龙江三道湾子金矿深部盲矿预测的构造叠加晕研究[J].地质找矿论丛,2015,12(5):623-624.
[12]李惠,禹斌,李德亮,等.不同类型金矿深部盲矿预测的构造叠加晕模型[J].矿产与地质,2015,10(5):648-651.
[13]苑进.贝叶斯学习框架下非线性制造过程建模及多目标优化关键技术研究[D].上海大学.
[14]李惠,岑况,沈镛立,等.危机矿山深部及其外围盲矿预测的化探新方法及其最佳组合[J].地质与勘探,2006,(4):62-68.
[15]罗亦泳,张豪,张立亭.基于自适应进化相关向量机的耕地面积预测模型[J].农业工程学报,2015,31(9):257-263.
[16]楼俊钢,蒋云良,申情,等.软件可靠性预测中不同核函数的预测能力评估[J].计算机学报2013,36(6):1303-1311.
[17]Arsanjani J J,Kainza H,Kainza W. Integration of logistic regression,Markov chain and cellular automata models to simulate urban expansion[J]. International Journal of Applied Earth Observation and Geoinformation,2013,21(3):265-275.
[18]刘学艺,李平,郜传厚.极限学习机的快速留一交叉验证算法[J].上海交通大学学报,2011,45(8):1140-1146.
[19]徐继红.基于自适应进化相关向量机的城市需水量预测模型研究[J].水资源开发与管理,2016,(1):45-52.
[20]马久菊,张永强,望开磊,等.甘肃天水宽沟金矿床构造叠加晕研究及盲矿预测[J].黄金科学技术,2012,20(1):27-31.
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