基于改进遗传算法-神经网络的玄武岩构造环境判别及对比实验
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摘要
通过岩浆岩的地球化学特征判别岩浆形成的大地构造环境和岩浆源区的化学性质是地球化学全岩分析最重要的应用之一。该方法利用全岩地球化学数据,包括主量元素、微量元素和同位素组成数据,对给定岩浆岩(玄武岩、花岗岩等)的大地构造环境进行判别。作为人工智能技术在地球化学研究领域中的新尝试,机器学习判别方法逐渐成为经典判别图解法的补充研究手段。然而,高维数据特征筛选和繁多未知参数确定是影响算法分类准确性的两个主要因素。为此,提出一种遗传算法优化神经网络耦合判别方法(GA-NNDM)。该方法利用特征选择、参数确定和分类性能之间的反馈联系,将分类准确率、所选特征数量和特征代价作为适应度函数,通过迭代演化寻求最佳特征子集和未知参数,从而达到减少特征、优化参数和提高性能的目的。此外,根据公开玄武岩样品地球化学数据,通过K折交叉验证等方法设置纵向、横向比较实验来验证GANNDM在玄武岩构造环境判别方面的准确性、稳定性和外延性。仿真实验结果表明,GA-NNDM具有优良的判别效果和泛化能力,其总体分类准确率能达到90%。因此,GA-NNDM值得在地球化学领域做进一步推广应用。
One of the most important applications of geochemical whole-rock analysis is to discriminate the tectonic settings for magma formation and properties of magmatic source areas through geochemical characteristics of magmatic rocks.This approach allows discrimination of tectonic setting of a given suite of magmatic rocks(basalt,granite,etc.)using whole-rock geochemical data including major and trace elemental and isotopic compositions.As a new application of artificial intelligence technique in the field of geochemistry,machine learning discrimination algorithm has gradually become a research tool supplementary to the classical discrimination diagram approach.However,the algorithm's classification accuracy is affected by two main factors:high-dimensional data feature screening and multiple unknown parameter determination.To this end,we propose here a coupling discrimination method involving improved genetic algorithm and optimized neural network(GA-NNDM),based on genetic algorithm(GA)and neural network discrimination method(NNDM).The proposed method uses the feedback links between feature selection,parameter determination and classification performance.It treats classification accuracy as a fitness function and seeks the best feature subset and unknown parameters through iterative evolution.As a result,data features are reduced,unknown parameters are optimized and classification performance is improved.In addition,according to the published geochemical data of basalt samples,vertical and horizontal comparison experiments are set up through K-fold cross-validation method to verify accuracy,stability and extensibility of GA-NNDM in the application of basalt tectonic setting discrimination.Simulation results show that GANNDM has an excellent discrimination effect and generalization ability,with the overall classification accuracy near 90%.We conclude that,as a whole,GA-NNDM can be applied widely in geochemistry.
One of the most important applications of geochemical whole-rock analysis is to discriminate the tectonic settings for magma formation and properties of magmatic source areas through geochemical characteristics of magmatic rocks.This approach allows discrimination of tectonic setting of a given suite of magmatic rocks(basalt,granite,etc.)using whole-rock geochemical data including major and trace elemental and isotopic compositions.As a new application of artificial intelligence technique in the field of geochemistry,machine learning discrimination algorithm has gradually become a research tool supplementary to the classical discrimination diagram approach.However,the algorithm's classification accuracy is affected by two main factors:high-dimensional data feature screening and multiple unknown parameter determination.To this end,we propose here a coupling discrimination method involving improved genetic algorithm and optimized neural network(GA-NNDM),based on genetic algorithm(GA)and neural network discrimination method(NNDM).The proposed method uses the feedback links between feature selection,parameter determination and classification performance.It treats classification accuracy as a fitness function and seeks the best feature subset and unknown parameters through iterative evolution.As a result,data features are reduced,unknown parameters are optimized and classification performance is improved.In addition,according to the published geochemical data of basalt samples,vertical and horizontal comparison experiments are set up through K-fold cross-validation method to verify accuracy,stability and extensibility of GA-NNDM in the application of basalt tectonic setting discrimination.Simulation results show that GANNDM has an excellent discrimination effect and generalization ability,with the overall classification accuracy near 90%.We conclude that,as a whole,GA-NNDM can be applied widely in geochemistry.
引文
[1]刘学龙.中甸岛弧岩浆岩地球化学特征及斑岩型铜矿成矿研究[D].昆明:昆明理工大学,2009.
[2]刘欣雨.全球新生代安山岩的时空分布及其构造环境研究[D].西安:西北大学,2017.
[3]丛智超,孙丰月,王冠,等.佳木斯地块中部岩浆岩锆石U-Pb年代学、地球化学及其大地构造意义[J].岩石学报,2016,32(4):1141-1152.
[4]李建红,卫三元,梁良,等.贵东岩体地质地球化学特征及其形成的构造环境识别[J].铀矿地质,2004,20(6):321-329.
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[6] PEARCE J A,CANN J R.Tectonic setting of basic volcanic rocks determined using trace element analyses[J].Earth and Planetary Science Letters,1973,19(2):290-300.
[7]李友余.金沙江—哀牢山俯冲-碰撞造山带南段绿春岛弧带的确认及特征[D].成都:成都理工大学,2013.
[8]杨婧,王金荣,张旗,等.全球岛弧玄武岩数据挖掘:在玄武岩判别图上的表现及初步解释[J].地质通报,2016,35(12):1937-1949.
[9]张旗.如何正确使用玄武岩判别图[J].岩石学报,1990,6(2):89-96.
[10]罗建民,王晓伟,宋秉田,等.岩浆岩定量分类方法探讨:以甘肃省西秦岭地区为例[J].岩石学报,2018,34(2):326-332.
[11] VERMEESCH P.Tectonic discrimination of basalts with classification trees[J].Geochimica et Cosmochimica Acta,2006,70(7):1839-1848.
[12] UEKI K,HINO H,KUWATANI T.Geochemical discrimination and characteristics of magmatic tectonic settings:a machine-learning-based approach[J].Geochemistry,Geophysics,Geosystems,2018,19(4):1327-1347.
[13] PETRELLI M,PERUGINI D.Solving petrological problems through machine learning:the study case of tectonic discrimination using geochemical and isotopic data[J].Contributions to Mineralogy and Petrology,2016,171(10):81.
[14]杨平,王新民,路来君.基于改进的数量化理论和RBF神经网络组合方法的地下水水质预测[J].地学前缘,2016,23(3):151-155.
[15]周立宏,孙志华,王振升,等.印缅俯冲增生楔气烟囱分带性及油气成藏规律[J].地学前缘,2017,24(4):352-369.
[16]姚敏,王卫红.演化计算技术[J].海南大学学报(自然科学版),2000,18(1):77-80.
[17]魏加华,张远东.基于多目标遗传算法的巨型水库群发电优化调度[J].地学前缘,2010,17(6):255-262.
[18]罗银河,刘江平,肖长安.优化遗传算法在孔隙度预测中的应用[J].地学前缘,2003,10(1):213-218.
[19] DAYHOFF J E.Neural network architectures:an introduction[M].New York:Van Nostrand Reinhold,1990.
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[22]周明,孙树栋.遗传算法原理及应用[M].北京:国防工业出版社,1999.
[23] HUANG C L,WANG C J.A GA-based feature selection and parameters optimization for support vector machines[J].Expert Systems with Applications,2006,31(2):231-240.
[24] LIN S W,YING K C,CHEN S C,et al.Particle swarm optimization for parameter determination and feature selection of support vector machines[J].Expert Systems with Applications,2008,35(4):1817-1824.
[25] KOHAVI R.A study of cross-validation and bootstrap for accuracy estimation and model selection[C]∥Proceedings of International Joint Conference on Artificial Intelligence.San Francisco:Morgan Kaufmann Publishers,1995:1137-1143.
[26]周志华.机器学习[M].北京:清华大学出版社,2016:28-37.
[27] STEHMAN S V.Selecting and interpreting measures of thematic classification accuracy[J].Remote Sensing of Environment,1997,62(1):77-89.
[28] IWAMORI H,YOSHIDA K,NAKAMURA H,et al.Classification of geochemical data based on multivariate statistical analyses:complementary roles of cluster,principal component,and independent component analyses[J].Geochemistry,Geophysics,Geosystems,2017,18(3):994-1012.
[2]刘欣雨.全球新生代安山岩的时空分布及其构造环境研究[D].西安:西北大学,2017.
[3]丛智超,孙丰月,王冠,等.佳木斯地块中部岩浆岩锆石U-Pb年代学、地球化学及其大地构造意义[J].岩石学报,2016,32(4):1141-1152.
[4]李建红,卫三元,梁良,等.贵东岩体地质地球化学特征及其形成的构造环境识别[J].铀矿地质,2004,20(6):321-329.
[5]颜耀阳.玄武岩的构造环境判别及其多解性分析[J].地质调查与研究,1994(4):71-75.
[6] PEARCE J A,CANN J R.Tectonic setting of basic volcanic rocks determined using trace element analyses[J].Earth and Planetary Science Letters,1973,19(2):290-300.
[7]李友余.金沙江—哀牢山俯冲-碰撞造山带南段绿春岛弧带的确认及特征[D].成都:成都理工大学,2013.
[8]杨婧,王金荣,张旗,等.全球岛弧玄武岩数据挖掘:在玄武岩判别图上的表现及初步解释[J].地质通报,2016,35(12):1937-1949.
[9]张旗.如何正确使用玄武岩判别图[J].岩石学报,1990,6(2):89-96.
[10]罗建民,王晓伟,宋秉田,等.岩浆岩定量分类方法探讨:以甘肃省西秦岭地区为例[J].岩石学报,2018,34(2):326-332.
[11] VERMEESCH P.Tectonic discrimination of basalts with classification trees[J].Geochimica et Cosmochimica Acta,2006,70(7):1839-1848.
[12] UEKI K,HINO H,KUWATANI T.Geochemical discrimination and characteristics of magmatic tectonic settings:a machine-learning-based approach[J].Geochemistry,Geophysics,Geosystems,2018,19(4):1327-1347.
[13] PETRELLI M,PERUGINI D.Solving petrological problems through machine learning:the study case of tectonic discrimination using geochemical and isotopic data[J].Contributions to Mineralogy and Petrology,2016,171(10):81.
[14]杨平,王新民,路来君.基于改进的数量化理论和RBF神经网络组合方法的地下水水质预测[J].地学前缘,2016,23(3):151-155.
[15]周立宏,孙志华,王振升,等.印缅俯冲增生楔气烟囱分带性及油气成藏规律[J].地学前缘,2017,24(4):352-369.
[16]姚敏,王卫红.演化计算技术[J].海南大学学报(自然科学版),2000,18(1):77-80.
[17]魏加华,张远东.基于多目标遗传算法的巨型水库群发电优化调度[J].地学前缘,2010,17(6):255-262.
[18]罗银河,刘江平,肖长安.优化遗传算法在孔隙度预测中的应用[J].地学前缘,2003,10(1):213-218.
[19] DAYHOFF J E.Neural network architectures:an introduction[M].New York:Van Nostrand Reinhold,1990.
[20] LESHNO M,LIN V Y,PINKUS A,et al.Multilayer feedforward networks with a nonpolynomial activation function can approximate any function[J].Neural Networks,1993,6(6):861-867.
[21] HOLLAND J H.Adaptation in natural and artificial systems:an introductory analysis with applications to biology,control,and artificial intelligence[M].Cambridge:MIT Press,1992.
[22]周明,孙树栋.遗传算法原理及应用[M].北京:国防工业出版社,1999.
[23] HUANG C L,WANG C J.A GA-based feature selection and parameters optimization for support vector machines[J].Expert Systems with Applications,2006,31(2):231-240.
[24] LIN S W,YING K C,CHEN S C,et al.Particle swarm optimization for parameter determination and feature selection of support vector machines[J].Expert Systems with Applications,2008,35(4):1817-1824.
[25] KOHAVI R.A study of cross-validation and bootstrap for accuracy estimation and model selection[C]∥Proceedings of International Joint Conference on Artificial Intelligence.San Francisco:Morgan Kaufmann Publishers,1995:1137-1143.
[26]周志华.机器学习[M].北京:清华大学出版社,2016:28-37.
[27] STEHMAN S V.Selecting and interpreting measures of thematic classification accuracy[J].Remote Sensing of Environment,1997,62(1):77-89.
[28] IWAMORI H,YOSHIDA K,NAKAMURA H,et al.Classification of geochemical data based on multivariate statistical analyses:complementary roles of cluster,principal component,and independent component analyses[J].Geochemistry,Geophysics,Geosystems,2017,18(3):994-1012.
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