Today’s era of big data is witnessing a gradual increase in the amount of data,more correlations between data,as well as growth in their spatial dimension.Conventional linear statistical models applied to mineral pro...Today’s era of big data is witnessing a gradual increase in the amount of data,more correlations between data,as well as growth in their spatial dimension.Conventional linear statistical models applied to mineral prospectivity mapping(MPM)perform poorly because of the random and nonlinear nature of metallogenic processes.To overcome this performance degradation,deep learning models have been introduced in 3 D MPM.In this study,taking the Huayuan sedimentary Mn deposit in Hunan Province as an example,we construct a 3 D digital model of this deposit based on the prospectivity model of the study area.In this approach,3 D predictor layers are converted from the conceptual model and employed in a 3 D convolutional neural network(3 D CNN).The characteristics of the spatial distribution are extracted by the 3 D CNN.Subsequently,we divide the 22 extracted ore-controlling variables into six groups for contrast experiments based on various combinations and further apply the 3 D CNN model and weight of evidence(WofE)method on each group.The predictive model is trained on the basis of the coupling correlation between the spatial distributions of the variables and the underground occurrence space of the Mn orebodies,and the correlation between different ore-controlling factors.The analysis of 12 factors indicates that the 3 D CNN model performs well in the 3 D MPM,achieving a promising accuracy of up to 100%and a loss value below 0.001.A comparison shows that the 3 D CNN model outperforms the WofE model in terms of predictive evaluation indexes,namely the success rate and ore-controlling rate.In particular,the 1–12 ore-controlling factors selected in experiment 5 provide a significantly better prediction effect than the other factors.Consequently,we conclude that the Mn deposit in the study area is not only related to the stratum and interlaminar anomalous bodies but also to the spatial distribution of the faults.The experimental results confirm that the proposed 3 D CNN is promising for 3 D MPM as it eliminates the inter展开更多
钽矿是我国的紧缺资源,近年来对别也萨麻斯地区钽矿取得了找矿新进展,包括新矿点的发现以及花岗伟晶岩型稀有金属资源的找矿突破。区内伟晶岩脉广泛发育,为探究含矿脉体的成矿时代、查明区内典型铌钽矿物的矿物学特征,本文以L18号伟晶...钽矿是我国的紧缺资源,近年来对别也萨麻斯地区钽矿取得了找矿新进展,包括新矿点的发现以及花岗伟晶岩型稀有金属资源的找矿突破。区内伟晶岩脉广泛发育,为探究含矿脉体的成矿时代、查明区内典型铌钽矿物的矿物学特征,本文以L18号伟晶岩脉中的钽锰矿为研究对象,对其物理性质、化学成分、地质年代等进行了分析。应用电子探针测试钽锰矿的化学组成,热电离质谱法(TIMS)测定其U-Pb年龄,确定含矿脉体的形成年代。结果表明,研究区钽锰矿中Ta 2 O 5含量为51.58%~74.80%,均值68.49%,Nb 2 O 5含量为6.15%~27.63%;部分主量元素分布不均,未表现出规律的分带性,但矿物颗粒中心部位的CaO含量较边部低,横剖面上SiO 2含量相对稳定,TiO 2与WO 3显示不规律波动。这种特征表明钽锰矿并非单纯由结晶分异作用形成,而是可能受到了后期交代作用的影响。钽锰矿的U-Pb年龄为160Ma,说明钽锰矿化发生于晚侏罗世早期,与围岩海西期二云母花岗岩相差甚远,后者并非L18号脉体的成矿母岩。展开更多
基金financially supported by the Chinese MOST project“Methods and Models for Quantitative Prediction of Deep Metallogenic Geological Anomalies”(No.2017YFC0601502)and“Research on key technology of mineral prediction based on geological big data analysis”(No.6142A01190104)。
文摘Today’s era of big data is witnessing a gradual increase in the amount of data,more correlations between data,as well as growth in their spatial dimension.Conventional linear statistical models applied to mineral prospectivity mapping(MPM)perform poorly because of the random and nonlinear nature of metallogenic processes.To overcome this performance degradation,deep learning models have been introduced in 3 D MPM.In this study,taking the Huayuan sedimentary Mn deposit in Hunan Province as an example,we construct a 3 D digital model of this deposit based on the prospectivity model of the study area.In this approach,3 D predictor layers are converted from the conceptual model and employed in a 3 D convolutional neural network(3 D CNN).The characteristics of the spatial distribution are extracted by the 3 D CNN.Subsequently,we divide the 22 extracted ore-controlling variables into six groups for contrast experiments based on various combinations and further apply the 3 D CNN model and weight of evidence(WofE)method on each group.The predictive model is trained on the basis of the coupling correlation between the spatial distributions of the variables and the underground occurrence space of the Mn orebodies,and the correlation between different ore-controlling factors.The analysis of 12 factors indicates that the 3 D CNN model performs well in the 3 D MPM,achieving a promising accuracy of up to 100%and a loss value below 0.001.A comparison shows that the 3 D CNN model outperforms the WofE model in terms of predictive evaluation indexes,namely the success rate and ore-controlling rate.In particular,the 1–12 ore-controlling factors selected in experiment 5 provide a significantly better prediction effect than the other factors.Consequently,we conclude that the Mn deposit in the study area is not only related to the stratum and interlaminar anomalous bodies but also to the spatial distribution of the faults.The experimental results confirm that the proposed 3 D CNN is promising for 3 D MPM as it eliminates the inter
文摘钽矿是我国的紧缺资源,近年来对别也萨麻斯地区钽矿取得了找矿新进展,包括新矿点的发现以及花岗伟晶岩型稀有金属资源的找矿突破。区内伟晶岩脉广泛发育,为探究含矿脉体的成矿时代、查明区内典型铌钽矿物的矿物学特征,本文以L18号伟晶岩脉中的钽锰矿为研究对象,对其物理性质、化学成分、地质年代等进行了分析。应用电子探针测试钽锰矿的化学组成,热电离质谱法(TIMS)测定其U-Pb年龄,确定含矿脉体的形成年代。结果表明,研究区钽锰矿中Ta 2 O 5含量为51.58%~74.80%,均值68.49%,Nb 2 O 5含量为6.15%~27.63%;部分主量元素分布不均,未表现出规律的分带性,但矿物颗粒中心部位的CaO含量较边部低,横剖面上SiO 2含量相对稳定,TiO 2与WO 3显示不规律波动。这种特征表明钽锰矿并非单纯由结晶分异作用形成,而是可能受到了后期交代作用的影响。钽锰矿的U-Pb年龄为160Ma,说明钽锰矿化发生于晚侏罗世早期,与围岩海西期二云母花岗岩相差甚远,后者并非L18号脉体的成矿母岩。
