| 基于高光谱技术的黑土地微量金属元素探测方法及地学意义 |
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| 引用本文： | 王建华,左玲,李志忠,穆华一,周萍,杨佳佳,赵英俊,秦凯.基于高光谱技术的黑土地微量金属元素探测方法及地学意义[J].地质力学学报,2021,27(3):418-429. |
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| 作者姓名： | 王建华 左玲 李志忠 穆华一 周萍 杨佳佳 赵英俊 秦凯 |
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| 作者单位： | 中国科学院空天信息创新研究院, 北京 100101;国际黑土地协会, 辽宁 沈阳 110034;中国地质大学(北京) , 北京 100083;中国地质调查局西安地质调查中心, 陕西 西安 710054;中国地质调查局沈阳地质调查中心, 辽宁 沈阳 110034;核工业北京地质研究院遥感信息与图像分析技术国家级重点实验室, 北京 100029 |
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| 基金项目： | 中国地质调查局地质调查项目（DD20190316）；国际地学计划项目（IGCP-665） |
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| 摘 要： | 在土壤中重金属含量较低的情况下，重金属的高光谱特征响应非常微弱，不易构建精确的高光谱直接反演模型。为了解决上述问题，依据土壤化学变量间的理化性质，将重金属富集特征转移到与之相关的化学主量元素上，使重金属微弱的信息得以间接定量反演。文中以海伦市黑土土壤为研究对象，通过主成分分析、聚类分析确定了主量元素氧化铁（Fe2O3）与微量重金属As、Zn、Cd之间存在明显吸附赋存关系。选用偏最小二乘法构建了研究区氧化铁含量的最佳反演模型（决定系数为0.704，均方根误差为0.148，F检验为12.732），并利用氧化铁与As、Zn、Cd之间的赋存关系，通过神经网络构建了氧化铁预测值与重金属真实值间的非线性拟合模型，得出As含量的拟合程度最高，Zn的拟合程度较好，Cd的拟合效果较理想，总体相关性分别为0.796、0.732、0.530。研究结果表明，基于氧化铁含量的间接预测模型能对微量重金属As、Zn、Cd进行较好的定量预测，为微量重金属含量的定量分析提供了新的方法参考，为高光谱遥感技术预测土壤重金属含量提供了依据，增强了土壤微量重金属反演可行性，对细化自然资源质量监测、深化开展地学系统综合分析与评价有重要意义。
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| 关 键 词： | 重金属 土壤 赋存关系 高光谱 地球化学 非线性拟合模型 间接预测 神经网络 |
| 收稿时间： | 2021/2/9 0:00:00 |
| 修稿时间： | 2021/5/10 0:00:00 |
A detection method of trace metal elements in black soil based on hyperspectral technology: Geological implications |
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| WANG Jianhu,ZUO Ling,LI Zhizhong,MU Huayi,ZHOU Ping,YANG Jiaji,ZHAO Yingjun,QIN Kai.A detection method of trace metal elements in black soil based on hyperspectral technology: Geological implications[J].Journal of Geomechanics,2021,27(3):418-429. |
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| Authors: | WANG Jianhu ZUO Ling LI Zhizhong MU Huayi ZHOU Ping YANG Jiaji ZHAO Yingjun QIN Kai |
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| Institution: | Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China;International Black Soil Society, Shenyang 110034, Liaoning, China;China University of Geosciences (Beijing), Beijing 100083, China;Xi''an Center of China Geological Survey, Xi''an 710054, Shannxi, China;Shenyang Center of China Geological Survey, Shenyang 110034, Lianing, China;National Key Laboratory of Remote Sensing Information and Imagery Analyzing Technology, Beijing Research Institute of Uranium Geology, Beijing 100029, China |
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| Abstract: | In the case of low content of heavy metals in soil, the hyperspectral characteristic response of heavy metals is very weak, so it is difficult to construct an accurate direct hyperspectral inversion model. In order to solve the above problems, according to the physical and chemical properties of soil chemical variables, the enrichment characteristics of heavy metals are transferred to the related major chemical elements, so that the weak information of heavy metals can be indirectly quantitatively inverted. In this paper, the black soil in Hailun was taken as the research object. Through principal component analysis and cluster analysis, it was confirmed that there was an obvious adsorption occurrence relationship between the major element iron oxide (Fe2O3) and trace heavy metals As, Zn, Cd. The best inversion model of iron oxide content in the study area was established by partial least square method (the determination coefficient is 0.704, the root mean square difference is 0.148, and the F-test is 12.732). Based on the occurrence relationship between iron oxide and As, Zn, CD, a nonlinear fitting model between the predicted value of iron oxide and the real value of heavy metals was constructed by neural network. The fitting results show that the fitting degree of As, Zn and Cd is As>Zn>Cd. The overall correlations are 0.796, 0.732, 0.530 respectively. The study results show that the indirect prediction model based on iron oxide content can better quantitatively predict As, Zn and Cd, which provides a new method for the quantitative analysis of trace heavy metal content. This model provides a basis for hyperspectral remote sensing technology to predict soil heavy metal content, enhances the feasibility of soil trace heavy metal inversion, and is helpful to refine the quality monitoring of natural resource. It is of great significance to deepen the comprehensive analysis and evaluation of geoscience system. |
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| Keywords: | heavy metal soil occurrence relationship hyperspectral geochemistry nonlinear fitting model indirect prediction neural networks |
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