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| 证据权模型中两种预测单元划分方式对比 | |
| 引用本文： | 张道军,成秋明,左仁广.证据权模型中两种预测单元划分方式对比[J].吉林大学学报(地球科学版),2013,43(3):1040-1052. |
| 作者姓名： | 张道军 成秋明 左仁广 |
| 作者单位： | 1.中国地质大学地质过程与矿产资源国家重点实验室，武汉430074; 2.中国地质大学资源学院，武汉430074; 3.中国地质大学地质过程与矿产资源国家重点实验室，北京100083 |
| 基金项目： | 中国地质调查局计划项目，国家自然科学基金项目，中央高校基本科研业务费专项资金项目，国土资源部公益性行业科研专项项目，地质过程与矿产资源国家重点实验室科技部专项经费资助项目 |
| 摘 要： | 证据权模型作为一种数据综合方法已被广泛应用于矿产资源定量预测与评价。在模糊证据权基础上，发展了基于地质单元思想的矢量证据图层构建和数据综合方法，并通过实例作具体阐述：它以矿点缓冲区图层作为训练图层，以各证据变量图层在空间上的叠置所形成的唯一地质单元作为评价对象，统一计算各个证据变量的证据权重，进而基于地质单元进行证据综合和后验概率成图。与基于栅格（或规则格网）的模型不同，基于矢量证据权模型以具有明确地质内涵的地质单元（而非规则网格单元）为预测单元，易于解释，并且消除了边界误差；相比基于规则格网划分所得到的成矿单元，以矿床（点）缓冲区作为训练对象，提高了已知矿点的代表性。实例表明：若预测单元大小为初始栅格大小整数倍，各缓冲等级平均面积计算误差为0.26%，否则面积平均误差达到6%；即使在预测单元大小为初始栅格大小整数倍情况下，矿点平均计算误差也达到4.78%。因此，基于地质单元思想的证据权预测单元划分方法在精度上优于基于栅格或规则格网方法。 |
| 关 键 词： | 证据权模型 单元划分 矢量数据 栅格数据 数据综合 资源评价 |
| 收稿时间： | 2012-06-11 |
A Comparison of Two Different Unit Division Methods in Weights of Evidence |
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| Zhang Daojun,Cheng Qiuming,Zuo Renguang.A Comparison of Two Different Unit Division Methods in Weights of Evidence[J].Journal of Jilin Unviersity:Earth Science Edition,2013,43(3):1040-1052. | |
| Authors: | Zhang Daojun Cheng Qiuming Zuo Renguang |
| Institution: | 1.State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan430074, China; 2.Faculty of Earth Resources, China University of Geosciences, Wuhan430074, China; 3.State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Beijing100083, China |
| Abstract: | As a data synthesis method, Weights of Evidence (WofE) model has been widely used in mineral resource quantitative assessment. On the basis of fuzzy weights of evidence, a new weight of evidence method is developed, where layer construction and data integration are based on vector data and vector method. The complete process of modeling is then given combined with a case study, in which the buffer of mineral deposits is taken as training layer, the unique geological units are obtained as evaluation objects by spatial overlaying of all evidential variable layers, and weights of each evidence are then calculated, at last probability map and delineated target can be gotten based on geological objects by weights integration of all evidential layers. Different from raster-based WofE, vector-based WofE model keeps the natural boundary of a geological feature used as geological units which have explicit geological meanings, and can improve the accuracy of posterior probability; Instead of the rectangular regions resulting from regular division, circular regions are used to represent known ore occurrences, which improves the known ore occurrences representatio. A case study shows that average area error are 0.26% and 6% when prediction unit size is equal and not equal with an integer multiple of initial grid size respectively; When prediction unit size is of an integer multiple of initial grid size, average calculation error for deposits is 4.78%. It can be concluded that Prediction unit partition method based on the weight of evidence of geologic units thought better accuracy than the method based on raster or regular grid. Thus the obtained posterior probability distribution based on vector prediction unit division method is more reliable than grid-based method. |
| Keywords: | weights of evidence unit division vector data raster data data integration resource valuation |
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