| 利用样本排序方法比较化探异常识别模型的效果 |
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| 引用本文： | 陈志军,成秋明,陈建国.利用样本排序方法比较化探异常识别模型的效果[J].地球科学,2009,34(2):353-364. |
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| 作者姓名： | 陈志军 成秋明 陈建国 |
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| 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, 湖北武汉 430074 |
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| 基金项目： | 国家自然科学基金，国家高技术研究发展计划(863计划)，教育部创新团队项目，国际数学地球科学学生奖励基金，GPMR国家重点实验室开放课题，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 |
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| 摘 要： | 地球化学异常的有效识别是化探找矿成败的关键环节.利用样本排序方法对各种化探异常识别模型的处理效果和优劣性进行了比较.以云南个旧及其周边地区铜元素水系沉积物为例, 应用元素含量、异常衬度、局部奇异性分析三大类方法对数据进行了处理, 对化探异常指示变量的排序值进行了3个方面的对比: (1) 在高背景区和低背景区样品的排序特征; (2) 有矿出现位置处样品的样本排序特征; (3) 累积面积(等效于上侧样本排序值) 不同分组所圈定的异常范围和矿床(点) 之间的空间相关性.结果表明, 局部奇异性分析方法较按含量高低的全局性方法对弱缓异常识别更为有效, 也相对优于滑动衬值.按奇异性指数基于证据权法圈定的异常远景区更具预测意义, 显著优于用元素含量值所圈定的异常范围.局部奇异性方法原理清晰、方法简便、可操作性强, 在地球化学异常识别中可以用其替代滑动衬值方法.
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| 关 键 词： | 局部奇异性指数 滑动衬值 样本排序 数据探查分析 证据权方法 个旧地区 |
| 收稿时间： | 2008-12-26 |
Comparison of Different Models for Anomaly Recognition of Geochemical Data by Using Sample Ranking Method |
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| CHEN Zhi-jun,CHENG Qiu-ming,CHEN Jian-guo,.State Key Laboratory of Geological Processes , Mineral Resources,China University of Geosciences,Wuhan,China.Faculty of Earth Resources,China.Comparison of Different Models for Anomaly Recognition of Geochemical Data by Using Sample Ranking Method[J].Earth Science-Journal of China University of Geosciences,2009,34(2):353-364. |
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| Authors: | CHEN Zhi-jun CHENG Qiu-ming CHEN Jian-guo State Key Laboratory of Geological Processes Mineral Resources China University of Geosciences Wuhan ChinaFaculty of Earth Resources China |
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| Institution: | CHEN Zhi-jun1,2,CHENG Qiu-ming1,3,CHEN Jian-guo1,21.State Key Laboratory of Geological Processes , Mineral Resources,China University of Geosciences,Wuhan430074,China2.Faculty of Earth Resources,China3.Department of Earth , Space Science , Engineering,York University,TorontoM3J1P3,Canada |
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| Abstract: | The geochemical anomaly recognition is the key to geochemical prospecting.Many new models are brought forward to identify and extract the geochemical weak anomalies from the complex background.How to compare the differences in aspect of the anomaly recognition effect of these different models? The authors advance the sample ranking method to discuss this problem.The gliding anomaly contrast and the local singularity analysis are applied to the Cu element data of the stream sediment samples from Gejiu area, Yunnan Province, China.The ranks for the raw data, contrast value and the local singularity exponents, denoted by RANK (Raw), RANK (CV) and RANK (Δα) respectively, are calculated by ordering the samples from the high anomaly to the low anomaly.Three ways are employed to compare the RANK (CV) and RANK (Δα) with the RANK (Raw) : (1) the characteristics of ranks for samples with the strong background and the weak background; (2) the characteristics of ranks for samples where the Cu deposits occur; and (3) the spatial correlation between the locations of Cu deposits and the cumulative area with the same thresholds of the upper rank values.The results demonstrate that the local singularity analysis is a useful model for the weak geochemical anomaly recognition, whose effect corresponds with the gliding anomaly contrast model or even better.The prospective areas delineated by means of weights of evidence method on the basis of local singularity exponents can provide new information and may be significant for the prediction of the undiscovered mineral deposits, which is significantly superior to the results on the basis of raw concentration data.The local singularity analysis has the advantage of the perspicuous principle, convenience and effective performance, and we can substitute it for the gliding contrast value method for the anomaly recognition of geochemical data. |
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