Multiple-map step effect and optimization of various experimental correction methods based on geochemical data
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摘要:
在处理整图幅地球化学数据、编制大范围地球化学图件时, 需要多图幅数据拼接成图, 不同图幅交界处易出现台阶效应。目前与系统误差相关的文献多为单一方法校正评价某一地区化探数据台阶效应, 很少进行多种方法的比较优化。以青海省沟里地区园以幅、巴加别里赤尔幅、沟里乡幅和智益幅为例, 使用分幅标准化法、归一化法和衬度法对4图幅范围的1∶5万水系沉积物地球化学数据进行了系统误差校正和方法优选, 为岩屑介质化探数据系统误差处理提供有效的方法参考。结果表明, 这3种方法对于处理系统误差均有较为明显的效果, 校正后相邻图幅间的背景差异基本消除, 台阶效应有效弱化, 地球化学区域分带连续, 强异常均被适当压抑, 弱异常被适当增强而从背景中突显。但同一元素用不同方法校正后异常面积、异常形态、与矿床(点)吻合度等存在差异。分幅标准化法和归一化法校正后异常与已知矿床(点)的吻合率均为75%, 异常均匀分布于全区, 但个别矿致异常被弱化, 且无法避免无效异常的影响。衬度法校正效果良好, 能有效地压抑高背景区非矿异常并强化低背景区矿致异常, 异常与矿床(点)吻合度为87.5%, 无效异常的干扰较小, 与地质背景契合度最高。结合研究区实体地质体条件的研究表明, 衬度法更适合沟里地区不同图幅水系沉积物地球化学测量数据系统误差的校正, 可在西北地区类似地形、地貌条件的化探数据处理工作中选择应用。
Abstract:It is necessary to plat the multiple-map sheet data sets into a single map when processing the geochemical data from the entire map sheet and compiling a large-scale geochemical map, but step effects might readily arise where different map sheets converge. There are currently few papers that compare and optimize various methods, and most systematic error-related studies focus on a specific way to correct and assess the step effect of geochemical data in a particular area. With the map sheet from the Yuanyi, Bajiabielichier, Goulixiang, and Zhiyi in Gouli region of Qinghai Province as a case study, this paper applies the framing standardization method, normalized method, and contrast method to perform systematic error correction and method optimization on 1∶50 000 stream sediment geochemical data and provides the effective method reference for the systematic error processing of the geochemical prospecting data of the rock cuttings. The results indicate that these three methods have distinct effects on systematic error correction.After the original data are corrected, the background difference between adjacent map sheets is basically eliminated, the step effect is effectively weakened, the geochemical zoning is continuous, the strong anomalies are appropriately suppressed, and the weak anomalies are appropriately enhanced and represented in the background. After the same element is corrected with various methods, there are differences in the anomalous area, form of anomalies, and goodness of fit with the mineral deposit (point). The goodness of fit between anomalies and known deposits (points) after correction with the framing standardization method and normalized method is 75%; the anomalies are evenly distributed throughout the region, but individual ore-induced anomalies are weakened, and the influence of invalid anomalies cannot be avoided. The nonmineral anomaly in the high background area can be effectively suppressed by the contrast method, and the mineral-induced anomaly in the low background area can be strengthened. The anomaly's goodness of fit with the deposit (point) is 87.5%, the invalid anomaly's influence is minimal, and the geological background has the maximum goodness of fit. Combined with the study on the physical geological body conditions in the region of interest, the contrast method is more suitable for the correction of systematic errors in stream sediment geochemical survey data on different map sheets in the Gouli Region and can be selected and applied in geochemical data processing under similar topographic and geomorphic conditions in Northwest China.
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Key words:
- step effect /
- systematic error /
- framing standardization method /
- normalized method /
- contrast method
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图 1 东昆仑地区区域地质简图及研究区位置(据文献[18]修改)
Figure 1. Generalized geologic map of East Kunlun region and location of the study area
图 2 研究区地质图
1.第四系冲洪积物; 2.第四系残坡积物; 3.鄂拉山组; 4.洪水川组; 5.大干沟组; 6.浩特洛洼组; 7.纳赤台群; 8.万宝沟岩群; 9.小庙组; 10.金水口岩群; 11.中印支期正长花岗岩; 12.中印支期花岗闪长岩; 13.中印支期二长花岗岩; 14.晚华力西期二长花岗岩; 15.晚华力西期石英闪长岩; 16.晚加里东期花岗闪长岩; 17.中加里东期花岗闪长岩; 18.早加里东期英云闪长岩; 19.大型金矿; 20.小型金矿; 21.金矿点; 22.铜矿点; 23.多金属矿点; 24.铜铁矿点; 25.铁矿点; 26.逆断层; 27.断层; 28.性质不明断层; 29.地质界线
Figure 2. Geological map of the study area
图 3 Au、Ag元素原始含量对数直方图
Figure 3. Logarithmic histograms of the original content of Au and Ag
图 4 Au、Ag元素剔值后含量对数直方图
Figure 4. Logarithmic histograms of the content of Au and Ag after elimination of value
图 5 校正前Au元素等值线图
a.原始数据集编制的Au等值线图；b.剔值后数据集编制的Au等值线图；1.阿斯哈金矿床; 2.叶陇沟金矿点; 3.按纳格金矿床; 4.尕之麻金矿点; 5.果龙洼金矿床; 6.园以金矿点; 7.德龙金矿床; 8.达里吉格塘金矿点
Figure 5. Contour maps of the Au element before correction
图 6 校正前Ag元素等值线图
a.原始数据集编制的Ag等值线图；b.剔值后数据集编制的Ag等值线图
Figure 6. Contour maps of the Ag element before correction
图 7 Au元素原始异常图
a.迭代替换后数据集编制的Au异常图; b.剔值后数据集编制的Au异常图；矿床(点)名称1~8同图 5
Figure 7. Original anomaly maps of the Au element
图 8 Ag元素原始异常图
a.迭代替换后数据集编制的Ag异常图; b.剔值后数据集编制的Ag异常图
Figure 8. Original anomaly maps of the Ag element
图 9 Au元素校正等值线图对比
a.误差校正前；b.分幅标准化法校正后；c.归一化法校正后；d.衬度法校正后；矿床(点)1~8同图 5
Figure 9. Comparison of contour maps of Au after correction
图 10 Ag元素校正等值线图对比
a.误差校正前；b.分幅标准化法校正后；c.归一化法校正后Ag；d.衬度法校正后
Figure 10. Comparison of contour maps of Ag after correction
图 11 Au元素校正异常图对比
a.误差校正前；b.分幅标准化法校正后；c.归一化法校正后；d.衬度法校正后；矿床(点)名称1~8同图 5
Figure 11. Comparison of anomaly maps of Au after correction
图 12 Ag元素校正异常图对比
a.误差校正前；b.分幅标准化法校正后；c.归一化法校正后；d.衬度法校正后
Figure 12. Comparison of anomaly maps of Ag after correction
表 1 研究区原始数据统计特征
Table 1. Statistical characteristics of the original data of the study area
wB/10-9 项目 N 极大值 极小值 平均值 标准差 方差 偏度 峰度 Au 8 950 249.8 0.5 2.05 4.17 17.42 35.22 1 790.38 Ag 8 950 5 000.0 11.0 63.80 101.39 10 280.45 23.56 858.44 
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表 2 研究区处理后数据统计特征
Table 2. Statistical characteristics of processed data of the study area
wB/10-9 方法 项目 N 极大值 极小值 平均值 标准差 方差 偏度 峰度 剔值法 Au 8 454 4.4 0.5 1.62 0.93 0.86 1.04 0.31 Ag 8 283 117.0 11.0 50.20 22.32 497.98 0.74 0.23 迭代替换法 Au 8 950 5.5 0.5 1.82 1.23 1.51 1.39 1.39 Ag 8 950 11.0 157.5 57.37 33.37 1 113.55 1.44 1.83
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表 3 分析室数据统计特征
Table 3. Statistical characteristics of the data of the analysis rooms
wB/10-9 图幅 项目 N 极小值 极大值 平均值 标准差 方差 偏度 峰度 圆以幅 Au 2 287 0.5 7.1 2.37 1.58 2.51 1.28 1.30 Ag 2 287 12.0 101.0 41.95 19.74 389.65 0.92 0.82 巴加别里赤尔幅 Au 2 385 0.5 3.8 1.39 0.82 0.68 1.45 1.60 Ag 2 385 16.0 187.0 70.11 39.13 1 530.87 1.37 1.51 沟里乡幅 Au 2 124 0.5 5.4 1.85 1.19 1.43 1.25 1.09 Ag 2 124 11.0 94.9 43.15 17.26 297.97 0.42 0.42 智益幅 Au 2 154 0.5 5.0 1.71 1.11 1.23 1.50 1.64 Ag 2 154 24.0 218.0 77.11 47.08 2216.24 1.54 1.86
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表 4 标准化转换后视含量统计特征
Table 4. Statistical characteristics of visual content after standardized conversion
项目 N 平均值 标准差 方差 偏度 峰度 Au 8 950 0 1 1 1.37 1.41 Ag 8 950 0 1 1 1.07 1.16
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表 5 归一化转换后视含量数据统计特征
Table 5. Statistical characteristics of visual content data after the normalization transformation
wB/10-9 项目 N 极大值 平均值 标准差 方差 偏度 峰度 Au 8 950 1 0.28 0.24 0.06 1.37 1.37 Ag 8 950 1 0.33 0.23 0.05 1.04 0.88
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表 6 台阶A、B迭代替换后数据统计特征
Table 6. Statistical characteristics of data after iterative replacement of step A and B
wB/10-9 项目 N 极小值 极大值 平均值 标准差 方差 偏度 峰度 台阶A Au 4 412 0.5 6.3 2.12 1.41 1.99 1.27 1.20 Ag 4 412 11.0 98.1 42.51 18.54 43.65 0.69 0.61 台阶B Au 4 539 0.5 4.4 1.54 0.97 0.94 1.50 1.69 Ag 4 539 11.0 201.0 73.23 42.72 1824.64 1.46 1.68
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表 7 衬度变换后数据统计特征
Table 7. Statistical characteristics of the data after contrast transformation
wB/10-9 项目 N 极小值 极大值 平均值 标准差 方差 偏度 峰度 Au 8 950 0.5 5.41 1.82 1.18 1.39 1.38 1.43 Ag 8 950 11.0 159.37 58.12 29.97 898.27 1.27 1.78
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表 8 不同校正方法圈定异常对比
Table 8. Comparison of anomalies determined with different correction methods
元素 方法 异常个数/个 异常面积/km2 异常面积占研究区面积比例/% 具三级浓度分带异常数量/个 具三级浓度分带异常占总异常比例/% 矿床(点)总数/个 落入异常的矿床(点)数量/个 落入异常的矿床(点)比例/% Au 校正前迭代替换数据集 38 60.36 3.55 25 66 8 6 75 校正前剔值数据集 37 43.62 2.57 31 84 8 4 50 分幅标准化法 43 51.13 3.01 26 60 8 6 75 归一化法 46 54.07 3.18 28 61 8 6 75 衬度法 43 60.28 3.55 26 60 8 7 87.5 Ag 校正前迭代替换数据集 44 82.39 4.85 16 36 校正前剔值数据集 44 97.44 5.73 33 75 分幅标准化法 39 64.14 3.77 34 87 归一化法 40 65.58 3.86 32 80 衬度法 46 76.29 4.49 34 74
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