Gaussian mixture model in geochemical anomaly delineation of stream sediments: A case study of Xupu, Hunan Province
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摘要:
化探数据的正确处理和解译对于区域矿产勘查工作具有重要意义。然而,由于不同类型的岩石具有不同的元素丰度,在处理复杂岩性区化探数据时如果采用统一的异常下限,会导致高背景区被误判为异常区,而部分低弱地球化学异常被忽略。所以复杂岩性区的化探数据需按岩性分类后再划分地球化学背景与异常,从而更准确地圈定化探异常。提出了基于因子得分高斯混合模型的化探异常圈定方法,首先将化探数据做对数比转换后进行因子分析,然后利用因子得分完成高斯混合模型岩性分类,再进行分类标准化处理以消除岩性背景的影响,最后使用处理后的数据圈定化探异常。利用该方法对湖南溆浦地区1:20万水系沉积物化探数据进行研究,结果表明,成矿元素在研究区不同岩性中的含量存在一定差异,若采用统一的异常下限是不合理的;而本研究提出的方法能准确地进行岩性分类、消除不同岩性的背景和强化低弱异常,且异常位置与已知矿点相吻合。因此,高斯混合模型方法可以准确地圈定复杂岩性区的化探异常,并为研究区下一步的矿产勘查工作提供一些参考依据。
Abstract:Objective The correct processing and interpretation of geochemical exploration data are critical for regional mineral exploration. High backgrounds may be misjudged as anomalies or low and weak geochemical anomalies may be ignored, if a unified anomaly threshold is adopted for geochemical exploration data in lithologically complex regions due to different elemental abundances in different lithologies. Therefore, it is essential to identify geochemical backgrounds and anomalies in lithologically complex regions based on lithologic classification.
Methods Here, we propose a method for delineating geochemical anomalies based on a Gaussian mixture model of factor scores. The geochemical exploration data are subjected to factor analysis after a log-ratio transformation, and then the lithologic classification is completed by the Gaussian mixture model with factor scores. Subsequently, the standardization is performed to eliminate the lithologic background, and geochemical exploration anomalies are delineated with the processed data. This method is used to the geochemical exploration data of 1:200 000 stream sediments in Xupu, Hunan Province.
Results The results show that the contents of the metallogenic elements in various lithologies of the study area are partly different, and consequently, it would be unreasonable to adopt a uniform anomaly threshold. In contrast, the method advanced in this paper can accurately classify lithology, eliminate the background of different lithologies, and enhance low and weak anomalies, with the location of the anomalies corresponding to known deposits.
Conclusion Hence, the Gaussian mixture model enables effective delineation of geochemical exploration anomalies in lithologically complex regions and provides certain information for further mineral prospecting in this region.
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表 1 旋转因子矩阵
Table 1. Rotation factor matrix
1 2 3 4 5 6 1 2 3 4 5 6 B 0.015 0.054 -0.06 0.079 -0.01 -0.856 Ti 0.327 0.538 0.303 -0.038 0.563 0.213 Ba 0.053 0.117 0.091 -0.885 -0.145 -0.079 U 0.117 -0.179 -0.239 -0.191 -0.778 -0.065 Be -0.697 -0.173 0.076 0.187 -0.323 -0.121 V 0.734 0.075 0.084 -0.506 -0.195 -0.167 Co 0.372 0.099 0.728 0.009 0.162 0.077 Y 0.156 0.795 0.072 -0.169 0.319 0.015 Cr 0.837 -0.045 0.312 0.055 0.039 -0.099 Zr -0.128 0.209 -0.266 0.302 0.196 0.698 La 0.026 0.642 -0.159 0.203 0.034 0.287 Al2O3 -0.551 0.4 0.202 0.499 0.253 0.099 Li -0.5 -0.075 -0.092 0.683 -0.237 -0.184 CaO 0.177 -0.642 -0.38 0.261 -0.052 0.033 Nb 0.027 0.683 0.221 -0.111 0.152 -0.161 Fe2O3 0.057 0.026 0.877 -0.031 0.183 -0.058 Ni 0.616 -0.316 0.446 -0.192 -0.294 0.056 K2O -0.866 0.028 0.099 0.03 -0.172 -0.031 P 0.437 0.283 0.174 -0.402 -0.1 -0.379 MgO 0.133 -0.545 0.242 -0.146 0.43 0.405 Sr -0.004 -0.255 -0.613 0.123 0.203 0.107 Na2O -0.713 -0.106 -0.319 0.099 0.351 0.107 Th -0.506 -0.035 -0.028 0.418 -0.418 0.281 SiO2 0.068 0.308 -0.365 0.009 0.613 0.105 表 2 原始数据和各分类中Li和K2O的均值、标准差和正态分布检验
Table 2. Mean, standard deviation values, and normal distribution test of Li and K2O in various classes and raw data
w(Li)/10-6 正态分布 w(K2O)/% 正态分布 均值 标准差 偏度 峰度 均值 标准差 偏度 峰度 原始数据 40.25 22.11 2.74 10.09 否 原始数据 2.62 0.96 1.06 1.05 否 Ⅰ 29.98 8.65 0.51 0.00 是 Ⅰ 2.34 0.53 0.47 -0.04 是 Ⅱ 29.38 6.00 0.93 2.03 是 Ⅱ 1.86 0.36 0.31 0.71 是 Ⅲ 30.59 6.79 0.32 0.44 是 Ⅲ 2.35 0.27 0.12 0.40 是 Ⅳ 108.39 31.81 0.52 -0.50 是 Ⅳ 4.72 0.77 -0.03 -0.39 是 Ⅴ 41.95 13.38 1.77 5.67 否 Ⅴ 1.55 0.42 0.07 0.35 是 Ⅵ 58.15 11.88 0.26 0.94 是 Ⅵ 3.88 0.68 -0.10 -0.52 是 Ⅶ 32.92 7.69 0.61 2.13 是 Ⅶ 2.67 0.39 0.01 -0.14 是 表 3 各分类中Au、Sb、W元素的均值和标准差
Table 3. Mean and standard deviation values of Au, Sb, and W in various classes
分类 Ⅰ Ⅱ Ⅲ Ⅳ Ⅴ Ⅵ Ⅶ Ⅰ Ⅱ Ⅲ Ⅳ Ⅴ Ⅵ Ⅶ w(Au) / 10-9 均值 1.12 0.97 1.00 1.04 0.95 1.03 1.28 标准差 0.46 0.19 0.27 0.21 0.21 0.34 0.50 w(Sb) / 10-6 4.33 1.23 1.79 0.77 4.10 1.28 2.45 2.83 0.52 0.80 0.34 2.75 0.85 1.00 w(W) / 10-6 2.11 1.74 1.93 4.78 2.43 3.40 2.21 0.49 0.43 0.50 4.96 0.67 2.62 0.45 表 4 原始数据和各分类中成矿元素异常下限
Table 4. Anomaly thresholds of metallogenic elements in raw data and various classes
原始数据 Ⅰ Ⅱ Ⅲ Ⅳ Ⅴ Ⅵ Ⅶ w(Au)/10-9 1.30 2.04 1.34 1.53 1.46 1.36 1.72 2.29 w(Sb)/10-6 2.52 9.99 2.26 3.39 1.44 9.59 2.97 4.46 w(W)/10-6 2.57 3.09 2.61 2.93 14.70 3.77 8.65 3.11 -
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