高斯混合模型在水系沉积物地球化学异常圈定中的应用: 以湖南省溆浦地区为例

刘旭洋; 赵玉岩

doi:10.19509/j.cnki.dzkq.tb20220423

高斯混合模型在水系沉积物地球化学异常圈定中的应用: 以湖南省溆浦地区为例

doi: 10.19509/j.cnki.dzkq.tb20220423

刘旭洋^{1, 2, ,},
赵玉岩²

1.
中国地质大学(武汉)资源学院, 武汉 430074
2.
吉林大学地球探测科学与技术学院, 长春 130026

基金项目:

湖南省国土资源信息中心地球化学找矿信息挖掘服务项目湘财采计[2014D]0326号

详细信息

通讯作者:
刘旭洋, E-mail: 156473059@qq.com

中图分类号: P632;P628
计量
- 文章访问数: 153
- PDF下载量: 25
- 被引次数: 0
出版历程
- 收稿日期: 2022-08-02
- 录用日期: 2022-10-10
- 修回日期: 2022-09-12

Gaussian mixture model in geochemical anomaly delineation of stream sediments: A case study of Xupu, Hunan Province

LIU Xuyang^{1, 2
, ,},
ZHAO Yuyan²

1.
School of Earth Resources, China University of Geosciences(Wuhan), Wuhan 430074 China
2.
College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China

More Information

Corresponding author: LIU Xuyang, E-mail: 156473059@qq.com

摘要

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。
- 水系沉积物 /
- 岩性背景 /
- 化探异常 /
- 因子分析 /
- 高斯混合模型 /
- 湖南溆浦
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.
- stream sediments /
- lithologic background /
- geochemical exploration anomaly /
- factor analysis /
- Gaussian mixture model /
- Xupu, Hunan Province
注释:

所有作者声明不存在利益冲突。

HTML全文

图 1 数据处理流程图

Figure 1. Data processing flow chart

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图 2 研究区地质简图

Figure 2. Simplified geological map of the study area

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图 3 Au, Sb, W元素质量分数直方图

Figure 3. Histograms of Au, Sb, and W contents

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图 4 岩性分类数目BIC曲线

Figure 4. BIC curve of classification number

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图 5 分类结果投图

Ⅰ.寒武系板岩；Ⅱ.侏罗系和白垩系的砂岩；Ⅲ.上元古界和震旦系的变质砂岩和板岩；Ⅳ.燕山期花岗岩；Ⅴ.泥盆系、石炭系、二叠系和三叠系的碳酸盐岩；Ⅵ.加里东期花岗岩；Ⅶ.奥陶系和志留系的砂岩和板岩；下同

Figure 5. Diagram of the classification result projection

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图 6 原始数据和各分类中Li(a)和K₂O(b)的质量分数直方图

Figure 6. Histograms of Li (a) and K₂O (b) contentsin various classes and raw data

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图 7 原始数据和GMM方法校正后的成矿元素质量分数直方图

Figure 7. Histograms of raw data and GMM-calibrated metallogenic element contents

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图 8 原始数据(a, c, e)和GMM方法校正后的(b, d, f)Au，Sb，W元素异常图(蓝点代表相应矿点)

Figure 8. Raw data (a, c, e) and GMM-calibrated (b, d, f) Au, Sb, W anomaly map(the bule points represent the corresponding mines)

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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

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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	正态分布		均值	标准差	偏度	峰度	正态分布
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

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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

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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

下载: 导出CSV

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