基于大数据的深部找矿靶区定量成矿预测——以大桥地区金矿为例

doi:10.11872/j.issn.1005-2518.2021.06.114

Abstract

Abstract:

Conventional geochemical sampling medium does not contain deep deposit information，and the remote sensing information is only the characteristics of surface images，which is difficult to identify deep deposit information.Geophysical information can well reflect the information of deep deposit and it is the best information choice for deep metallogenic prediction.However，the interpretation of geophysical information are multi-resolution，which has always seriously affected the accuracy and accuracy of deep prospecting target prediction.Big data is triggering a profound revolution in the field of Geoscience.New methods and technologies such as big data and artificial intelligence represented by statistical analysis methods and machine learning algorithms have been gradually applied to metallogenic prediction and achieved good prediction results.The Western Qinling area of Gansu Province is an important polymetallic metallogenic accumulation area in China，which has accumulated rich geological data.It is of great significance to carry out quantitative prediction of gold prospecting target by deep mining geological data with big data method for gold exploration and expansion of gold reserves in Western Qinling area of Gansu Province.In order to eliminate the multi solution of geophysical information and improve the prediction accuracy of deep prospecting target，big data ideas and methods were applied to deep mining of aeromagnetic data from Daqiao area in west Qinling of Gansu Province，and established the aeromagnetic database，the aeromagnetic information research unit database and the known ore unit database respectively.Through the discriminant analysis of aeromagnetic database and known ore unit database，a series of quantitative prediction models of prospecting target were constructed，and deep prospecting targets were delineated，combined with geological and mineral information to optimize the grade prospecting targets.A total of 31 gold prospecting targets have been delineated in the study area，including 6 Class Ⅰ targets （seeing ore rate 16.9%），10 Class Ⅱ ore targets （seeing mine rate 31.32%） and 15 Class Ⅲ targets （seeing ore rate 20%）.Gold industrial ore bodies have been found in the prospecting target in the overburden area.The cumulative area of the target accounts for 2.4% of the area of the study area，which greatly reduces the scope of prospecting.The study believes that the series of quantitative prediction models for prospecting targets established based on aeromagnetic information have high accuracy in determining prospecting targets for Daqiao-type gold deposits in the study area.It provides a new idea and method for the metallogenic prediction of deep and concealed deposit.

Key words: aeromagnetic information, big data, quantitative prediction model, deep prospecting target, Daqiao gold deposit, Western Qinling

CLC Number:

P618.51

Huaitao WANG,Xiaowei WANG,Yunzhi LUO,Bingtian SONG,Jianmin LUO,Lei XU. Quantitative Metallogenic Prediction of Deep Prospecting Target Based on Big Data：Taking Gold Deposit in Daqiao Area as an Example[J].Gold Science and Technology, 2021, 29(6): 771-780.

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模型分类	参数	变化系数	贡献	累计贡献	有效性检验
模型1.1	（常量）	-1.437			R₀=1.62 F_0.01=2.32 F_p=30.18 有矿正判率：98.8% 无矿正判率：92%
	ΔT1	1.68	26.37	26.37
	ΔT2	0.02	22.25	48.61
	ΔT3	-0.01	17.86	66.47
	ΔT4	1.19	15.7	82.17
	ΔT5	1.12	14.63	96.8
	ΔT6	-1.63	1.58	98.38
	ΔT7	-0.01	0.93	99.31
	ΔT8	1.35	0.21	99.91
	ΔT9	-0.78	0.09	100
模型1.2	（常量）	-10.659			R₀=0.66 F_0.01=3.02 F_p=-14.54 有矿正判率：95.5% 无矿正判率：94.2%
	ΔT9	10.49	57.81	57.81
	ΔT10	-5.47	19.56	77.36
	ΔT11	5.34	17.04	94.4
	ΔT12	0	2.88	97.28
	ΔT6	1.58	2.72	100
模型2.1	（常量）	21.295
	ΔT13	305.17	44.71	44.71
	ΔT14	-178.95	26.2	70.91
	ΔT15	-57.81	7.8	78.71	R₀=0.70 F_0.01=2.32 F_p=-11.58 有矿正判率：97.1% 无矿正判率：80.5%
	ΔT16	0.01	7.49	86.21
	ΔT17	-47.62	6.28	92.48
	ΔT18	-1.46	4.13	96.61
	ΔT19	-24.35	1.2	97.81
	ΔT4	0.81	1.1	98.91
	ΔT20	24.58	0.71	99.62
	ΔT21	3.68	0.38	100
模型2.2	（常量）	-4.179			R₀=0.69 F_0.01=1.47 F_p=0.93 有矿正判率：95% 无矿正判率：76.6%
	ΔT12	-0.05	31.37	31.37
	ΔT3	0.04	26.6	57.98
	ΔT7	0.14	4.83	62.81
	ΔT11	56.23	4.49	67.3
	ΔT22	-0.13	3.17	70.47
	ΔT18	-0.67	2.38	72.85
	ΔT23	5 116.34	2.35	75.2
	ΔT24	-42.07	1.89	77.09
	ΔT25	-57.96	1.69	78.78
	ΔT26	-0.04	1.53	80.31

预测单元分级	预测有矿单元/个	预测有矿单元占比/%	有矿单元/个	有矿单元占比/%	有矿单元 /有矿单元总数	较有矿单元平均值提高倍数
单元总数/个	48 157		有矿单元总数/个		106
Ⅰ级单元	382	0.79	49	12.83	0.46	58.34
Ⅱ级单元	618	1.28	15	2.43	0.14	11.04
Ⅲ级单元	999	2.08	28	2.80	0.26	12.73
预测总数	1 999	4.15	92	4.60	0.86	20.92

预测单元分级	预测有矿单元/个	预测有矿单元占比/%	有矿单元/个	有矿单元占比/%	有矿单元 /有矿单元总数	较有矿单位平均值提高倍数
单元总数/个	48 157		有矿单元总数/个		106
Ⅰ级单元	247	0.51	11	4.45	0.10	20.23
Ⅱ级单元	400	0.83	1	0.25	0.09	1.14
Ⅲ级单元	647	1.34	9	1.39	0.85	6.32
预测总数	1 294	2.69	21	1.62	0.19	7.38

预测单元分级	预测有矿单元/个	预测有矿单元占比/%	有矿单元/个	有矿单元占比/%	有矿单元 /有矿单元总数	较有矿单元平均值提高倍数
单元总数/个	48 157		有矿单元总数/个		106
Ⅰ级单元	197	0.41	34	17.22	0.32	78.26
Ⅱ级单元	320	0.66	23	7.20	0.22	32.72
Ⅲ级单元	517	1.07	11	2.13	0.10	9.67
预测总数	1 034	2.15	68	6.58	0.64	29.89

预测单元分级	预测有矿单元/个	预测有矿单元占比/%	有矿单元/个	有矿单元占比/%	有矿单元 /有矿单元总数	较有矿单元平均值提高倍数
单元总数/个	48 157		有矿单元总数/个		106
Ⅰ级单元	186	0.39	13	6.99	12.26	31.77
Ⅱ级单元	301	0.63	13	4.32	12.26	19.63
Ⅲ级单元	487	1.01	12	2.46	11.32	11.20
预测总数	974	2.02	38	3.90	35.85	17.73

Quantitative Metallogenic Prediction of Deep Prospecting Target Based on Big Data：Taking Gold Deposit in Daqiao Area as an Example

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靶区编码	靶区面积	靶区分级	矿床点	矿床规模	判别值
Ⅰ-1	9.47	Ⅰ			22.04
Ⅰ-2	30.75	Ⅰ	西和县大桥金矿床	超大型矿床	19.54
Ⅰ-3	1.97	Ⅰ			19.38
Ⅰ-4	1.31	Ⅰ			16.61
Ⅰ-5	8.01	Ⅰ			16.24
Ⅰ-6	4.80	Ⅰ			16.21
Ⅱ-7	11.98	Ⅱ	小金厂C8金矿化点	矿化点	16.1
Ⅱ-8	8.43	Ⅱ	联合村金矿化点	矿化点	16.04
Ⅱ-9	3.90	Ⅱ			15.47
Ⅱ-10	14.66	Ⅱ			15.35
Ⅱ-11	6.60	Ⅱ	西和县白崖沟金矿	小型矿床	14.98
Ⅱ-12	3.54	Ⅱ			14.75
Ⅱ-13	5.43	Ⅱ			14.64
Ⅱ-14	19.73	Ⅱ			13.87
Ⅱ-15	7.67	Ⅱ			13.73
Ⅱ-16	3.66	Ⅱ			13.6
Ⅲ-17	9.25	Ⅲ			12.58
Ⅲ-18	6.72	Ⅲ			12.36
Ⅲ-19	1.15	Ⅲ	西和县红山金矿化点	矿化点	11.88
Ⅲ-20	2.87	Ⅲ	大坪金矿化点	矿化点	11.81
Ⅲ-21	1.89	Ⅲ			11.49
Ⅲ-22	9.48	Ⅲ			11.44
Ⅲ-23	13.36	Ⅲ			11.14
Ⅲ-24	1.80	Ⅲ			11.08
Ⅲ-25	6.14	Ⅲ			10.74
Ⅲ-26	0.90	Ⅲ			10.44
Ⅲ-27	4.76	Ⅲ			9.87
Ⅲ-28	0.71	Ⅲ			9.73
Ⅲ-29	7.00	Ⅲ			8.51
Ⅲ-30	4.66	Ⅲ			7.51
Ⅲ-31	0.56	Ⅲ	安子山金矿点	矿点	7.49

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