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Gold Science and Technology >

2021 , Vol. 29 >Issue 6: 771 - 780

DOI: https://doi.org/10.11872/j.issn.1005-2518.2021.06.114

Mineral Exploration and Resource Evaluation

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

  • Huaitao WANG , 1, 2, 3 ,
  • Xiaowei WANG 1, 2, 3 ,
  • Yunzhi LUO 1, 2, 3 ,
  • Bingtian SONG 1, 2, 3 ,
  • Jianmin LUO 1, 2, 3 ,
  • Lei XU 1
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  • 1. Geology Survey of Gansu Province, Lanzhou 730000, Gansu, China
  • 2. Geoscience Big Data Exploration Engineering Technology Innovation Center of Gansu Provincial Bureau of Geo-logy and Mineral Exploration and Development, Lanzhou 730000, Gansu, China
  • 3. Geoscience Big Data Engineering Research Center of Gansu Province, Lanzhou 730000, Gansu, China

Received date: 2021-08-14

  Revised date: 2021-10-11

  Online published: 2022-03-07

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Highlights

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.

Cite this article

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 . DOI: 10.11872/j.issn.1005-2518.2021.06.114

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http://www.goldsci.ac.cn/article/2021/1005-2518/1005-2518-2021-29-6-771.shtml

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