Quantitative Metallogenic Prediction of Deep Prospecting Target Based on Big Data：Taking Gold Deposit in Daqiao Area as an Example
Received date: 2021-08-14
Revised date: 2021-10-11
Online published: 2022-03-07
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.
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
| null | Carranza E J M，2009.Controls on mineral deposit occurrence inferred from analysis of their spatital pattern and spatial association with geological features［J］.Ore Geology Reviews，35（3）：383-400. |
| null | Carranza E J M，2011.Geocomputation of mineral exploration targets［J］.Computers & Geosciences，37（12）：1907-1916. |
| null | Frankel F， Reid R，2008.Big data：Distilling meaning from data［J］.Nature，455：30. |
| null | Luo Jianmin， Zhang Qi， Song Bingtian， al et，2017.Application of integrated geophysical and geochemical data processing to metallogenic target zone quantitative prediction and optimization［J］.Bulletin of Mineralogy，Petrology and Geochemistry，36（6）：886-890. |
| null | Luo Jianmin， Wang Xiaowei， Song Bingtian， al et，2018.Discussion on the method for quantitative classification of magmatic rocks：Taking it’s application in West Qinling of Gansu Province for example［J］. Acta Petrologica Sinica，34（2）：326-332. |
| null | Luo Jianmin， Zhang Qi，2019a.Big data pioneers new ways of geoscience research：Identifying relecant relationships to enhance research feasibility［J］.Earth Science Frontiers，26（4）：6-12. |
| null | Luo Jianmin， Wang Xiaowei， Zhang Qi， al et，2019b.Application of geological big data to quantitative target area optimization for regional mineral prospeting in China［J］.Earth Science Frontiers，26（4）：76-83. |
| null | Liu Yanpeng， Zhu Lixin， Zhou Yongzhang，2018.Application of convolutional neural network in prospecting prediction of ore deposits：Taking the Zhaojikou Pb-Zn ore deposit in Anhui Province as a case［J］.Acta Petrologica Sinica，34（11）：3217-3224. |
| null | Liu Yanpeng， Zhu Lixin， Zhou Yongzhang，2020.Experimental research on big data mining and intelligent prediction of prospecting target area-application of convolutional neural network model［J］.Geotectonica et Metallogenia，44（2）：192-202. |
| null | Ren Wenxiu， Luo Jianmin， Sun Bainian， al et，2018.Application of geochemical data in gold prospecting and target selecting：Taking the Yushishan area in Gansu Province as a case［J］.Acta Petrologica Sinica，34（11） ：3225-3234. |
| null | Silver D， Schrittwieser J， Simonyan K， al et，2017.ing the game of go without human knowledge［J］.Nature，550：354-359. |
| null | Wigan M R， Clarke R，2013.Big data’s big unintended consequences［J］.IEEE Computer，46：36-53. |
| null | Wang Huaitao， Luo Jianmin， Wang Jinrong， al et，2018.Quantitative classification and metallogenic prognosis of basic-ultrabasic rocks based on big data：Taking the Beishan area as an example［J］.Acta Petrologica Sinica，34（11）：3195-3206. |
| null | Wang Huaitao， Yang Jing， Du Jun， al et，2019.Comparison of the ultrabasic rocks associated with the Cu-Ni deposits in Beishan and Jinchuan，Gansu province and those associated within-plate environment：preliminary results of big data research［J］.Earth Science Fromtiers，26（4）：94-108. |
| null | Wang Yuxi， Luo Jianmin， Wang Jinrong， al et，2018.The gold prospecting targets quantitative optimization based on information of the magmatic rocks （oxide） in Dunhuang block，Gansu Province［J］.Acta Petrologica Sinica，34（2）：319-325. |
| null | Wang Yu， Zhou Yongzhang， Xiao Fan， al et，2020.Numerical metallogenic modelling and support vector machine methodsapplied to predict deep mineralization：A case study from the Fankou Pb-Zn ore deposit in northern Guangdong［J］.Geotectonica et Metallogenia，44（2）：222-230. |
| null | Zuo R G， Xiong Y H， Wang J， al et，2019.Deep learning and its application in geochemical mapping［J］.Earth-Science Reviews，192：1-14. |
| null | Zhang Qi， Zhou Yongzhang，2018.Big data helps geology de-velop rapidly［J］.Acta Petrologica Sinica，34（11）：3167-3172. |
| null | Zhao C B， Hobbs B E， Ord A，2009.Fundamentals of Computational Geoscience［J］.Berlin：Springer. |
| null | Zhou Yongzhang， Chen Shuo， Zhang Qi， al et，2018.Advances and prospects of big data and mathematical geoscience［J］.Acta Petrologica Sinica，34（2） ：255-263. |
| null | Zhang Zhongping， Wu Yafei， Li Jianwei，2018.Characteristics and genesis of the silicified breccias in the Dqiao gold deposit，West Qinling orogeny［J］.Geological Science and Technology Information，37（2）：79-88. |
| null | 罗建民，张琪，宋秉田，等，2017.物化探信息综合处理在找矿靶区定量优选中的应用［J］.矿物岩石地球化学通报，36（6）：886-890. |
| null | 罗建民，王晓伟，宋秉田，等，2018.岩浆岩定量分类方法探讨—以甘肃省西秦岭地区为例［J］.岩石学报，34（2）：326-332. |
| null | 罗建民，张旗，2019a.大数据开创地学研究新途径：查明相关关系，增强研究可行性［J］.地学前缘，26（4）：6-12. |
| null | 罗建民，王晓伟，张琪，等，2019b.地质大数据方法在区域找矿靶区定量优选中的应用［J］.地学前缘，26（4）：76-83. |
| null | 刘艳鹏，朱立新，周永章，2018.卷积神经网络及其在矿床找矿预测中的应用——以安徽省兆吉口铅锌矿床为例［J］.岩石学报，34（11）：3217-3224. |
| null | 刘艳鹏，朱立新，周永章，2020.大数据挖掘与智能预测找矿靶区实验研究——卷积神经网络模型的应用［J］.大地构造与成矿学，44（2）：192-202. |
| null | 任文秀，罗建民，孙柏年，等，2018.化探数据挖掘在金矿找矿及靶区优选中的应用—以甘肃玉石山地区为例［J］.岩石学报，34（11）：3225-3234. |
| null | 王怀涛，罗建民，王金荣，等，2018.基于大数据的基性—超基性岩定量分类及成矿预测研究——以北山地区为例［J］.岩石学报，34（11）：3195-3206. |
| null | 王怀涛，杨婧，杜君，等，2019.甘肃北山和金川与铜镍矿床有关的超基性岩与全球板内环境超基性岩的对比：大数据研究的初步结果［J］.地学前缘，26（4）：94-108. |
| null | 王玉玺，罗建民，王金荣，等，2018.甘肃敦煌地块基于岩浆岩（氧化物）信息的金找矿靶区定量优选［J］.岩石学报，34（2）：319-325. |
| null | 王语，周永章，肖凡，等，2020.基于成矿条件数值模拟和支持向量机算法的深部成矿预测——以粤北凡口铅锌矿为例［J］.大地构造与成矿学，44（2）：222-230. |
| null | 张旗，周永章，2018.大数据助地质腾飞：岩石学报2018第11期大数据专题“序”［J］.岩石学报，34（11）：3167-3172. |
| null | 周永章，陈烁，张旗，等，2018.大数据与数学地球科学研究进展——大数据与数学地球科学专题代序［J］.岩石学报，34（2）：255-263. |
| null | 张忠平，吴亚飞，李建威，2018.西秦岭地区大桥金矿床规划角砾岩的特征及成因［J］.地质科技情报，37（2）：79-88. |
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