Chinese Academy of Geological SciencesHost
Advanced Search
Previous Article Next Article
2023 Vol. 44, No. 5
Article Contents
Article navigation > Acta Geoscientica Sinica > 2023 > 44(5): 877-886
Next Article Previous Article

LIU Bing-li, XIE Miao, KONG Yun-hui, TANG Rui, YU Zheng-bo, LUO De-jiang. 2023. Quantitative Gold Resources Prediction in Xiahe–Hezuo Area Based on Convolutional Auto-Encode Network. Acta Geoscientica Sinica, 44(5): 877-886. doi: 10.3975/cagsb.2023.022801
Citation: LIU Bing-li, XIE Miao, KONG Yun-hui, TANG Rui, YU Zheng-bo, LUO De-jiang. 2023. Quantitative Gold Resources Prediction in Xiahe–Hezuo Area Based on Convolutional Auto-Encode Network. Acta Geoscientica Sinica, 44(5): 877-886. doi: 10.3975/cagsb.2023.022801
shu

Quantitative Gold Resources Prediction in Xiahe–Hezuo Area Based on Convolutional Auto-Encode Network

  • Geomathematics Key Laboratory of Sichuan Province (Chengdu University of Technology), Chengdu, Sichuan 610059

Received Date:  06 November 2022
Revised Date:  25 February 2023
    Abstract
  • The Xiahe–Hezuo area of Gansu province has complex geological structures and abundant gold mineral resources, is an important metallogenic belt within the West Qinling Mountains. At present, a certain number of gold polymetallic deposits have been found in this area, and there is still a good prospecting potential of gold mineralization. Therefore, this paper uses the Xiahe–Hezuo area as a research area. Geochemical associations are quantitatively extracted by the method of compositional data analysis. Based on the geological structure and geochemical anomalies, multiple metallogenic information is integrated for building the mineral exploration model. And then, the Convolutional Auto-Encode network (CAE) method is used for regional gold resource prediction. Finally, 7 exploration targets are delineated. The result shows an excellent prediction performance (AUC=0.90), and the targets deserve further research.
    • FullText(HTML)
  • 加载中
    • References
  • 蔡惠慧, 徐永洋, 李孜轩, 曹豪豪, 冯雅兴, 陈思琼, 李永胜.2019. 基于卷积神经网络模型划分成矿远景区--以甘肃大桥地区金多金属矿田为例[J]. 地质通报, 38(12):1999-2009.

    Google Scholar

    陈建平, 吕鹏, 吴文, 王功文, 朱鹏飞, 赵洁. 2007. 基于三维建模的立方体预测模型找矿方法: CN, CN101038680 A[P].2007-09-19.

    Google Scholar

    成秋明. 2006. 非线性成矿预测理论: 多重分形奇异性-广义自相似性-分形谱系模型与方法[J]. 地球科学--中国地质大学学报, 31(3): 337-348.

    Google Scholar

    成秋明. 2007. 成矿过程奇异性与矿产预测定量化的新理论与新方法[J]. 地学前缘, (5): 42-53.

    Google Scholar

    成秋明. 2021. 什么是数学地球科学及其前沿领域?[J]. 地学前缘, 28(3): 6-25.

    Google Scholar

    来杰, 王晓丹, 向前, 宋亚飞, 权文. 2021. 自编码器及其应用综述[J]. 通信学报, 42(9): 218-230.

    Google Scholar

    李程. 2021. 深部地质地球化学三维定量矿产预测方法研究-以西秦岭早子沟金矿为例[D]. 成都: 成都理工大学.

    Google Scholar

    李康宁, 贾儒雅, 李鸿睿, 汤磊, 刘伯崇, 严康, 韦乐乐. 2020.西秦岭甘肃夏河-合作地区与中酸性侵入岩有关的金铜多金属成矿系统及找矿预测[J]. 地质通报, 39(8): 1191-1203.

    Google Scholar

    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.

    Google Scholar

    刘勇. 2013. 甘肃省枣子沟金矿中酸性脉岩与金成矿关系研究[D]. 西安: 长安大学.

    Google Scholar

    马瑶, 赵江南. 2021. 机器学习方法在矿产资源定量预测应用研究进展[J]. 地质科技通报, 40(1): 132-141.

    Google Scholar

    毛先成, 戴塔根, 吴湘滨, 邹艳红. 2009. 危机矿山深边部隐伏矿体立体定量预测研究--以广西大厂锡多金属矿床为例[J]. 中国地质, 36(2): 424-435.

    Google Scholar

    毛景文. 2001. 西秦岭地区造山型与卡林型金矿床[J]. 矿物岩石地球化学通报, (1): 11-13.

    Google Scholar

    韦良喜. 2015. 甘肃省早子沟金矿床构造演化与成矿[D]. 北京:中国地质大学(北京).

    Google Scholar

    王世称, 陈永良, 夏立显.2000. 综合信息矿产预测理论与方法[M]. 北京:科学出版社.

    Google Scholar

    王世称. 2010. 综合信息矿产预测理论与方法体系新进展[J].地质通报, 29(10): 1399-1403.

    Google Scholar

    肖克炎, 李楠, 孙莉, 邹伟, 李莹. 2012. 基于三维信息技术大比例尺三维立体矿产预测方法及途径[J]. 地质学刊, 36(3):229-236.

    Google Scholar

    谢学锦, 任天祥, 孙焕振.2018. 中国地球化学图集[M]. 北京:地质出版社.

    Google Scholar

    袁峰, 李晓晖, 张明明, 贾蔡, 胡训宇. 2018. 三维成矿预测研究进展[J]. 甘肃地质, 27(1): 32-36.

    Google Scholar

    袁峰, 张明明, 李晓晖, 葛粲, 陆三明, 李建设, 周宇章, 兰学毅. 2019. 成矿预测: 从二维到三维[J]. 岩石学报, 35(12):3863-3874.

    Google Scholar

    叶天竺, 肖克炎, 严光生. 2007. 矿床模型综合地质信息预测技术研究[J]. 地学前缘, 14(5): 11-19.

    Google Scholar

    叶天竺. 2013. 矿床模型综合地质信息预测技术方法理论框架[J]. 吉林大学学报(地球科学版), 43(4): 1053-1072.

    Google Scholar

    张继荣. 2016. 甘肃省夏河地区成矿预测及找矿靶区研究[D].西安: 长安大学.

    Google Scholar

    张帅. 2021. 甘肃省合作-美武地区综合信息找矿预测研究[D].北京:中国地质大学(北京).

    Google Scholar

    张帅, 肖克炎, 朱裕生. 2018. 甘肃夏河--合作一带成矿预测及预测方法比较[J]. 地质学刊, 42(3): 393-400.

    Google Scholar

    赵鹏大, 陈建平, 张寿庭. 2003. “三联式”成矿预测新进展[J].地学前缘, 10(2): 455-463.

    Google Scholar

    赵鹏大. 2002. “三联式”资源定量预测与评价--数字找矿理论与实践探讨[J]. 地球科学, (5): 482-489.

    Google Scholar

    左仁广. 2019. 基于深度学习的深层次矿化信息挖掘与集成[J].矿物岩石地球化学通报, 38(1): 53-60.

    Google Scholar

    AITCHISON J, BARCELÓ-VIDAL C, MARTÍN-FERNÁNDEZ J A, PAWLOWSKY-GLAHN V. 2000. Logratio analysis and compositional distance[J] Mathematical Geology, 32(3):271-275.

    Google Scholar

    BRAHIMI S, AOUN N B, AMAR C B. 2019. Boosted convolutional neural network for object recognition at large scale[J].Neurocomputing, 330: 337-354.

    Google Scholar

    CAI Hui-hui, XU Yong-yang, LI Zi-xuan, CAO Hao-hao, FENG Ya-xing, CHEN Si-qiong, LI Yong-sheng. 2019. The division of metallogenic prospective areas based on convolutional neural network model: A case study of the Daqiao gold polymetallic deposit[J]. Geological Bulletin of China, 38(12):1999-2009(in Chinese with English abstract).

    Google Scholar

    CARRANZA E J M, LABORTE A G. 2015. Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines)[J]. Computers & Geosciences, 74: 60-70.

    Google Scholar

    CARRANZA E J M. 2011. Analysis and mapping of geochemical anomalies using logratio-transformed stream sediment data with censored values[J]. Journal of Geochemical Exploration, 110(2): 167-185.

    Google Scholar

    CHEN Jian-ping, LÜ Peng, WU Wen, WANG Gong-wen, ZHUPeng-fei, ZHAO Jie. 2007. A cube prediction model exploration method based on 3D modeling CN, CN101038680 A[P].(in Chinese)

    Google Scholar

    CHEN Yong-liang, WU Wei. 2017. Mapping mineral prospectivity using an extreme learning machine regression[J]. Ore Geology Reviews, 80: 200-213.

    Google Scholar

    CHENG Qiu-ming. 2006. Singularity-generalized self-similarity-fractal spectrum (3S) models[J]. Earth Science-Journal of China University of Geosciences, 31(3):337-348(in Chinese with English abstract).

    Google Scholar

    CHENG Qiu-ming. 2007. Singular mineralization processes and mineral resources quantitative prediction: new theories and methods[J]. Earth Science Frontiers, (5): 42-53(in Chinese with English abstract).

    Google Scholar

    CHENG Qiu-ming. 2021. What are Mathematical Geosciences and its frontiers?[J]. Earth Science Frontiers, 28(3): 6-25(in Chinese with English abstract).

    Google Scholar

    EGOZCUE J J, PAWLOWSKY-GLAHN V. 2005. Groups of parts and their balances in compositional data analysis[J]. Mathematical Geology, 37(7): 795-828.

    Google Scholar

    FILZMOSER P, HRON K. 2009. Correlation analysis for compositional data[J]. Mathematical Geosciences, 41(8): 905-919.

    Google Scholar

    FILZMOSER P, HRON K, REIMANN C. 2009. Univariate statistical analysis of environmental (compositional) data: problems and possibilities[J]. Science of The Total Environment, 407(23): 6100-6108.

    Google Scholar

    FIORE U, PALMIERI F, CASTIGLIONE A, DE SANTIS A. 2013.Network anomaly detection with the restricted Boltzmann machine[J]. Neurocomputing, 122: 13-23.

    Google Scholar

    GAO Yuan, ZHANG Zhen-jie, XIONG Yi-hui, ZUO Ren-guang.2016. Mapping mineral prospectivity for Cu polymetallic mineralization in southwest Fujian Province China[J]. Ore Geology Reviews, 75: 16-28.

    Google Scholar

    KINGMA D P, BA J L. 2014. Adam: A Method for Stochastic Optimization[J]. ArXiv e-prints: 1412.6980.

    Google Scholar

    LAI Jie, WANG Xiao-dan, XIANG Qian, SONG Ya-fei, QUAN Wen. 2021. Review on autoencoder and its application[J].Journal on Communications, 42(9): 218-230(in Chinese with English abstract).

    Google Scholar

    LI Cheng. 2021. 3D quantitative prediction of mineral resources at depth based on geology and geochemistry-A case study of Zaozigou gold deposit in western Qinling[D]. Chengdu:Chengdu University of Techonology(in Chinese with English abstract).

    Google Scholar

    LI Kang-ning, JIA Ru-ya, LI Hong-rui, TANG Lei, LIU Bo-chong, YAN Kang, WEI Le-le. 2020. The Au-Cu polymetallic mineralization system related to intermediate to felsic intrusive rocks and the prospecting prediction in Xiahe-Hezuo area of Gansu, West Qinling orogenic belt[J]. Geological Bulletin of China, 39(8): 1191-1203(in Chinese with English abstract).

    Google Scholar

    LI Shi, CHEN Jian-ping, XIANG Jie. 2018. Prospecting information extraction by text mining based on convolutional neural networks–A case study of the Lala copper deposit China[J].IEEE Access, 6: 52286-52297.

    Google Scholar

    LI Shi, CHEN Jian-ping, XIANG Jie. 2020. Applications of deep convolutional neural networks in prospecting prediction based on two-dimensional geological big data[J]. Neural Computing and Applications, 32(7): 2037-2053.

    Google Scholar

    LI T F, XIA Q L, ZHAO M Y, GUI Z, LENG S. 2019. Prospectivity Mapping for Tungsten Polymetallic Mineral Resources Nanling Metallogenic Belt South China: Use of Random Forest Algorithm from a Perspective of Data Imbalance[J].Natural Resources Research, 29(1): 1-25.

    Google Scholar

    LIU Yue, ZHOU Ke-fa, ZHANG Nan-an, WANG Jin-lin. 2018.Maximum entropy modeling for orogenic gold prospectivity mapping in the Tangbale-Hatu belt, western Junggar China[J].Ore Geology Reviews, 100: 133-147.

    Google Scholar

    LIU Yan-peng, ZHU Li-xin, ZHOU Yong-zhang. 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(in Chinese with English abstract).

    Google Scholar

    LIU Yong. 2013. Relationship between intermediate-acid dike rock and gold mineralization of the Zaozigou gold deposit, Gansu Province[D]. Xi’an: Chang’an University(in Chinese with English abstract).

    Google Scholar

    MA Yao, ZHAO Jiang-nan. 2021. Advances in the application of machine learning methods in mineral prospectivity mapping[J]. Bulletin of Geological Science and Technology, 40(1):132-141(in Chinese with English abstract).

    Google Scholar

    MAO Jing-wen. 2001. Geology, distribution and classification of gold deposits in the western Qinling Belt, Central China[J].Bulletin of Mineralogy, Petrology and Geochemistry, (1):11-13(in Chinese with English abstract).

    Google Scholar

    MAO Xian-cheng, DAI Ta-gen, WU Xiang-bin, ZOU Yan-hong.2009. The stereoscopic quantitative prediction of concealed ore bodies in the deep and marginal parts of crisis mines: a case study of the Dachang tin polymetallic ore deposit in Guangxi[J]. Geology in China, 36(2): 424-435(in Chinese with English abstract).

    Google Scholar

    RODRIGUEZ-GALIANO V, SANCHEZ-CASTILLO M, CHICA-OLMO M, CHICA-RIVAS M. 2015. Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines[J]. Ore Geology Reviews, 71: 804-818.

    Google Scholar

    SHABANKAREH M, HEZARKHANI A. 2017. Application of support vector machines for copper potential mapping in Kerman region, Iran[J]. Journal of African Earth Sciences, 128: 116-126.

    Google Scholar

    SUN Tao, LI Hui, WU Kai-xing, CHEN Fei, ZHU Zhong, HU Zi-juan. 2020. Data-driven predictive modelling of mineral prospectivity using machine learning and deep learning methods: A case study from Southern Jiangxi Province, China[J]. Minerals, 10(2): 102.

    Google Scholar

    VALENTINE A P, TRAMPERT J. 2012. Data space reduction, quality assessment and searching of seismograms: autoencoder networks for waveform data[J]. Geophysical Journal International, 189(2): 1183-1202.

    Google Scholar

    WANG Ya-si, YAO Hong-xun, ZHAO Si-cheng. 2016. Au to-encoder based dimensionality reduction[J]. Neurocomputing, 184: 232-242.

    Google Scholar

    WANG Shi-cheng, CHEN Yong-liang, XIA Li-xian.2000. Thetheory and method of integrated information mineral prediction[M]. Beijing: Science Press(in Chinese).

    Google Scholar

    WANG Shi-cheng. 2010. The new development of theory and method of synthetic information mineral resources prognosis[J]. Geological Bulletin of China, 29(10): 1399-1403(in Chinese with English abstract).

    Google Scholar

    WEI Liang-xi. 2015. Tectonic evolution and mineralization of the Zaozigou gold deposit, Gansu Province[D]. Beijing: China University of Geosciences(Beijing)(in Chinese with English abstract).

    Google Scholar

    XIAO Ke-yan, LI Nan, SUN Li, ZOU Wei, LI Ying. 2012. Large scale 3D mineral prediction methods and channels based on 3D information technology[J]. Journal of Geology, 36(3):229-236(in Chinese with English abstract).

    Google Scholar

    XIE Xue-jin, REN Tian-xiang, SUN Huan-zhen.2012. Geochemical Atlas of China[M]. Beijing: Geology Press, Beijing (in Chinese with English abstract).

    Google Scholar

    XIONG Yi-hui, ZUO Ren-guang, CARRANZA E J M. 2018.Mapping mineral prospectivity through big data analytics and a deep learning algorithm[J]. Ore Geology Reviews, 102:811-817.

    Google Scholar

    XIONG Yi-hui, ZUO Ren-guang. 2016. Recognition of geochemical anomalies using a deep autoencoder network[J]. Computers & Geosciences, 86: 75-82.

    Google Scholar

    YE Tian-zhu, XIAO Ke-yan, YAN Guang-sheng. 2007. Methodology of deposit modeling and mineral resource potential assessment using integrated geological information[J]. Earth Science Frontiers, 14(5): 11-19(in Chinese with English abstract).

    Google Scholar

    YE Tian-zhu. 2013. Theoretical framework of methodology of deposit modeling and integrated geological information for mineral resource potential assessment[J]. Journal of Jilin University(Earth Science Edition), 43(4): 1053-1072(in Chinese with English abstract).

    Google Scholar

    YUAN Feng, LI Xiao-hui, ZHANG Ming-ming, JIA Cai, HU Xun-yu. 2018. Research progress of 3D prospectivity modeling[J]. Gansu Geology, 27(1): 32-36(in Chinese with English abstract).

    Google Scholar

    YUAN Feng, ZHANG Ming-ming, LI Xiao-hui, GE Can, LU San-ming, LI Jian-she, ZHOU Yu-zhang, LAN Xue-yi. 2019.Prospectivity modeling: From two-dimension to three-dimension[J]. Acta Petrologica Sinica, 35(12): 3863-3874(in Chinese with English abstract).

    Google Scholar

    ZHANG Chun-jie, ZUO Ren-guang. 2021. Recognition of multivariate geochemical anomalies associated with mineraliaztion using an improved generative adversarial network[J]. Ore Geology Reviews, 136: 104264.

    Google Scholar

    ZHANG Ji-rong. 2016. The metallogenic prognosis and mineral resource targeting in the Xiahe area, Gansu Province[D].Xi’an: Chang’an University(in Chinese with English abstract).

    Google Scholar

    Z HANG Shuai. 2021. Multi-geoinformation integration for mineral prospectivity mapping in the Hezuo-Meiwu district, Gansu Province[D]. Beijing: China University of Geosciences(Beijing)(in Chinese with English abstract).

    Google Scholar

    ZHANG Shuai, XIAO Ke-yan, CARRANZA E J M, YANG Fan.2019. Maximum entropy and random forest modeling of mineral potential: Analysis of gold prospectivity in the Hezuo–Meiwu district, West Qinling orogeny, China[J]. Natural Resources Research, 28(3): 645-664.

    Google Scholar

    ZHANG Shuai, CARRANZA E J M, XIAO Ke-yan, WEI Han-tao, YANG Fan, CHEN Zheng-hui, LI Nan, XIANG Jie. 2021.Mineral prospectivity mapping based on isolation forest and random forest: Implication for the existence of spatial signature of mineralization in Outliers[J]. Natural Resources Research, 31(4): 1981-1999.

    Google Scholar

    ZHANG Shuai, XIAO Ke-yan, ZHU Yu-sheng. 2018. Metallogenic prediction and prediction method comparsion in Xia-Hezuo area, Gansu Province[J]. Journal of Geology, 42(3):393-400(in Chinese with English abstract).

    Google Scholar

    ZHANG Zhi-bo, JAISWAL P, RAI R. 2018. FeatureNet: Machining feature recognition based on 3D Convolution Neural Network[J]. Computer-Aided Design, 101: 12-22.

    Google Scholar

    ZHAO Peng-da, CHEN Jian-ping, ZHANG Shou-ting. 2003. The new development of “Three Components” quantitative mineral prediction[J]. Earth Science Frontiers, 10(2): 455-463(in Chinese with English abstract).

    Google Scholar

    ZHAO Peng-da. 2002. “Three-Component” quantitative resource prediction and assessments: Theory and pratice of digital mineral prospecting[J]. Earth Science, (5): 482-489(in Chinese with English abstract).

    Google Scholar

    ZHENG Wen-bao, LIU Bing-li, MCKINLEY M J, COOPER M R, WANG Lu. 2021. Geology and geochemistry-based metallogenic exploration model for the eastern Tethys Himalayan metallogenic belt, Xizang[J]. Journal of Geochemical Exploration, 224: 106743.

    Google Scholar

    ZUO Ren-guang, CARRANZA E J M. 2011. Support vector machine: A tool for mapping mineral prospectivity[J]. Computers & Geosciences, 37(12): 1967-1975.

    Google Scholar

    ZUO Ren-guang. 2014. Identification of weak geochemical anomalies using robust neighborhood statistics coupled with GIS in covered areas[J]. Journal of Geochemical Exploration, 136: 93-101.

    Google Scholar

    ZUO Ren-guang. 2017. Machine learning of mineraliza tion-related geochemical anomalies: A review of potential methods[J]. Natural Resources Research, 26(4): 457-464.

    Google Scholar

    ZUO Ren-guang, XIONG Yi-hui. 2018. Big data analytics of identifying geochemical anomalies supported by machine learning methods[J]. Natural Resources Research, 27(1): 5-13.

    Google Scholar

    ZUO Ren-guang. 2019. Deep learning-based mining and integration of deep-level mineralization information[J]. Bulletin of Mineralogy, Petrology and Geochemistry, 38(1): 53-60(in Chinese with English abstract).

    Google Scholar

    • Relative Articles
  • Cited by
  • Supplements
    • Proportional views
  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索
Export Citation
PDF
XML

Article Metrics

Article views(84) PDF downloads(74) Cited by(0)

Access History

Other Articles By Authors

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    版权所有：中国地质调查局发展研究中心 copyright @2020