2019 Vol. 2, No. 2
Article Contents

Shi-hong Zhang, Ke-yan Xiao, Jian-ping Chen, Jie Xiang, Ning Cui, Xiao-nan Wang, 2019. Development and future prospects of quantitative mineral assessment in China, China Geology, 2, 198-210. doi: 10.31035/cg2018097
Citation: Shi-hong Zhang, Ke-yan Xiao, Jian-ping Chen, Jie Xiang, Ning Cui, Xiao-nan Wang, 2019. Development and future prospects of quantitative mineral assessment in China, China Geology, 2, 198-210. doi: 10.31035/cg2018097

Development and future prospects of quantitative mineral assessment in China

More Information
  • Mineral potential assessment at the Earth’s surface has been an important research for geoscientists around the world in the past five decades. The fundamental aspects of mineral assessment at different scales can be associated with the following tasks, e.g., mineral potential mapping and estimation of mineral resources. This paper summarized the history and development in terms of theories, methods technologies and software platforms for quantitative assessment of mineral resources in China, e.g. comprehensive information methodology, geological anomaly, three-component quantitative prediction method, 5P ore-finding area, integrated information assessment method, nonlinear process modeling and fractals, three dimensional mineral potential mapping, etc. At last, to discuss the future of quantitative mineral assessment in an era of big data including platform for 3D visualization, analysis and sharing, new methods and protocols for data cleaning, information enhancement, information integration, and uncertainties and multiple explanations of multi-information.

  • 加载中
  • [1] Agterberg FP. 1974. Geomathematics. Elsevier, Amsterdam, 596.

    Google Scholar

    [2] Agterberg FP. 1989. Computer programs for mineral exploration. Science, 245, 76–81. doi: 10.1126/science.245.4913.76

    CrossRef Google Scholar

    [3] Agterberg FP. 2014. Geomathematics: Theoretical foundations, applications and future developments. Springer International Publishing, 553.

    Google Scholar

    [4] Allais M. 1957. Method of appraising economic prospects of mining exploration over large territories: Algerian Sahara case study. Management Science, 3(4), 285–347. doi: 10.1287/mnsc.3.4.285

    CrossRef Google Scholar

    [5] Berg RC. 2011. Synopsis of current three-dimensional geological mapping and modeling in geological survey organizations. Champaign ILL Illinois State Geological Survey, 578.

    Google Scholar

    [6] Bliss JD, Menzie WD. 1993. Spatial mineral-deposit models and the prediction of undiscovered mineral deposits. In: Kirkham RV, Sinclair WD, Thorpe RI, Duke JM (eds) Mineral deposit modeling. Geological Association Canada Special Paper, 40, 693–706

    Google Scholar

    [7] Bonham-Carter GF, Agterberg FP, Wright, DF. 1989. Weight of evidence modeling: A new approach to mapping mineral potential. In: Agterberg, F.P., Bonham-Carter, G.F., (Eds.), Statistical Applications in the Earth Sciences: Geological Survey Canada Paper 89–9, 171–183.

    Google Scholar

    [8] Bonham-Carter GF. 1994. Geographic information systems for geoscientists: modelling with GIS. Pergamon, Computer Methods in the Geosciences, 13, 398.

    Google Scholar

    [9] Brown WM, Gedeon TD, Groves DI, Barnes RG. 2000. Artificial neural networks: a new method for mineral prospectivity mapping. Australian Journal of Earth Sciences, 47(4), 757–770. doi: 10.1046/j.1440-0952.2000.00807.x

    CrossRef Google Scholar

    [10] Chen GX, Cheng QM. 2017. Fractal-based wavelet filter for separating geophysical or geochemical anomalies from background. Mathematical Geosciences, 1–24.

    Google Scholar

    [11] Chen JP, Lü P, Wu W, Zhao J, Hu Q. 2007. A 3D method for predicting blind orebodies, based on a 3d visualization model and its application. Earth Science Frontiers, 14(5), 54–62 (in Chinese with English abstract). doi: 10.1016/S1872-5791(07)60035-9

    CrossRef Google Scholar

    [12] Chen JP, Wang CN, Shang BC, Shi R. 2012. Three-dimensional metallogenic prediction in Yongmei region based on digital ore deposit model. Scientific & Technological Management of Land & Resources, 29(6), 14–20 (in Chinese with English abstract).

    Google Scholar

    [13] Chen JP, Yu PP, Shi R, Yu M, Zhang SC. 2014. Research on three-dimensional quantitative prediction and evaluation methods of regional concealed ore bodies. Earth Science Frontiers, 21(5), 211–220 (in Chinese with English abstract).

    Google Scholar

    [14] Chen JP, Li J, Cui N, Yu PP. 2015. The construction and application of geological cloud under the big data background. Geological Bulletin of China, 34(7), 1260–1265 (in Chinese with English abstract).

    Google Scholar

    [15] Chen JP, Xiang J, Hu Q, Yang W, Lai ZL, Hu B, Wei W. 2016. Quantitative geoscience and geological big data development: a review. Acta Geologica Sinica (English Edition), 90(4), 1490–1515.

    Google Scholar

    [16] Chen YL. 2015. Mineral potential mapping with a restricted boltzmann machine. Ore Geology Reviews, 71, 749–760. doi: 10.1016/j.oregeorev.2014.08.012

    CrossRef Google Scholar

    [17] Chen YL, Wu W. 2017. Mapping mineral prospectivity using an extreme learning machine regression. Ore Geology Reviews, 80, 200–213. doi: 10.1016/j.oregeorev.2016.06.033

    CrossRef Google Scholar

    [18] Chen YL, Wu W. 2018. Isolation Forest as an alternative data-driven mineral prospectivity mapping method with a higher data-processing efficiency. Natural Resources Research, 1–16.

    Google Scholar

    [19] Chen Y. 1989. Application of fractals in earth science. Academic Press, 1–163 (in Chinese with English abstract).

    Google Scholar

    [20] Cheng QM, Agterberg FP, Ballantyne S. 1994. The separation of geochemical anomalies from background by fractal methods. Journal of Geochemical Exploration, 51, 109–130. doi: 10.1016/0375-6742(94)90013-2

    CrossRef Google Scholar

    [21] Cheng QM, Agterberg FP. 1999. Fuzzy weights of evidence method and its application in mineral potential mapping. Natural Resources Research, 8, 27–35. doi: 10.1023/A:1021677510649

    CrossRef Google Scholar

    [22] Cheng QM. 1999. Multifractality and spatial statistics. Computers & Geosciences, 25, 949–961.

    Google Scholar

    [23] Cheng QM. 2000. GeoData analysis system (GeoDAS) for mineral exploration: user’s guide and exercise manual. Material for the training workshop on GeoDAS held at York University, 1–3, 2000.

    Google Scholar

    [24] Cheng QM, Xu Y, Grunsky E. 2000. Integrated spatial and spectrum method for geochemical anomaly separation. Natural Resources Research, 9, 43–52. doi: 10.1023/A:1010109829861

    CrossRef Google Scholar

    [25] Cheng QM. 2004. A new model for quantifying anisotropic scale invariance and for decomposition of mixing patterns. Mathematical Geology, 36, 345–360. doi: 10.1023/B:MATG.0000028441.62108.8a

    CrossRef Google Scholar

    [26] Cheng QM. 2005. Multifractal distribution of eigenvalues and eigenvectors from 2D multiplicative cascade multifractal fields. Mathematical Geology, 37, 915–927. doi: 10.1007/s11004-005-9223-1

    CrossRef Google Scholar

    [27] Cheng QM. 2006. Singularity generalized self Similarity fractal spectrum (3S) models. Earth Science, 31(3), 337–348 (in Chinese with English abstract).

    Google Scholar

    [28] Cheng QM. 2007. Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan Province, China. Ore Geology Reviews, 32, 314–324. doi: 10.1016/j.oregeorev.2006.10.002

    CrossRef Google Scholar

    [29] Cheng QM. 2008. Non-linear theory and power-law models for information integration and mineral resources quantitative assessments. Mathematical Geosciences, 40, 503–532. doi: 10.1007/s11004-008-9172-6

    CrossRef Google Scholar

    [30] Cheng QM, Bonham-Carter GF, Wang WL, Zhang SY, Li WC, Xia QL. 2011. A spatially weighted principal component analysis for multi-element geochemical data for mapping locations of felsic intrusions in the Gejiu mineral district of Yunnan, China. Computers & Geosciences, 37, 662–669.

    Google Scholar

    [31] Cheng QM. 2012a. Singularity theory and methods for mapping geochemical anomalies caused by buried sources and for predicting undiscovered mineral deposits in covered areas. Journal of Geochemical Exploration, 122, 55–70. doi: 10.1016/j.gexplo.2012.07.007

    CrossRef Google Scholar

    [32] Cheng QM. 2012b. Ideas and methods for mineral resources integrated prediction in covered areas. Earth Science, 37(6), 1109–1125 (in Chinese with English abstract).

    Google Scholar

    [33] Cheng QM. 2015. Boost WofE: A new sequential weights of evidence model reducing the effect of conditional dependency. Mathematical Geosciences, 47, 591–621. doi: 10.1007/s11004-014-9578-2

    CrossRef Google Scholar

    [34] Cheng QM. 2017. Singularity analysis of global zircon U-Pb age series and implication of continental crust evolution. Gondwana Research, 51, 51–63. doi: 10.1016/j.gr.2017.07.011

    CrossRef Google Scholar

    [35] Chung CF. 1978. Computer program for the logistic model to estimate the probability of occurrence of discrete events. Geological Survey of Canada, 78–12, 23.

    Google Scholar

    [36] Cox DP. 1983. U.S. Geological Survey-INGEOMINAS mineral resource assessment of Colombia: additional ore deposit models. U.S. Geological Survey Open File Report, 83–901.

    Google Scholar

    [37] Cui N, Chen JP, Xiang J. 2018. Prediction model and resource potential of copper in China. Earth Science Frontiers, 25(3), 13–30 (in Chinese with English abstract).

    Google Scholar

    [38] Currenti G, Napoli R, Sicali A, Greco F, Negro CD. 2014. Geofim: a WebGIS application for integrated geophysical modeling in active volcanic regions. Computers & Geosciences, 70(9), 120–127.

    Google Scholar

    [39] Di QY, Zhu RX, Xue GQ, Yin CC, Li X. 2019. New development of the Electromagnetic (EM) methods for deep exploration. Chinese Journal of Geophysics, 62(6), 2128–2138 (in Chinese with English abstract).

    Google Scholar

    [40] Du BF, Yang CQ, Chai JY, Bai GD, Li WW, Ning FZ. 2018. The effect of stream sediment survey for prospecting in Chunzhe area, Tibet. Geology in China, 45(3), 604–616 (in Chinese with English abstract).

    Google Scholar

    [41] Eberlein DG, Menzie WD. 1978. Maps and tables describing areas of mineral resource potential of Central Alaska. U.S. Geological Survey Open File Report 78–1–D.

    Google Scholar

    [42] Folger P. 2009. Geospatial information and geographic information systems (GIS): current issues and future challenges. CRS Report for Congress, 1–26.

    Google Scholar

    [43] Frank T, Tertois AL, Mallet JL. 2007. 3D-reconstruction of complex geological interfaces from irregularly distributed and noisy point data. Computers and Geosciences, 33(7), 932–943. doi: 10.1016/j.cageo.2006.11.014

    CrossRef Google Scholar

    [44] Gao L, Lu YT, Yu PP, Xiao F. 2017. Three-dimensional visualization and quantitative prediction for mine: A case study in Xiayuandong Pb-Zn ore deposits, Pangxidong region, southern part of Qin-Hang metallogenic belt, China. Acta Petrologica Sinica, 33(3), 767–778 (in Chinese with English abstract).

    Google Scholar

    [45] Guillen A, Calcagno P, Courrioux G, Joly A, Ledru P. 2008. Geological modelling from field data and geological knowledge - Part II. Modelling validation using gravity and magnetic data inversion. Physics of the Earth and Planetary Interiors, 171(1-4), 158–169.

    Google Scholar

    [46] Harris DP. 1965. An application of multivariate statistical analysis to mineral exploration. PhD dissertation. The Pennsylvania State Univ., University Park, Pennsylvania, 261.

    Google Scholar

    [47] Harris DP. 1984. Mineral resources appraisal–mineral endowment, resources, and potential supply. Concept, methods, and cases. Oxford University Press, New York, 455.

    Google Scholar

    [48] Houlding SW. 1994. 3D geoscience modeling: computer techniques for geological characterization. London: Springer-Verlag, 309.

    Google Scholar

    [49] Hu GD, Chen JG, Chen SY. 2000. Metallic mineral resources assessment and analysis system design. Journal of China University of Geosciences, 11(3), 308–311 (in Chinese with English abstract).

    Google Scholar

    [50] Huang WB, Xiao KY, Ding JH, Li N. 2011. Potential assessment of solid mineral resources based on GIS. Acta Geologica Sinica, 85(11), 1834–1843 (in Chinese with English abstract).

    Google Scholar

    [51] Jørgensen F, Høyer AS, Sandersen PBE, He XL, Foged N. 2015. Combining 3D geological modelling techniques to address variations in geology, data type and density-an example from southern denmark. Computers & Geosciences, 81(C), 53–63.

    Google Scholar

    [52] Kaufmann O, Martin T. 2008. 3D geological modelling from boreholes, cross-sections and geological maps, application over former natural gas storages in coal mines. Computers & Geosciences, 34(3), 278–290.

    Google Scholar

    [53] Kingston GA, David M, Meyer RF, Oeenshine AT, Slamet S, Schanz JJ. 1978. Workshop on volumetric estimation. Journal of the International Association for Mathematical Geology, 10(5), 495–499. doi: 10.1007/BF02461980

    CrossRef Google Scholar

    [54] Lei SB, Qing M, Niu CY, Wang L. 2016. Current gold prospecting in China. Acta Geologica Sinica (English Edition), 90(4), 1298–1320.

    Google Scholar

    [55] Li GM, She HQ, Zhang L, Liu B, Dong YJ. 2009. Based on Mineral Resource Assessment System (MRAS) for the metallogenic prognosis in the Gangdese metallogenic belt, Tibet. Geology and Exploration, 45(6), 645–654 (in Chinese with English abstract).

    Google Scholar

    [56] Li RX, Wang GW, Carranza EJM. 2016. Geocube: A 3D mineral resources quantitative prediction and assessment system. Computers & Geosciences, 89, 161–173.

    Google Scholar

    [57] Li N, Xiao KY, Sun L, Li SM, Zi JW, Wang K, Song XL, Ding JH, Li CB. 2018. Part I: a resource estimation based on mineral system modelling prospectivity approaches and analogical analysis: a case study of the MVT Pb-Zn deposits in huayuan district, china. Ore Geology Reviews, 101, 966–984. doi: 10.1016/j.oregeorev.2018.02.014

    CrossRef Google Scholar

    [58] Li S, Chen JP, Xiang J. 2018. Prospecting information extraction by text mining based on convolutional neural networks-a case study of the lala copper deposit, China. IEEE Access, 6, 52286–52297. doi: 10.1109/ACCESS.2018.2870203

    CrossRef Google Scholar

    [59] Li S, Xiao KY, Tang JX, Zou W, Li N, Sun Y. 2011. 3D geologic modeling of the Jiama Cu deposit based on MinExplorer system. Journal of Chengdu University of Technology, 38(3), 291–297 (in Chinese with English abstract).

    Google Scholar

    [60] Liu Y, Zhou KF, Xia QL. 2017. A maxEnt model for mineral prospectivity mapping. Natural Resources Research, 27, 299–313.

    Google Scholar

    [61] Liu QN, Zhou XD, Su XY, He YL, Guo Z. 2018. An analysis of geological, geophysical and geochemical characteristics and prospecting potentiality of the Liulaowan area in Henan Province. Geology in China, 45(2), 392–407 (in Chinese with English abstract).

    Google Scholar

    [62] Mallet JL. 1997. Discrete modeling for natural objects. Mathematical Geology, 29(2), 199–219. doi: 10.1007/BF02769628

    CrossRef Google Scholar

    [63] Mallet JL. 2002. Geomodeling. Applied Geostatistics. Oxford University Press, New York, 624.

    Google Scholar

    [64] Mandelbrot BB. 1975. Les objects fractals: forme, hazard et dimension. Flammarion, Paris, 1–19.

    Google Scholar

    [65] Mao XC, Ren J, Liu ZK, Chen J, Tang L, Deng H, Bayless RC, Yang B, Wang MJ, Liu CM. 2019. Three-dimensional prospectivity modeling of the Jiaojia-type gold deposit, Jiaodong Peninsula, Eastern China: A case study of the Dayingezhuang deposit. Journal of Geochemical Exploration, 203, 27–44. doi: 10.1016/j.gexplo.2019.04.002

    CrossRef Google Scholar

    [66] Mao XC, Zhang B, Deng H, Chen J. 2016. Three-dimensional morphological analysis method for geologic bodies and its parallel implementation. Computers & Geosciences, 96, 11–22.

    Google Scholar

    [67] Markus R, Gustau CV, Bjorn S, Martin J, Joachim D, Nuno C, Prabhat. 2019. Deep learning and process understanding for data-driven Earth system science. Nature, 566, 195–204. doi: 10.1038/s41586-019-0912-1

    CrossRef Google Scholar

    [68] Martelet G, Calcagno P, Gumiaux C, Truffert C, Bitri A, Gapais D, Brun JP. 2004. Integrated 3D geophysical and geological modelling of the Hercynian Suture Zone in the Champtoceaux area (South Brittany, France). Tectonophysics, 382(1-2), 117–128. doi: 10.1016/j.tecto.2003.12.009

    CrossRef Google Scholar

    [69] Matheron G. 1971. The Theory of Regionalized Variables and its Applications. Ecole Nationale Supérieure des Mines de Paris, Fontainebleau, 21.

    Google Scholar

    [70] Mauro AD, Greco M, Grimaldi M. 2016. A formal definition of big data based on its essential features. Library Review, 65(3), 122–135. doi: 10.1108/LR-06-2015-0061

    CrossRef Google Scholar

    [71] Mayer-Schönberger V, Cukier K. 2013. Big data: a revolution that will transform how we live, work and think. Houghton Mifflin Harcourt, 1–256.

    Google Scholar

    [72] Menzies T, Zimmermann T. 2013. Software analytics: So what? IEEE Software, 30(4), 31–37. doi: 10.1109/MS.2013.86

    CrossRef Google Scholar

    [73] Merriam DF, Drew J, Schuenemeyer JH. 2004. Zipf's Law: a viable geological paradigm? Natural Resources Research, 13, 265–271. doi: 10.1007/s11053-004-0134-5

    CrossRef Google Scholar

    [74] Merticariu V, Baumann P. 2016. The earthServer federation: State, role, and contribution to GEOSS. EGU general assembly Conference(Vol.18). EGU General Assembly Conference Abstracts. http://www.earthserver.eu/node/152.

    Google Scholar

    [75] Meyer RF. 1978. The volumetric method for petroleum resource estimation. Journal of the International Association for Mathematical Geology, 10(5), 501–518. doi: 10.1007/BF02461981

    CrossRef Google Scholar

    [76] Miao JL, Shang W, Wei YH, Gao ZX, Xu Z. 2015. Construction and practice of geological big data management platform based on hybrid architecture. Scientific and Technological Management of Land and Resources, 32(2), 114–119 (in Chinese with English abstract).

    Google Scholar

    [77] European Parliament and Council of the European Union. 2007. Directive 2007/2/EC of the European Parliament and of the Council of 14 March 2007 establishing an Infrastructure for Spatial Information in the European Community (INSPIRE). Official Journal of the European Union, L 108, 1–14. https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX:32007L0002.

    Google Scholar

    [78] Pan GC, Harris DP. 2000. Information synthesis for mineral exploration. Oxford University Press, 450.

    Google Scholar

    [79] Pan GC. 2018. General framework of quantitative target selections. In: Sagar BSD, Cheng QM, Agterberg FP, (Eds.), Handbook of Mathematical Geosciences, 171–183.

    Google Scholar

    [80] Porwal AK, Carranza EJM. 2001. Extended weights-of-evidence modelling for predictive mapping of base metal deposit potential in Aravalli province. Exploration and Mining Geology, 10(4), 273–287. doi: 10.2113/0100273

    CrossRef Google Scholar

    [81] Porwal AK, Kreuzer OP. 2010. Introduction to the special issue: mineral prospectivity analysis and quantitative resource estimation. Ore Geology Reviews, 38(3), 121–127. doi: 10.1016/j.oregeorev.2010.06.002

    CrossRef Google Scholar

    [82] Reninger PA, Martelet G, Perrin J, Deparis J, Chen Y. 2017. Slopes of an airborne electromagnetic resistivity model interpolated jointly with borehole data for 3d geological modelling. Geophysical Prospecting, 65, 1085–1096. doi: 10.1111/gpr.2017.65.issue-4

    CrossRef Google Scholar

    [83] Richter DH, Singer DA, Cox DP. 1975. Mineral resource map of the Nabesna Quadrangle, Alaska. U.S. Geological Survey Miscellaneous Field Studies Map MF–655K.

    Google Scholar

    [84] Root DH, Menzie WD, Scott WA. 1992. Computer Monte Carlo simulation in quantitative resource estimation. Nonrenewable Resources 1, 125–138.

    Google Scholar

    [85] Schruben P. 2002. Assessment of undiscovered deposits of gold, silver, copper, lead, and zinc in the United States: a portable document (PDF) recompilation of USGSOFR 96-96 and Circular 1178 (1998). U.S. Geological Survey Open File Report 02–198.

    Google Scholar

    [86] Schultz A. 2018. The evolution of a continent: thirteen years of EarthScope magnetotelluric three-dimensional imaging of the United States. Acta Geologica Sinica (English Edition), 92(z1), 1–2.

    Google Scholar

    [87] Shen BM. 1993. Fractal structural factors and its application in geology. Acta Petrologica Sinica, 9(3), 267–276 (in Chinese with English abstract).

    Google Scholar

    [88] Shen XD, Chen C, Meng HC. 2017. Research progress of three-dimensional geological modeling of mine based on 3DMine. Industrial Minerals & Processing, (4), 34–37 (in Chinese with English abstract).

    Google Scholar

    [89] Sierra Systems Group Inc. 2003. GeoNOVA portal architecture and implementation plan. http://www.gov.ns.ca/snsmr/land/geonova/pdf/Geo-NOVAPortal-Vision_Final_20030721.pdf.

    Google Scholar

    [90] Singer DA, Kouda R. 1996. Application of a feedforward neural network in the search for kuroko deposits in the Hokuroku district, Japan. Mathematical Geology, 28(8), 1017–1023. doi: 10.1007/BF02068587

    CrossRef Google Scholar

    [91] Singer DA. 1993. Basic concepts in three-part quantitative assessments of undiscovered mineral resources. Nonrenewable Resources, 2(2), 69–83. doi: 10.1007/BF02272804

    CrossRef Google Scholar

    [92] Song MM, Li Z, Zhou B, Li CL. 2014. Cloud computing model for big geological data processing. Applied Mechanics & Materials, 475-476, 306–311.

    Google Scholar

    [93] Song XL, Li N, Xiao KY, Fan JF, Cui N. 2018. Design and implementation of a information management system for the national mineral resource potential assessment project. Earth Science Frontiers, 25(3), 196–203 (in Chinese with English abstract).

    Google Scholar

    [94] Steuer A, Siemon B, Auken E. 2009. A comparison of helicopter-borne electromagnetics infrequency- and time-domain at the Cuxhaven valley in Northern Germany. Journal of Applied Geophysics, 67(3), 194–205. doi: 10.1016/j.jappgeo.2007.07.001

    CrossRef Google Scholar

    [95] Su GH, Shen YP, Sun JH, He SF and Wei HL. 2012. The global oil and gas geology database information sharing system based on ArcGIS server. Journal of Geo-Information Science, 14(2), 217–222 (in Chinese with English abstract). doi: 10.3724/SP.J.1047.2012.00217

    CrossRef Google Scholar

    [96] Sun L, Xu CP, Xiao KY, Zhu YS, Yan LY. 2018. Geological characteristics, metallogenic regularities and the exploration of graphite deposits in China. China Geology, 1, 425–434.

    Google Scholar

    [97] Sun YB, Wang RJ, Wei BZ, Wang B, Dong SF, Li CJ, Li MS. 2018. The application of hyperspectral remote sensing ground-air integrated prediction method to the copper gold deposit prospecting in Kalatag area, Xinjiang. Geology in China, 45(1), 178–191 (in Chinese with English abstract).

    Google Scholar

    [98] Vistelius AB. 1968. Mathematical geology: a report of progress. Geocom, 1(8), 229–269.

    Google Scholar

    [99] Wang GW, Huang L. 2012. 3D geological modeling for mineral resource assessment of the Tongshan Cu deposit, Heilongjiang Province, China. Geoscience Frontiers, 3(4), 483–491 (in Chinese with English abstract). doi: 10.1016/j.gsf.2011.12.012

    CrossRef Google Scholar

    [100] Wang SC, Wang YT. 1989. Principles of synthetic information interpretation and the compiling method of synthetic prognostic map. Press of Jilin University, Changchun, China, 165 (in Chinese with English abstract).

    Google Scholar

    [101] Wang SC, Fan JZ, Cheng QM. 1990. Evaluation method of comprehensive information for Au deposits. Jilin Science and Technology Press, 441.

    Google Scholar

    [102] Wang SC, Chen YQ. 1994. The basic rules and characteristics of ore-forming series prognosis. Contributions To Geology and Mineral Resources Research, 9(4), 79–85 (in Chinese with English abstract).

    Google Scholar

    [103] Wang SC. 2010. The new development of theory and method of synthetic information mineral resources prognosis. Geological Bulletin of China, 29(10), 1399–1403 (in Chinese with English abstract).

    Google Scholar

    [104] Wang Y, Shen W, Zhao PD. 2008. MRQP: A windows-based mixed language program for mineral resource quantitative prediction. Computers & Geosciences, 34(11), 1631–1637.

    Google Scholar

    [105] Wang YS, Liu SF, Wang JH, Qin XY, Liu GQ, Cui XL. 2018. Geophysical field characteristics and deep ore prospecting prediction of the Nannihu molybdenum lead-zinc-silver polymetallic ore field in east Qinling mountain. Geology in China, 45(4), 803–818 (in Chinese with English abstract).

    Google Scholar

    [106] Wyborn LAI, Heinrich CA, Jauqes AL. 1994. Australian Proterozoic mineral systems: essential ingredients and mappable criteria. Journal of the City Planning Institute of Japan, v. 5, p. 109–115.

    Google Scholar

    [107] Xiang J, Chen J, Hu B, Hu Q, Yang W. 2016. 3D metallogenic prediction based on 3D geological-geophysical model: A case study in Tongling mineral district of Anhui. Advances in Earth Science, 31(6), 603–614 (in Chinese with English abstract).

    Google Scholar

    [108] Xiao, KY, Zhang XH, Li JC, Yang YH, Chen JP, Ding JH, Lou DB, Wang BL, Ye TZ, Zhang LX. 2007. Quantitative assessment method for national important mineral resources prognosis. Earth Science Frontiers, 14(5), 20–26 (in Chinese with English abstract).

    Google Scholar

    [109] Xiao KY, Chen XG, Li N, Zou W, Sun L. 2010a. 3D visualization technology for geological and mineral exploration assessment and the development of MinExplorer system. Mineral Deposit, 29(s1), 758–760 (in Chinese with English abstract).

    Google Scholar

    [110] Xiao, KY, Ye TZ, Li JC, Yang YH, Ding JH. 2010b. Integrated geo-information modeling reserve estimation method. Geological Bulletin of China, 10, 1404–1412 (in Chinese with English abstract).

    Google Scholar

    [111] Xiao KY, Cheng SL, Lou DB, Sun L. 2010c. Integrated information evaluation model for regional mineral resources quantitative assessment. Geological Bulletin of China, 29(10), 1430–1444 (in Chinese with English abstract).

    Google Scholar

    [112] Xiao KY, Li, N, Sun L, Zou W, Li Y. 2012. Method of 3D digital deposit modeling establishment and its application. Mineral Deposits, s1, 929–930 (in Chinese with English abstract).

    Google Scholar

    [113] Xiao KY, Lou DB, Sun L, Yin JN, Cong Y, Zhang TT. 2013. Collected model of potential evaluation for important national mineral resources in China. Journal of Geology, 37(3), 341–348 (in Chinese with English abstract).

    Google Scholar

    [114] Xiao KY, Sun L, Yin JN, Ding JH, Niu CW, Chen JP, Yang HY. 2014. The prediction and assessment of important mineral resources in China. Acta Geoscientica Sinica, 35(5), 543–551 (in Chinese with English abstract).

    Google Scholar

    [115] Xiao KY, Li N, Porwal A, Holden EJ, Bagas L, Lu Y. 2015a. Research on GIS-based 3D prospectivity mapping and a case study of Jiama copper-polymetallic deposit in Tibet, China. Ore Geology Reviews, 71(3), 611–632.

    Google Scholar

    [116] Xiao KY, Sun L, Li N, Wang K, Fan JF, Ding JH. 2015b. Mineral resources assessment under the thought of big data. Geological Bulletin of China, 34(7), 1266–1272 (in Chinese with English abstract).

    Google Scholar

    [117] Xiao KY, Xing SW, Bagas L, Sun L, Li N, Yin JN, Cui N, Cong Y, Li J, Chen YC, Ye TZ. 2017. The China national mineral assessment initiative. Ore Geology Reviews, 91, 1084–1093. doi: 10.1016/j.oregeorev.2017.08.036

    CrossRef Google Scholar

    [118] Xiong YH, Zuo RG. 2016. Recognition of geochemical anomalies using a deep autoencoder network. Computers & Geosciences, 86, 75–82.

    Google Scholar

    [119] Xiong YH, Zuo RG, Carranza EJM. 2018. Mapping mineral prospectivity through big data analytics and a deep learning algorithm. Ore Geology Reviews, 102, 811–817. doi: 10.1016/j.oregeorev.2018.10.006

    CrossRef Google Scholar

    [120] Yu CW. 1987. Mineralization and dissipative structure. Acta Geologica Sinica, 4, 54–67 (in Chinese with English abstract).

    Google Scholar

    [121] Yu CW. 2006. Fractal growth of mineral deposits at the edge of chaos. Anhui Education Publishing House, 1429 (in Chinese with English abstract).

    Google Scholar

    [122] Yu PP, Chen JP, Chai FS, Zheng X, Yu M, Xu B. 2015. Research on model-driven quantitative prediction and evaluation of mineral resources based on geological big data concept. Geological Bulletin of China, 34(7), 1333–1343 (in Chinese with English abstract).

    Google Scholar

    [123] Ye TZ. 2004. Research on method and technology for assessment of solid mineral resources. China Land Press, Beijing, 351.

    Google Scholar

    [124] Ye TZ, Xiao KY, Yan GS. 2007. Methodology of deposit modeling and mineral resource potential assessment using integrated geological information. Earth Science Frontiers, 14(5), 11–19 (in Chinese with English abstract).

    Google Scholar

    [125] Ye TZ. 2013. Theoretical framework of methodology of deposit modeling and integrated geological information for mineral resource potential assessment. Journal of Jilin University (Earth Science Edition), 43(4), 1053–1072 (in Chinese with English abstract).

    Google Scholar

    [126] Zhai YS. 1999. On the metallogenic system. Earth science Frontiers, 6(1), 13–27 (in Chinese with English abstract).

    Google Scholar

    [127] Zhang J, Li SY, Xu S, Liu CC, Zhou YH. 2012. Potential gold evaluation with weighting of evidence based on MRAS in Jinzhou-Fuxin gold metallogenic belt, western Liaoning Province. Journal of Central South University (Science and Technology), 43(09), 3565–3574 (in Chinese with English abstract).

    Google Scholar

    [128] Zhang ZJ, Zuo RG, Xiong YH. 2016. A comparative study of fuzzy weights of evidence and random forests for mapping mineral prospectivity for skarn-type Fe deposits in the southwestern Fujian metallogenic belt, China. Science China Earth Sciences, 59, 556–572. doi: 10.1007/s11430-015-5178-3

    CrossRef Google Scholar

    [129] Zhang SH, Xiao KY, Zhu YS, Cui N. 2017. A prediction model for important mineral resources in China. Ore Geology Reviews, 91, 1094–1101. doi: 10.1016/j.oregeorev.2017.09.010

    CrossRef Google Scholar

    [130] Zhang BY, Chen YR, Huang AS, Lu H, Cheng QM. 2018. Geochemical field and its roles on the 3D prediction of concealed ore bodies. Acta Petrologica Sinica, 34(2), 352–362 (in Chinese with English abstract).

    Google Scholar

    [131] Zhao PD. 1964. Some basic problems of research on orebody geology in deposits exploration. Geology in China, 2, 9–18 (in Chinese with English abstract).

    Google Scholar

    [132] Zhao PD. 1983. Some questions of prediction of total reserves of mineral. China Geology, 1, 16–19 (in Chinese with English abstract).

    Google Scholar

    [133] Zhao PD, Chi SD. 1991. A preliminary view on geological anomaly. Earth Science-Journal of China University of Geosciences, 3, 241–248 (in Chinese with English abstract).

    Google Scholar

    [134] Zhao PD, Hu WL. 1992. Geologic anomaly theory and mineral resource prognosis. Xinjiang Geology, 2, 93–100 (in Chinese with English abstract).

    Google Scholar

    [135] Zhao PD, Meng XG. 1993. Geological anomaly and mineral prediction. Earth Science-Journal of China University of Geosciences, 1, 39–47 (in Chinese with English abstract).

    Google Scholar

    [136] Zhao PD, Chen YQ, Jin YY. 2000. Quantitative delineation and assessment of "5P" ore-finding area on the basis of geoanomaly principles. Geological Review, S1, 6–16 (in Chinese with English abstract).

    Google Scholar

    [137] Zhao PD. 2002. Three component quantitative resources prediction and assessments: theory and practice of digital mineral prospecting. Earth Science-Journal of China University of Geosciences, 27(5), 482–489 (in Chinese with English abstract).

    Google Scholar

    [138] Zhao PD, Zhang ST, Chen JP. 2004. Discussion on prediction and appraisement of replaceable resources of crisis mine. Journal of Chengdu University of Technology (Science & Technology Edition), 31(2), 111–117 (in Chinese with English abstract).

    Google Scholar

    [139] Zhao PD, Chen JP, Zhang ST. 2005. Mineral deposits: geological anomalies with high economic value. In: Cheng, Q.M., Bonham-Carter, G., eds., Proceedings of Annual Conference of International Association for Mathematical Geology (IAMG’05), 1022–1027.

    Google Scholar

    [140] Zhao PD, Cheng QM, Xia QL. 2008. Quantitative prediction for deep mineral exploration. Journal of China University of Geosciences, 19(4), 309–318. doi: 10.1016/S1002-0705(08)60063-1

    CrossRef Google Scholar

    [141] Zhao PD. 2015. Digital mineral exploration and quantitative evaluation in the big data age. Geological Bulletin of China, 34(7), 1255–1259 (in Chinese with English abstract).

    Google Scholar

    [142] Zhou YZ, Chen S, Zhang Q, Xiao F, Wang SG, Liu YP, Jiao ST. 2018a. Advances and prospects of big data and mathematical geoscience. Acta Petrologica Sinica, 34(2), 255–263 (in Chinese with English abstract).

    Google Scholar

    [143] Zhou YZ, Wang J, Zuo RG, Xiao F, Shen WJ and Wang SG. 2018b. Machine learning, deep learning and Python language in field of geology. Acta Petrologica Sinica, 34(11), 3173–317 (in Chinese with English abstract).

    Google Scholar

    [144] Zhu YS. 2006. Mineral prognosis theory. Acta Geologica Sinica, 10(80), 1515–1527 (in Chinese with English abstract).

    Google Scholar

    [145] Zuo RG, Carranza EJM. 2011. Support vector machine: a tool for mapping mineral prospectivity. Computers & Geosciences, 37(12), 1967–1975.

    Google Scholar

    [146] Zuo RG. 2017. Machine learning of mineralization-related geochemical anomalies: a review of potential methods. Natural Resources Research, 26, 457–4. doi: 10.1007/s11053-017-9345-4

    CrossRef Google Scholar

    [147] Zuo RG, Xiong YH. 2018. Big data analytics of identifying geochemical anomalies supported by machine learning methods. Natural Resources Research, 27, 5–13. doi: 10.1007/s11053-017-9357-0

    CrossRef Google Scholar

    [148] Zuo RG, Xiong YH, Wang J, Carranza EJM. 2019. Deep learning and its application in geochemical mapping. Earth-Science Reviews, 192, 1–14. doi: 10.1016/j.earscirev.2019.02.023

    CrossRef Google Scholar

  • 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

Figures(4)

Article Metrics

Article views(2023) PDF downloads(14) Cited by(0)

Access History

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint