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《工程(英文)》 >> 2019年 第5卷 第3期 doi: 10.1016/j.eng.2019.03.006

数据驱动型研究方法在矿物学领域里的新发现——矿物数据资源、数据分析和可视化的最新研究进展

a Geophysical Laboratory, Carnegie Institution for Science, Washington, DC 20015, USA
b Department of Geosciences, The University of Arizona, Tucson, AZ 85721-0077, USA
c Tetherless World Constellation, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
d School of Earth and Climate Sciences, University of Maine, Orono, ME 04469, USA
e Department of Geology, Southern Illinois University, Carbondale, IL 62901, USA
f Mathematics, Statistics, and Computer Science, Purdue University Northwest, Hammond, IN 46323-2094, USA
g Kola Science Centre of the Russian Academy of Sciences, Apatity, Murmansk Region 184209, Russia
h Department of Computer Science, University of Idaho, Moscow, ID 83844-1010, USA
i Mindat.org, Mitcham CR4 4FD, UK
j Department of Geology and Geophysics, University of Wyoming, Laramie, WY 82071-2000, USA

收稿日期: 2018-11-15 修回日期: 2019-02-18 录用日期: 2019-03-13 发布日期: 2019-06-14

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摘要

随着矿物种类多样性、矿物(时空)分布特征和矿物性质等领域海量数据的快速增长,矿物学迎来了数据驱动型研究发现的新纪元。当前,最全面的国际性矿物数据库是IMA 数据库和mindat.org 数据库,其中,IMA 数据库包含了超过5300 种被国际矿物学协会(International Mineralogical Association,IMA)批准认可的矿物及其属性信息。此外,mindat.org 数据库包含了超过100 万种矿物种类及其产地信息,这些矿物来自于世界各地,有登记在册的产地来源就超过了30 万个。采用各种现代化分析方法对这些海量地学数据进行分析解读和可视化处理,进一步增进了对地球圈和生物圈协同演化过程的理解认识,这些分析方法包括chord 图、cluster 图、Klee 图、skyline 图,以及各式各样的网络分析方法。新型数据驱动型分析策略包括矿物演化分析、矿物生态学分析和矿物网络分析,这些分析策略能够系统性地综合考虑矿物的时空分布特征及其多样性。这些分析策略正在增进对矿物共生现象的深入认识,并且首次推动了对“地球上存在但尚未被发现和记录在册矿物”的预测。

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参考文献

[ 1 ] Gastil G. The distribution of mineral dates in time and space. Am J Sci 1960;258 (1):1–35. 链接1

[ 2 ] Nash JT, Granger HC, Adams SS. Geology and concepts of genesis of important types of uranium deposits. Econ Geol 1981:63–116. 链接1

[ 3 ] Zhabin AG. Is there evolution of mineral speciation on Earth? Dokl Earth Sci Sect 1981;247:142–4. 链接1

[ 4 ] Yushkin NP. Evolutionary ideas in modern mineralogy. Zap Vses Mineral Obshch 1982;116(4):432–42. Russian.

[ 5 ] Hazen RM, Papineau D, Bleeker W, Downs RT, Ferry J, McCoy T, et al. Mineral evolution. Am Mineral 2008;93(11–12):1693–720. 链接1

[ 6 ] Hazen RM, Ewing RJ, Sverjensky DA. Evolution of uranium and thorium minerals. Am Mineral 2009;94(10):1293–311. 链接1

[ 7 ] Hazen RM, Bekker A, Bish DL, Bleeker W, Downs RT, Farquhar J, et al. Needs and opportunities in mineral evolution research. Am Mineral 2011;96(7):953–63. 链接1

[ 8 ] Hazen RM, Golden JJ, Downs RT, Hysted G, Grew ES, Azzolini D, et al. Mercury (Hg) mineral evolution: a mineralogical record of supercontinent assembly, changing ocean geochemistry, and the emerging terrestrial biosphere. Am Mineral 2012;97(7):1013–42. 链接1

[ 9 ] Hazen RM, Papineau D. Mineralogical co-evolution of the geosphere and biosphere. In: Knoll AH, Canfield DE, Konhauser KO, editors. Fundamentals of geobiology. Oxford: Wiley-Blackwell; 2012. p. 333–50. 链接1

[10] Hazen RM, Jones AP, Kah L, Sverjensky DA. Carbon mineral evolution. In: Hazen RM, Jones AP, Baross J, editors. Carbon in Earth. Washington, DC: Mineralogical Society of America; 2013. p. 79–107. 链接1

[11] Hazen RM, Sverjensky DA, Azzolini D, Bish DL, Elmore S, Hinnov L, et al. Clay mineral evolution. Am Mineral 2013;98(11–12):2007–29. 链接1

[12] Hazen RM, Liu XM, Downs RT, Golden JJ, Pires AJ, Grew ES, et al. Mineral evolution: episodic metallogenesis, the supercontinent cycle, and the coevolving geosphere and biosphere. Soc Econ Geolog Special Pub 2014;18:1–15. 链接1

[13] Hazen RM, Grew ES, Origlieri M, Downs RT. On the mineralogy of the ‘‘Anthropocene Epoch”. Am Mineral 2017;102(3):595–611. 链接1

[14] Hazen RM. Evolution of minerals. Sci Am 2010;302(3):58–65. 链接1

[15] Hazen RM. Paleomineralogy of the Hadean Eon: a preliminary list. Am J Sci 2013;313(9):807–43. 链接1

[16] Hazen RM. Mineral evolution, the Great Oxidation Event, and the rise of colorful minerals. Mineralog Record 2015;46(805–816):34. 链接1

[17] Hazen RM. An evolutionary system of mineralogy: proposal for a classification based on natural kind clustering. Am Mineral. In press.

[18] Hazen RM, Eldredge N. Themes and variations in complex systems. Elements 2010;6(1):43–6. 链接1

[19] Hazen RM, Ferry JM. Mineral evolution: mineralogy in the fourth dimension. Elements 2010;6(1):9–12. 链接1

[20] Golden J, McMillan M, Downs RT, Hystad G, Stein HJ, Zimmerman A, et al. Rhenium variations in molybdenite (MoS2): evidence for progressive subsurface oxidation. Earth Planet Sci Lett 2013;366:1–5. 链接1

[21] Grew ES, Hazen RM. Evolution of the minerals of beryllium. Stein 2013:4–19. 链接1

[22] Grew ES, Hazen RM. Beryllium mineral evolution. Am Mineral 2014;99(5–6): 999–1021. 链接1

[23] Krivovichev SV. Structural complexity of minerals: information storage and processing in the mineral world. Mineral Mag 2013;77(3):275–326. 链接1

[24] Krivovichev SV. Structural complexity of minerals and mineral parageneses: information and its evolution in the mineral world. In: Armbruster T, Danisi RM, editors. Highlights in mineralogical crystallography. Berlin/Boston: de Gruyter; 2015. p. 31–74. 链接1

[25] Grew ES, Dymek RF, De Hoog JCM, Harley SL, Boak JM, Hazen RM, et al. Boron isotopes in tourmaline from the 3.7–3.8 Ga Isua Belt, Greenland: sources for boron in Eoarchean continental crust and seawater. Geochim Cosmochim Acta 2015;163:156–77. 链接1

[26] Krivovichev SV, Krivovichev VG, Hazen RM. Structural and chemical complexity of minerals: correlations and time evolution. Eur J Mineral 2018;30(2):231–6. 链接1

[27] Liu C, Knoll AH, Hazen RM. Geochemical and mineralogical evidence that Rodinian assembly was unique. Nat Commun 2017;8(1):1950. 链接1

[28] Hystad G, Downs RT, Hazen RM. Mineral species frequency distribution conforms to a large number of rare events model: prediction of Earth’s missing minerals. Math Geosci 2015;47(6):647–61. 链接1

[29] Hystad G, Downs RT, Grew ES, Hazen RM. Statistical analysis of mineral diversity and distribution: Earth’s mineralogy is unique. Earth Planet Sci Lett 2015;426:154–7. 链接1

[30] Hystad G, Downs RT, Hazen RM, Golden JJ. Relative abundances for the mineral species on Earth: a statistical measure to characterize Earth-like planets based on Earth’s mineralogy. Math Geosci 2017;49(2):179–94. 链接1

[31] Hazen RM, Grew ES, Downs RT, Golden J, Hystad G. Mineral ecology: chance and necessity in the mineral diversity of terrestrial planets. Can Mineral 2015;53(2):295–323. 链接1

[32] Hazen RM, Hystad G, Downs RT, Golden J, Pires A, Grew ES. Earth’s ‘‘missing” minerals. Am Mineral 2015;100(10):2344–7. 链接1

[33] Hazen RM, Hummer DR, Hystad G, Downs RT, Golden JJ. Carbon mineral ecology: predicting the undiscovered minerals of carbon. Am Mineral 2016;101(4):889–906. 链接1

[34] Hazen RM, Hystad G, Golden JJ, Hummer DR, Liu C, Downs RT, et al. Cobalt mineral ecology. Am Mineral 2017;102(1):108–16. 链接1

[35] Grew ES, Krivovichev SV, Hazen RM, Hystad G. Evolution of structural complexity in boron minerals. Can Mineral 2016;54(1):125–43. 链接1

[36] Grew ES, Hystad G, Hazen RM, Krivovichev SV, Gorelova LA. How many boron minerals occur in Earth’s upper crust? Am Mineral 2017;102(8): 1573–87. 链接1

[37] Hazen RM, Ausubel J. On the nature and significance of rarity in mineralogy. Am Mineral 2016;101(6):1245–51. 链接1

[38] Liu C, Hystad G, Golden JJ, Hummer DR, Downs RT, Morrison SM, et al. Chromium mineral ecology. Am Mineral 2017;102(3):612–9. 链接1

[39] Liu C, Eleish A, Hystad G, Golden JJ, Downs RT, Morrison SM, et al. Analysis and visualization of vanadium mineral diversity and distribution. Am Mineral 2018;103(7):1080–6. 链接1

[40] Morrison SM, Liu C, Eleish A, Prabhu A, Li C, Ralph J, et al. Network analysis of mineralogical systems. Am Mineral 2017;102(8):1588–96. 链接1

[41] Downs RT. The RRUFF project: an integrated study of the chemistry, crystallography, Raman and infrared spectroscopy of minerals. In: Proceedings of the 19th General Meeting of the International Mineralogical Association; 2006 July 23–28; Kobe, Japan; 2006.

[42] Lehnert KA, Walker D, Sarbas B. EarthChem: a geochemistry data network. Geochim Cosmochim Acta 2007;71:A559. 链接1

[43] Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 2016;3:160018. 链接1

[44] Fox P, Hendler J. Changing the equation on scientific data visualization. Science 2011;331(6018):705–8. 链接1

[45] Hazen RM. Data-driven abductive discovery in mineralogy. Am Mineral 2014;99(11–12):2165–70. 链接1

[46] Papike JJ, editor. Planetary materials. Chantilly: Mineralogical Society of America; 1998. 链接1

[47] Morrison SM, Downs RT, Blake DF, Vaniman DT, Ming DW, Rampe EB, et al. Crystal chemistry of martian minerals from Bradbury Landing through Naukluft Plateau, Gale crater, Mars. Am Mineral 2018;103(6):857–71. 链接1

[48] Liu XM, Kah LC, Knoll AH, Cui H, Kaufman AJ, Shahar A, et al. Tracing Earth’s CO2 evolution using Zn/Fe ratios in marine carbonate. Geochem Perspect Lett 2016;2:24–34. 链接1

[49] Carroll SB. Chance and necessity: the evolution of morphological complexity and diversity. Nature 2001;409(6823):1102–9. 链接1

[50] Ma X, Hummer D, Golden JJ, Fox PA, Hazen RM, Morrison SM, et al. Using visualized exploratory data analysis to facilitate collaboration and hypothesis generation in cross-disciplinary research. ISPRS Int J Geoinf 2017;6(11):368. 链接1

[51] Otte E, Rousseau R. Social network analysis: a powerful strategy, also for the information sciences. J Inf Sci 2002;28(6):441–53. 链接1

[52] Abraham A, Hassanien AE, Snasel V, editors. Computational social network analysis: trends, tools and research advances. New York: Springer; 2010. 链接1

[53] Pinheiro CAR. Social network analysis in telecommunications. Hoboken: Wiley; 2011. 链接1

[54] Kadushin C. Understanding social networks. New York: Oxford University Press; 2012. 链接1

[55] Hwang N, Houghtalen R. Fundamentals of hydraulic engineering systems. Upper Saddle River: Prentice Hall; 1996. 链接1

[56] Guimerà R, Mossa S, Turtschi A, Amaral LAN. The worldwide air transportation network: anomalous centrality, community structure, and cities’ global roles. Proc Natl Acad Sci USA 2005;102(22):7794–9. 链接1

[57] Dong W, Pentland A. A network analysis of road traffic with vehicle tracking data. In: Proceedings of the American Association of Artificial Intelligence, Spring Symposium, Human Behavior Modeling; 2009 Mar 23–25; Palo Alto, CA, USA; 2009. p. 7–12.

[58] Pagani GA, Aiello M. The power grid as a complex network: a survey. Phys A 2013;392(11):2688–700. 链接1

[59] Amitai G, Shemesh A, Sitbon E, Shklar M, Netanely D, Venger I, et al. Network analysis of protein structures identifies functional residues. J Mol Biol 2004;344(4):1135–46. 链接1

[60] Banda-R K, Delgado-Salinas A, Dexter KG, Linares-Palomino R, Oliveira-Filho A, Prado D, et al. Plant diversity patterns in neotropical dry forests and their conservation implications. Science 2016;353(6306):1383–7. 链接1

[61] Corel E, Lopez P, Méheust R, Bapteste E. Network-thinking: graphs to analyze microbial complexity and evolution. Trends Microbiol 2016;24(3):224–37. 链接1

[62] Muscente AD, Prabhu A, Zhong H, Eleish A, Meyer MB, Fox P, et al. Quantifying ecological impacts of mass extinctions with network analysis of fossil communities. Proc Natl Acad Sci USA 2018;115(20):5217–22. 链接1

[63] Kolaczyk ED. Statistical analysis of network data. New York: Springer; 2009. 链接1

[64] Newman MEJ. Networks: an introduction. New York: Oxford University Press; 2013. 链接1

[65] Asratian AS, Denley TMJ, Häggkvist R. Bipartite graphs and their applications. New York: Cambridge University Press; 1998. 链接1

[66] Adomavicius G, Tuzhilin A. Context-aware recommender systems. In: Ricci F, Rokach L, Shapira B, Kantor PB, editors. Recommender systems handbook. Boston: Springer; 2011. p. 217–53. 链接1

[67] Ricci F, Rokach L, Shapira B. Introduction to recommender systems handbook. In: Ricci F, Rokach L, Shapira B, Kantor PB, editors. Recommender systems handbook. Boston: Springer; 2011. p. 1–35. 链接1

[68] Panniello U, Tuzhilin A, Gorgoglione M. Comparing context-aware recommender systems in terms of accuracy and diversity. User Model Useradapt Interact 2014;24(1–2):35–65. 链接1

[69] Gagné OC, Hawthorne FC. Bond-length distributions for ions bonded to oxygen: alkali and alkaline-earth metals. Acta Crystallogr B Struct Sci Cryst Eng Mater 2016;72(Pt 4):602–25. 链接1

[70] Gagné OC, Hawthorne FC. Bond-length distributions for ions bonded to oxygen: results for the non-metals and discussion of lone-pair stereoactivity and the polymerization of PO4. Acta Crystallogr B 2018;74:79–96. 链接1

[71] Gagné OC, Hawthorne FC. Bond-length distributions for ions bonded to oxygen: metalloids and post-transition metals. Acta Crystallogr B 2018;74: 63–78. 链接1

[72] Gagné OC, Hawthorne FC. Bond-length distributions for ions bonded to oxygen: results for the transition metals and discussion of d0 cations and the Jahn-Teller effect. Acta Cryst B 2018;74(Pt 1):79–96. 链接1

[73] Gagné OC. Bond-length distributions for ions bonded to oxygen: results for the lanthanides and actinides and discussion of the f-block contraction. Acta Crystallogr B 2018;74:49–62. 链接1

[74] Gagné OC, Mercier PHJ, Hawthorne FC. A priori bond-valence and bondlength calculations in rock-forming minerals. Acta Crystallogr B 2018;74: 470–82. 链接1

[75] Gagné OC, Hawthorne FC. Comprehensive derivation of bond-valence parameters for ion pairs involving oxygen. Acta Crystallogr B Struct Sci Cryst Eng Mater 2015;71(Pt 5):562–78. 链接1

[76] Schutt R, O’Neil C. Doing data science: straight talk from the frontline. New York: O’Reilly; 2013. 链接1

[77] Kitchin R. The data revolution: big data, open data, data infrastructures & their consequences. London: Sage; 2014. 链接1

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