A Preliminary Study on the Temporal Distribution Characteristics of Iron Minerals and Prediction of the Deep-Time Evolution

doi:10.19657/j.geoscience.1000-8527.2025.040

Abstract

Abstract:

Minerals are important recorders of Earth’s evolution over billions of years. Data-driven research in mineralogy makes it easier to understand the underlying laws and driving mechanisms of the evolutionary processes of Earth. Iron plays a critical role in the matter and energy cycles, evolution of life, and environmental remediation, and is also recognized as a vitally important ‘oxygen fugacity buffer’ during the evolution of Earth surficial system. A total amount of 949 iron minerals are collected in this study. And by exploring their evolutionary patterns and distributional characteristics, a preliminary framework is constructed to tap into the intrinsic links between the evolution of deep-time iron minerals, evolution of Earth’s environment and evolution of life. The results reveal that the diversity of iron minerals has expanded episodically throughout geological history, and the peak of the growth stage coincides with the period of supercontinent collisions. The processes of plate motion, atmospheric oxygenation, and life’s metabolic activities have combined to promote the evolution of iron minerals in a more complex and diverse direction. Furthermore, back propagation neural network (BPNN), random forest (RF) and support vector regression (SVR), are used to establish and predict the evolution model of iron minerals since 4.0 Ga. The results show that RF is more appropriate to predict the evolution trend more accurately with stronger generalization ability than BPNN and SVR.

Key words: iron mineral, mineral evolution, machine learning, evolutionary feature

CLC Number:

HUANG Siyi, XU Jingbo, WEI Linhong, CAI Yuanfeng. A Preliminary Study on the Temporal Distribution Characteristics of Iron Minerals and Prediction of the Deep-Time Evolution[J]. Geoscience, 2025, 39(03): 552-559.

Figures/Tables 5

References 44

[1]	郭承基, 王中刚. 矿物演化[J]. 矿物学报, 1981, 1(1): 1-9.
[2]	陈光远. 成因矿物学与找矿矿物学发展现状[J]. 矿物岩石地球化学通报, 1987, 6(2): 68-70.
[3]	HAZEN R M, PAPINEAU D, BLEEKER W, et al. Mineral evolution[J]. American Mineralogist, 2008, 93(11/12): 1693-1720.
[4]	HAZEN R M, MORRISON S M. On the paragenetic modes of minerals: A mineral evolution perspective[J]. American Mineralogist, 2022, 107(7): 1262-1287.
[5]	KENDALL B, ANBAR A D, KAPPLER A, et al. The global iron cycle[M]. KNOLL A H, CANFIELD D E, KONHAUSER K O. Fundamentals of geobiology. America: Blackwell Publishing Ltd. 2012: 65-92.
[6]	WADE J, BYRNE D J, BALLENTINE C J, et al. Temporal variation of planetary iron as a driver of evolution[J]. Proceedings of the National Academy of Sciences of the United States of America, 2021, 118(51): e2109865118.
[7]	KAPPLER A, BRYCE C, MANSOR M, et al. An evolving view on biogeochemical cycling of iron[J]. Nature Reviews Microbiology, 2021, 19(6): 360-374. DOI PMID
[8]	吴琪, 仇巍巍, 江拓, 等. 中国铁矿勘探开发及供需形势[J]. 地质论评, 2024, 70(A1): 111-112.
[9]	WANG P, FU F G, LIU T G. A review of the new multifunctional nano zero-valent iron composites for wastewater treatment: Emergence, preparation, optimization and mechanism[J]. Chemosphere, 2021, 285(0): 131435.
[10]	GONG Y S, WANG Y, LIN N P, et al. Iron-based materials for simultaneous removal of heavy metal(loid)s and emerging organic contaminants from the aquatic environment: Recent advances and perspectives[J]. Environmental Pollution, 2022, 299: 118871.
[11]	HAZEN R M, DOWNS R T, ELEISH A, et al. Data-driven discovery in mineralogy: Recent advances in data resources, analysis, and visualization[J]. Engineering, 2019, 5(3): 397-405.
[12]	LU A H. Mineral evolution heralds a new era for mineralogy[J]. American Mineralogist, 2022, 107(7): 1217-1218.
[13]	WANG C S, HAZEN R M, CHENG Q M, et al. The deep-time digital earth program: data-driven discovery in geosciences[J]. National Science Review, 2021, 8(9): 156-166.
[14]	李晓彦, 张超. 大数据和机器学习在矿物学研究中的应用[J]. 矿物岩石地球化学通报, 2023, 42(2): 253-266, 252.
[15]	CHEN G X, CHENG Q M, LYONS T W, et al. Reconstructing Earth’s atmospheric oxygenation history using machine learning[J]. Nature Communications, 2022, 13(1): 5862.
[16]	KRISSANSEN-TOTTON J, ARNEY G N, CATLING D C. Constraining the climate and ocean pH of the early Earth with a geological carbon cycle model[J]. Proceedings of the National Academy of Sciences of the United States of America, 2018, 115(16): 4105-4110.
[17]	LI J, CHENG J H, SHI J Y, et al. Brief introduction of back propagation (BP) neural network algorithm and its improvement[M]. DAVID J, SALLY L. Advances in computer science and information engineering: Volume 2. 2012: 553-558.
[18]	沈花玉, 王兆霞, 高成耀, 等. BP神经网络隐含层单元数的确定[J]. 天津理工大学学报, 2008, 24 (5): 13-15.
[19]	BREIMAN L. Random forests[J]. Machine Learning, 2001, 45(1): 5-32.
[20]	KHADEMIAN M, IMLAY J A. How microbes evolved to tolerate oxygen[J]. Trends in Microbiology, 2021, 29(5): 428-440. DOI PMID
[21]	LYONS T W, DIAMOND C W, PLANAVSKY N J, et al. Oxygenation, life, and the planetary system during Earth’s middle history: An overview[J]. Astrobiology, 2021, 21(8): 906-923.
[22]	HAZEN R M. Paleomineralogy of the Hadean Eon: A preliminary species list[J]. American Journal of Science, 2013, 313(9): 807-843.
[23]	TANG Q, ZHENG W T, ZHANG S H, et al. Quantifying the global biodiversity of Proterozoic eukaryotes[J]. Science, 2024, 386: eadm9137.
[24]	FAN J X, SHEN S Z, ERWIN D H, et al. A high-resolution summary of Cambrian to Early Triassic marine invertebrate biodiversity[J]. Science, 2020, 367(6475): 272-277.
[25]	HAZEN R M, LIU X-M, DOWNS R T, et al. Mineral evolution: Episodic metallogenesis, the supercontinent cycle, and the coevolving geosphere and biosphere[M]. KELLEY K D, GOLDEN H C. Building exploration capability for the 21st century. Littleton: Society of Economic Geologists. 2014: 1-15.
[26]	翟明国. 华北前寒武纪成矿系统与重大地质事件的联系[J]. 岩石学报, 2013, 29(5): 1759-1773.
[27]	BEKKER A, SLACK J F, PLANAVSKY N, et al. Iron formation: The sedimentary product of a complex interplay among mantle, tectonic, oceanic, and biospheric processes[J]. Economic Geology, 2010, 105(3): 467-508.
[28]	KONHAUSER K O, PLANAVSKY N J, HARDISTY D S, et al. Iron formations: A global record of Neoarchaean to Palaeoproterozoic environmental history[J]. Earth-Science Reviews, 2017, 172(1): 140-177.
[29]	KLEIN C. Some Precambrian banded iron-formations (BIFs) from around the world: Their age, geologic setting, mineralogy, metamorphism, geochemistry, and origins[J]. American Mineralogist, 2005, 90(10): 1473-1499.
[30]	SCHWERTMANN U, MURAD E. Effect of pH on the formation of goethite and hematite from ferrihydrite[J]. Clays and Clay Minerals, 1983, 31(4): 277-284.
[31]	OHMOTO H. Nonredox transformations of magnetite-hematite in hydrothermal systems[J]. Economic Geology, 2003, 98(1): 157-161.
[32]	宋颜, 董少春, 胡欢, 等. 基于大数据的铌钽矿物全球时空分布特征分析[J]. 地学前缘, 2023, 30(5): 197-204. DOI
[33]	唐鸣昊, 滕辉, 陆现彩, 等. 砷矿物演化与矿物生态初探[J]. 矿物岩石地球化学通报, 2024, 43(2): 418-427.
[34]	ZHUANG Z Y, ZHANG Y, LI Y, et al. Evolutionary dynamics of redox-sensitive minerals reveal details and possible regulatory mechanisms of Earth’s oxygenation events[J]. Earth and Planetary Science Letters, 2024, 626: 118528.
[35]	TANG H S, CHEN Y J. Global glaciations and atmospheric change at ca. 2.3 Ga[J]. Geoscience Frontiers, 2013, 4(5): 583-596.
[36]	HOFFMANN K H, CONDON D J, BOWRING S A, et al. U-Pb zircon date from the Neoproterozoic Ghaub Formation, Namibia: Constraints on Marinoan Glaciation[J]. Geology, 2004, 32(9): 817-820.
[37]	ZHANG S H, JIANG G Q, HAN Y G. The age of the Nantuo Formation and Nantuo glaciation in South China[J]. TERRA NOVA, 2008, 20(4): 289-294.
[38]	MACDONALD F A, SCHMITZ M D, CROWLEY J L, et al. Calibrating the Cryogenian[J]. Science, 2010, 327: 1241-1243. DOI PMID
[39]	赵彦彦, 郑永飞. 全球新元古代冰期的记录和时限[J]. 岩石学报, 2011, 27(2): 545-565.
[40]	MIAO L Y, YIN Z J, KNOLL A H, et al. 1.63-billion-year-old multicellular eukaryotes from the Chuanlinggou Formation in North China[J]. Science advances, 2024, 10(4): eadk3208.
[41]	ILBERT M, BONNEFOY V. Insight into the evolution of the iron oxidation pathways[J]. Biochimica et Biophysica Acta-Bioenergetics, 2013, 1827(2): 161-175.
[42]	王芙仙, 郑世玲, 邱浩, 等. 铁还原细菌Shewanella oneidensis MR-4诱导水合氧化铁形成蓝铁矿的过程[J]. 微生物学报, 2018, 58(4): 573-583.
[43]	张志飞, 刘璠, 梁悦, 等. 寒武纪生命大爆发与地球生态系统起源演化[J]. 西北大学学报(自然科学版), 2021, 51(6): 1065-1106.
[44]	朱永官, 段桂兰, 陈保冬, 等. 土壤-微生物-植物系统中矿物风化与元素循环[J]. 中国科学(地球科学), 2014, 44(6): 1107-1116.

参数	单位	最大值	最小值	参考文献
大气O₂含量[lg(pO₂)]	PAL	-0.1811	-7.3256	[15]
大气CO₂含量	bar	0.2917	0.0003	[16]
海洋pH		8.20	6.64	[16]
地表温度	K	292.18	283.97	[16]
深海温度	K	279.81	271.14	[16]

参数	单位	最大值	最小值	参考文献
大气O₂含量[lg(pO₂)]	PAL	-0.1811	-7.3256	[15]
大气CO₂含量	bar	0.2917	0.0003	[16]
海洋pH		8.20	6.64	[16]
地表温度	K	292.18	283.97	[16]
深海温度	K	279.81	271.14	[16]

模型	参数	最优值
BPNN	激活函数	Sigmoid
	隐藏层	1
	隐藏层神经元节点数	12
RF	决策树数目	246
	决策树最大深度	50
	最小叶子节点样本数	4
	内部节点再划分所需最小样本数	20
	分裂考虑最大特征数	3
SVR	核函数	RBF
	惩罚因子C	223.26
	核函数系数	0.49

模型	参数	最优值
BPNN	激活函数	Sigmoid
	隐藏层	1
	隐藏层神经元节点数	12
RF	决策树数目	246
	决策树最大深度	50
	最小叶子节点样本数	4
	内部节点再划分所需最小样本数	20
	分裂考虑最大特征数	3
SVR	核函数	RBF
	惩罚因子C	223.26
	核函数系数	0.49

模型	BPNN	RF	SVR
R²	0.99904	0.99904	0.99898
RMSE	7.3719	7.3603	7.6073
MAPE	2.4136	1.6361	7.3626