共查询到17条相似文献，搜索用时 390 毫秒
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磁铁矿广泛分布在岩浆、热液及沉积等各类矿床中,其地球化学元素组成往往受温度、氧逸度等物理化学条件的影响,能反映矿床形成环境并指示矿床成因类型,是一种重要的勘查指示矿物。自20世纪60年代以来,磁铁矿的主微量元素数据被用来构建不同的判别图,试图来区分矿床的成因类型。然而,由于矿床成因类型的多样性以及同一类型矿床的磁铁矿的主微量元素地球化学组成的复杂性,以往基于少数磁铁矿的主微量元素地球化学成分构建的矿床成因类型判别图存在一定的局限性。基于此,本文收集了前人发表在国内外期刊上的主要矿床类型的磁铁矿的元素地球化学数据(7 388条),初步构建了基于电子探针(EPMA)和激光剥蚀-电感耦合等离子体质谱(LA-ICP-MS)磁铁矿元素地球化学大数据集,建立了基于随机森林算法的矿床成因分类模型,并对磁铁矿主微量元素在矿床成因分类中的重要性做出排序。研究结果表明,基于磁铁矿大数据和机器学习算法构建的判别模型,能有效区分主要矿床类型,整体分类准确度高达95%。由于LA-ICP-MS磁铁矿数据集的测试元素多,分析精度高,使得基于LA-ICP-MS磁铁矿数据集的矿床成因分类模型精度高于基于EPMA数据集,表明磁铁矿中元素种类多少和数据测试精度影响矿床成因分类精度。同时,研究发现V元素在矿床成因分类过程中起到了较为重要的作用。此外,基于大数据和机器学习建立的判别模型对新的磁铁矿数据进行测试,可给出该数据属于每种矿床类型的概率,能有效判别矿床成因类型。 相似文献
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锆石是在自然界中多种温压条件下能够稳定保存,并记录原岩年龄信息的副矿物。锆石微量元素能完整记录地质演化过程信息。通过微量元素分析锆石成因的研究已久,通常利用Th-U图解和LaN-(Sm/La)N图解等二元图解对锆石进行分类研究。然而,随着锆石研究的深入,以及二元图解无法呈现数据高维度信息的局限性,传统图解已经不能满足对锆石类型进行准确判别,且对已知类型的锆石出现判定偏差。因此,本文将地质大数据与机器学习相结合,训练出高维度锆石成因分类器。文中收集了3 498条不同成因类型的锆石微量元素数据,并通过测试和运用随机森林、支持向量机、人工神经网络和k近邻等4种机器学习算法,最终得出准确率为86.8%的线性支持向量机锆石成因分类器,用于锆石类型的判定与预测。这项工作为锆石分类研究提供了更高维度的判别手段,极大提高了微量元素分析成因结果的精度。将锆石微量元素数据与机器学习方法相结合,是大数据分析与机器学习技术在地球化学研究中的积极探索。 相似文献
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磷灰石广泛分布于火成岩、沉积岩和变质岩中,是一种常见的、包含丰富微量元素的副矿物。磷灰石晶格可容纳丰富的微量元素,且因其形成的物理化学条件不同会表现出差异明显的微量元素特征。利用磷灰石微量元素特征可以追踪物质来源和演化。现在常用的方法是利用磷灰石的微量元素绘制二元判别图解,经典判别图解包括Sr-Y、Sr-Mn、Y-(Eu/Eu^(*))和(Ce/Yb)_(N)-REE图解。随着微区测试技术发展,磷灰石微量元素数据日渐丰富,同时由于磷灰石化学成分的复杂性,传统图解已逐渐无法有效利用这些数据所携带的信息,进而无法准确判别其生成环境。建立能准确判别磷灰石物源的新型判别图解故而迫切。近年来,磷灰石微量元素数据的大量积累,为运用以大数据为依托,准确判别磷灰石物源奠定了数据基础。本研究将大数据技术与地球化学数据相结合,共收集整理了1925个代表性磷灰石测试点的微量元素数据,对富碱性火成岩、超镁铁质岩石、镁铁质火成岩、长英质花岗岩、中-低级变质岩、高级变质岩六种类型中磷灰石微量元素数据进行穷举端元处理,共获得7140个磷灰石物源判别图解端元组合,在轮廓系数限定下,进一步有效筛选并提取出能判别磷灰石物源类型的最优图解端元。本文构建了Eu/Y-Ce磷灰石判别新图解,相较于之前的磷灰石判别图解,其涵盖了更全面的物源类型,可以更准确地判别源区类型。 相似文献
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通过对赣中相山地区前寒武纪正、副角闪岩矿物学、岩石-地球化学特征系统对比研究,得出角闪岩成因判别标志的几点新认识:①提出了识别正、副角闪岩的角闪石矿物学标志;②阐明了区分正、副角闪岩的稀土元素地球化学特征;③对角闪岩微量元素地球化学特征提出采用模糊聚类分析与蛛网图相结合进行成因判别的有效方法;④提出综合应用15种元素对的比值(Ti/V,Ce/Yb,La/Yb,Th/Yb,La/Sm,La/Nb,Ce/Y,Rb/Sr,Nb/Y,Sm/Nd,Sr/Y,Sr/Ba,Zr/Y,Ti/Zr,Ti/Y)作为判别角闪岩成因的标志. 相似文献
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区分不同构造环境中岩浆作用是认识地幔中岩浆形成过程的基本手段。目前较为成熟的是利用全岩去区分判别,而利用造岩矿物去判别构造环境、演绎岩浆演化的研究还不够深入。本文运用机器学习的方法,以全球新生代大洋中脊、洋岛以及岛弧构造背景中的镁铁质-超镁铁质岩浆岩中单斜辉石的地球化学数据为研究对象,试图区分这3 种不同构造环境的单斜辉石。通过机器学习方法中K-邻近（K-Nearest Neighbor,KNN）和随机森林（Random Forest,RF）的计算和比较,认为RF 是一种有效的地球化学区分方法,它的结果不仅可用来判别构造环境,同时还能够提取特征元素。同时我们发现,在镁铁质-超镁铁质岩浆岩单斜辉石构造环境判别图解中,Rb、La、Ba、Cr、Sr、Yb、V、Ti、Nd、Eu、Gd等微量元素具有较高的贡献率,而主量元素贡献率较低。在此基础上,我们结合前人的对单斜辉石的构造环境判别图的研究成果,提出几个判别效果较好的判别图解。但是整个研究由于缺少进一步可视化的成果,限制了机器学习方法的推广,这也是今后需要进一步研究的课题。 相似文献
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华南钦杭结合带燕山期岩浆活动异常活跃,且具有较明显的成矿专属性。近年来微区测试技术日益成熟,积累了大量锆石微量数据。通过全体数据挖掘的思维方法,对前人发表的数据进行了进一步数据挖掘,利用锆石稀土元素对岩体成矿潜力进行判别,探讨有效的找矿地球化学标志。利用Python语言编程,对采用的13种稀土元素及元素比值进行穷举式组合,获得了4095个二元图解及121485个三元图解,并设计筛选算法,自动筛选出能有效区分锆石母岩成矿类型的图解。结果表明,锆石稀土元素含量及比值图解对不同成矿类型岩体的区分程度各异:与Ce、Eu有关的地球化学指标可以较清晰地对斑岩铜矿和钨锡(锡)矿床进行判别,这可能与岩体的氧逸度和含水量有关。此外,还挖掘出一些新的元素组合图解,如Dy/Lu-Er/Lu、Gd/Dy-Er/Yb等,可以有效区分岩体成矿类型,其隐含的地球化学机制尚待进一步解释。地球化学数据挖掘结果可以作为找矿标志使用,为华南燕山期岩浆-热液矿床研究及找矿勘查提供了科学依据,也是大数据技术在矿床学方面应用研究的积极探索。 相似文献
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岩石与矿物的地球化学成分数据具有高维度特征。传统的岩矿地球化学成分研究主要采用二元/三元图解判别法,准确率不高,在数理统计方法上有欠缺。机器学习方法非常适用于对大样本高维度的岩矿成分数据进行数理统计处理。本文在介绍机器学习常见算法基本原理的基础上,总结近5年来国内外学者将机器学习方法应用于岩石矿物成分数据研究的实例,包括：① 根据矿物成分溯源其母岩（源岩）、判别矿床类型,② 新生代火山岩溯源,③ 判别变质岩原岩,④ 依据岩浆岩成分判别大地构造环境等。已有的研究实例显示,机器学习方法的准确度明显优于传统的低维度判别法。机器学习本质是分析大样本数据的高维度变量之间的相关、归类等多元统计问题。推广机器学习的应用需要建设开放获取（Open Access）的矿物、岩石成分数据库,同时全面实施开放研究（Open Research）的发表策略。 相似文献
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笔者总结了福建余朋—南口地区火把山萤石矿床的地质特征, 并通过萤石的微量元素及稀土元素地球化学特征, 探讨了矿床的成因及成矿物质、流体来源。萤石矿石的微量元素及稀土元素结果显示: 火把山萤石呈中等Eu负异常及弱Ce异常, 稀土元素配分模式具有轻稀土富集型及轻稀土弱亏损的平缓型等两种曲线型式。根据萤石矿石特征、微量元素、稀土元素等特征及Y/Ho-La/Ho图解, 可知萤石存在至少三期成矿地质作用。依据M?ller的Tb/La-Tb/Ca成因判别图解, 结合常口萤石矿床的萤石及围岩的稀土元素特征, 认为火把山萤石成矿流体一方面与常口大型萤石矿床受同一成矿流体场控制, 并在有利的部位富集成矿; 另一方面, 成矿热液流体流经下峰岩组发生了淋滤、萃取、交换作用, 形成了不同地球化学特征的另一成矿期次的萤石。综合分析认为, 火把山萤石矿床为受断裂控制的大气降水成因的中低温热液充填矿床。 相似文献
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《地学前缘(英文版)》2023,14(4):101580
Due to the combined influences such as ore-forming temperature, fluid and metal sources, sphalerite tends to incorporate diverse contents of trace elements during the formation of different types of Lead-zinc (Pb-Zn) deposits. Therefore, trace elements in sphalerite have long been utilized to distinguish Pb-Zn deposit types. However, previous discriminant diagrams usually contain two or three dimensions, which are limited to revealing the complicated interrelations between trace elements of sphalerite and the types of Pb-Zn deposits. In this study, we aim to prove that the sphalerite trace elements can be used to classify the Pb-Zn deposit types and extract key factors from sphalerite trace elements that can discriminate Pb-Zn deposit types using machine learning algorithms. A dataset of nearly 3600 sphalerite spot analyses from 95 Pb-Zn deposits worldwide determined by LA-ICP-MS was compiled from peer-reviewed publications, containing 12 elements (Mn, Fe, Co, Cu, Ga, Ge, Ag, Cd, In, Sn, Sb, and Pb) from 5 types, including Sedimentary Exhalative (SEDEX), Mississippi Valley Type (MVT), Volcanic Massive Sulfide (VMS), skarn, and epithermal deposits. Random Forests (RF) is applied to the data processing and the results show that trace elements of sphalerite can successfully discriminate different types of Pb-Zn deposits except for VMS deposits, most of which are falsely distinguished as skarn and epithermal types. To further discriminate VMS deposits, future studies could focus on enlarging the capacity of VMS deposits in datasets and applying other geological factors along with sphalerite trace elements when constructing the classification model. RF’s feature importance and permutation feature importance were adopted to evaluate the element significance for classification. Besides, a visualized tool, t-distributed stochastic neighbor embedding (t-SNE), was used to verify the results of both classification and evaluation. The results presented here show that Mn, Co, and Ge display significant impacts on classification of Pb-Zn deposits and In, Ga, Sn, Cd, and Fe also have relatively important effects compared to the rest elements, confirming that Pb-Zn deposits discrimination is mainly controlled by multi-elements in sphalerite. Our study hence shows that machine learning algorithm can provide new insights into conventional geochemical analyses, inspiring future research on constructing classification models of mineral deposits using mineral geochemistry data. 相似文献
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Distribution and petrogenetic behaviour of trace elements in granitic pegmatite quartz from South Norway 总被引：1，自引：0，他引：1
The present study documents that the trace-element distribution in granitic quartz is highly sensitive to CAFC processes in granitic melts. Igneous quartz efficiently records both the origin and the evolution of the granitic pegmatites. Aluminium, P, Li, Ti, Ge and Na in that order of abundance, comprises >95% of the trace elements. Most samples feature >1 ppm of any of these elements. The remnant 5% includes K, Fe, Be, B, Ba and Sr whereas the other elements are present at concentrations lower than the detection limit. Potassium, Fe, Be and Ti are relatively compatible hence obtain the highest concentrations in early formed quartz. Phosphorous, Ge, Li and Al are relatively incompatible and generally obtain the highest concentrations in quartz that formed at lower temperatures from more evolved granitic melts. The Ge/Ti, the Ge/Be, the P/Ge and the P/Be ratios of quartz are strongly sensitive to the origin and evolution of the granitic melts and similarly the Rb/Sr and the Rb/K ratios of K-feldspars may be utilised in petrogenetic interpretations. However, the quartz trace element ratios are better at distinguishing similarities and differences in the origin and evolution of granitic melts. After evaluating the different trace element ratios, the Ge/Ti ratio appears to be most robust during subsolidus processes in the igneous systems, hence probably should be the preferred ratio for analysing and understanding petrogenetic processes in granitic igneous rocks.Editorial responsibility: J. Hoefs 相似文献
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不同类型铀矿床的沥青铀矿/晶质铀矿具有不同的稀土元素组成,其组成可作为判别铀矿床类型的重要指标。采用基于Python语言的主成分分析（principal component analysis,PCA）与支持向量机（support vector machines,SVM）结合的分类模型,对收集到的全球已知6种类型铀矿床的216组沥青铀矿/晶质铀矿稀土元素数据进行研究。以216组数据为训练集,通过数据清洗、特征缩放、PCA特征提取、网格搜索和交叉验证参数寻优构建SVM分类模型,对24组同变质型胡家峪晶质铀矿进行智能识别。研究结果显示：仅使用稀土元素的14维训练集最优模型判定胡家峪晶质铀矿类型的测试准确率为0.4%;由稀土元素、稀土总量、轻重稀土比、铕异常组成的17维训练集最优模型的测试准确率为75.0%,较14维训练集提高74.6%,模型泛化能力强;而通过传统稀土元素配分曲线、w（ΣREE）-（LREE/HREE）N图解不能有效判定胡家峪晶质铀矿类型。本次研究表明,PCA-SVM算法对增有传统稀土判别指标数据集进行挖掘可有效厘定铀氧化物成因类型,效果明显优于单纯的稀土元素数据集以及传统的稀土配分曲线、w（ΣREE）-（LREE/HREE）N图解。 相似文献
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判别岩浆岩产出的构造环境已经成为岩石学、地球化学及其地球动力学研究的重要内容。作为岩浆岩中的一种喷出岩,玄武岩被视为判别构造环境的最佳成员。对其中单斜辉石的研究,由于其数据本身的利用程度有限而效果欠佳。理论上,不同构造环境的辉长岩也会存在一定差异。为此,利用机器学习算法研究全球新生代辉长岩的单斜辉石势在必行。本文主要针对岛弧(IAB)、洋岛(OIB)及大洋中脊(MORB)3种构造背景辉长岩的单斜辉石进行特征筛选和数据分类。从GEOROC数据库中,经数据收集与清洗,我们分别获得岛弧辉长岩单斜辉石数据385条,洋岛辉长岩单斜辉石数据756条,大洋中脊辉长岩单斜辉石数据5 500条。其中绝大部分为主量元素数据,其余为微量元素数据。在特征提取部分,我们选用卡方检验判断特征独立性,F检验估计两个随机变量之间的线性依赖程度,互信息法捕获其他种类的统计相关性。3种检验方法互相印证,得出了统计学可靠的重要分类特征。在数据分类过程中,本文对比了K-近邻、决策树和支持向量机3种主流机器学习分类算法在辉长岩数据上的表现。研究表明,对于上述3种构造背景,Al2O3、TiO2为最有区分度的辉长岩单斜辉石主量元素成分,Sr为最有区分度的微量元素成分。另外,对于3种构造背景的辉长岩单斜辉石主量元素和微量元素数据,机器学习模型分类准确率均达94%。 相似文献
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作为战略性矿产资源之一,高纯石英已广泛应用于集成电路、半导体芯片、太阳能等高新技术产业中,但是能够生产高纯石英的原料矿床极为稀缺,我国尤为紧缺高纯石英原料矿。鄂东南地区是湖北省脉石英矿床的主要分布区。本文针对鄂东南付家山脉石英矿床,通过光学显微镜、扫描电子显微镜观察了脉石英的脉石矿物类型和包裹体特征,采用电感耦合等离子发射光谱法(ICP-OES)对原矿进行了微量元素分析,旨在获得付家山脉石英矿床的杂质元素特征,进而评价矿床用作高纯石英原料的潜力。结果表明,付家山脉石英矿石SiO2含量大于99.95%,杂质元素主要为Al、K、Fe、Ti、Ca等,脉石矿物主要有白云母、钾长石、铁氧化物等,流体包裹体较为发育。杂质元素分析结果表明,付家山脉石英原矿质量达到低端高纯石英标准,经传统工艺提纯后,可能具有生产中高端高纯石英的潜力。 相似文献
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Trace element chemistry of pyrite: A useful guide to the occurrence of sulfide base metal mineralization 总被引：1，自引：0，他引：1
F. Ivor Roberts 《Journal of Geochemical Exploration》1982,17(1):49-62
Trace element contents for pyrite from a range of sulfide mineral occurrences in the Kangiara region, eastern Australia, illustrate two main groups of pyrite. The first group, with higher Ag, Cu, Pb and Mo contents, corresponds to samples from sulfide base metal deposits and the second group, with higher Mn, Ti and Ni contents, contains samples from skarn mineralization, volcanic rocks and quartz veins. The model proposed for the development of pyrite in the Kangiara region is that the first group was formed from base metal-bearing solutions, while the second group reflects diagenetic pyrite and metamorphic pyrite. Thus, the pyrite trace element chemistry may provide a means of distinguishing types of mineral occurrences, in particular, those containing significant base metal mineralization. 相似文献
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The Taihe, Baima, Hongge, Panzhihua and Anyi intrusions of the Emeishan Large Igneous Province (ELIP), SW China, contain large magmatic Fe–Ti–(V) oxide ore deposits. Magnetites from these intrusions have extensive trellis or sandwich exsolution lamellae of ilmenite and spinel. Regular electron microprobe analyses are insufficient to obtain the primary compositions of such magnetites. Instead, laser ablation ICP-MS uses large spot sizes (~ 40 μm) and can produce reliable data for magnetites with exsolution lamellae. Although magnetites from these deposits have variable trace element contents, they have similar multi-element variation patterns. Primary controls of trace element variations of magnetite in these deposits include crystallography in terms of the affinity of the ionic radius and the overall charge balance, oxygen fugacity, magma composition and coexisting minerals. Early deposition of chromite or Cr-magnetite can greatly deplete magmas in Cr and thus Cr-poor magnetite crystallized from such magmas. Co-crystallizing minerals, olivine, pyroxenes, plagioclase and apatite, have little influence on trace element contents of magnetite because elements compatible in magnetite are incompatible in these silicate and phosphate minerals. Low contents and bi-modal distribution of the highly compatible trace elements such as V and Cr in magnetite from Fe–Ti oxide ores of the ELIP suggest that magnetite may not form from fractional crystallization, but from relatively homogeneous Fe-rich melts. QUILF equilibrium modeling further indicates that the parental magmas of the Panzhihua and Baima intrusions had high oxygen fugacities and thus crystallized massive and/or net-textured Fe–Ti oxide ores at the bottom of the intrusive bodies. Magnetite of the Taihe, Hongge and Anyi intrusions, on the other hand, crystallized under relatively low oxygen fugacities and, therefore, formed net-textured and/or disseminated Fe–Ti oxides after a lengthy period of silicate fractionation. Plots of Ge vs. Ga + Co can be used as a discrimination diagram to differentiate magnetite of Fe–Ti–(V) oxide-bearing layered intrusions in the ELIP from that of massif anorthosites and magmatic Cu–Ni sulfide deposits. Variable amounts of trace elements of magmatic magnetites from Fe–Ti–(P) oxide ores of the Damiao anorthosite massif (North China) and from Cu–Ni sulfide deposits of Sudbury (Canada) and Huangshandong (northwest China) demonstrate the primary control of magma compositions on major and trace element contents of magnetite. 相似文献