华北多伦-固原探矿区火山型铀矿化地球化学特征的数据挖掘

IF 3.4 2区 地球科学 Q1 GEOCHEMISTRY & GEOPHYSICS
Zongqing Zhang , Zhirui Wang , Lixin Wang , Xiaopeng Zhang , Yang Liu , Qingli Zhang , Zicun Cao , Yang Zhang , Kaiguo Yang , Yang Zhou , Domenico Cicchella
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引用次数: 0

摘要

将先进的数据挖掘方法应用于各类地球化学数据,能够找出有效的成矿特征,从而揭示矿石成因并发现新矿物。但将数据挖掘方法应用于局部和区域尺度、沉积物和全岩多元素地球化学数据集的个别研究相对较少。在此,我们将数据挖掘方法,包括多元统计分析(主成分分析)、空间分析(趋势面分析)、无监督机器学习算法(K-均值聚类)、有监督算法(随机森林和深度神经网络)应用于多伦-固原探矿区的区域沉积物地球化学数据和局部岩石地球化学数据,通过分析以下特征来确定火山型铀矿化的地球化学特征:(1)代表性元素关联;(2)原生晕的轴向分带;(3)元素分布模式;(4)地壳结构(通过基于深度学习的预测性铪(Hf)同位素绘图)。主成分分析和随机森林的结果表明,来自已知矿区的样品(如张马泾和大观岭)具有较高的铪(Hf)同位素含量、主要成矿元素(铀和钼)、亲钙元素(银、汞、铅、锑和砷)、稀有和稀土元素(铍、锂、喇、铌和钇)、钨(W)、铋(Bi)以及成岩元素(SiO2、K2O、Na2O 和 Al2O3)的独特组合,与成矿区和贫瘠区的样品均有所不同。大观昌原生矿晕的轴向分带由超矿晕(稀土元素Th、Nb、Zr、Hf、Ga和Rb)、近矿晕(U、Mo、Pb、Zn、Cd和Sb)和亚矿晕(Li、Be、Sc、V、Cu、Sr、Cs、Ba、W和Bi)组成。此外,趋势面分析表明,在研究区域内,上、近、亚矿石元素的空间分布格局形成了西北走向,上矿石元素集中在东南部,亚矿石元素集中在西北部,近矿石元素介于两者之间。最后,基于深度学习的预测性铪(Hf)同位素图谱显示,除多伦西部、西南部和围场北部的局部地区外,多伦-固原找矿区内锆石εHf(t)平均值以负值为主,范围在-17至0之间。上述结果可能显示了火山岩型铀矿化的关键特征,包括地壳再加工形成的元辉或近辉碱性流纹岩、周围的地幔火成岩、近热源、伴生的热液矿床(如Ag、Au等),以及铀、钼和相关元素(特别是Th、W、Bi、Ag和Sb等)的异常富集。我们的研究将有效地为火山岩型铀矿床提供新的勘探地球化学指标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data mining for geochemical signatures of volcanic-type uranium mineralization, Duolun-Guyuan prospect, North China

Application of advanced data mining methods to various types of geochemical data is able to fingerprint valid signatures of mineralization, thus unveiling ore genesis and discovering new minerals. But individual studies that apply data mining methods to both local- and regional-scale, both sediment and whole-rock multi-element geochemical data sets are relatively scarce. Here, we applied data mining methods, including multivariate statistical analysis (principal component analysis), spatial analysis (trend surface analysis), unsupervised machine learning algorithm (K-means clustering), supervised algorithms (random forest and deep neural network) to both regional sediment geochemical and local lithogeochemical data from the Duolun-Guyuan prospect, in order to determine the geochemical signatures of volcanic-type uranium mineralization through characterizing: (1) representative element associations; (2) axial zonation of primary haloes; (3) element distribution patterns; and (4) crustal structures (via deep learning-based predictive hafnium (Hf) isotopic mapping). Results of principal component analysis and random forest show that samples from known ore districts (e.g., Zhangmajing and Daguanchang) exhibit a distinct combination of major ore-forming elements (U and Mo), chalcophile elements (Ag, Hg, Pb, Sb and As), rare and rare earth elements (Be, Li, La, Nb and Y), tungsten (W), bismuth (Bi), and rock-forming elements (SiO2, K2O, Na2O and Al2O3), differing from samples of both the mineralized and barren areas. The axial zonation of primary haloes in Daguanchang is comprised of supra-ore haloes (rare earth elements, Th, Nb, Zr, Hf, Ga and Rb), near-ore haloes (U, Mo, Pb, Zn, Cd and Sb), and sub-ore haloes (Li, Be, Sc, V, Cu, Sr, Cs, Ba, W and Bi). Moreover, trend surface analysis shows that in the study area, the spatial distribution pattern of the supra-, near-, and sub-ore elements forms a northwesterly alignment, with the supra-ore elements concentrated in the southeast, the sub-ore elements in the northwest, and the near-ore elements in between. Finally, deep learning-based predictive hafnium (Hf) isotopic mapping reveals that the Duolun-Guyuan prospect is dominated by negative mean zircon εHf(t) values ranging from −17 to 0, except for some local areas in the west and southwest of Duolun and the north of Weichang. The above results may indicate critical signatures of volcanic-type U mineralization, consisting of meta- or pera-luminous, alkaline rhyolite resulted from crustal reworking, surrounding mantle-derived igneous rocks, proximal heat source, accompanying epithermal deposits (e.g., Ag, Au, etc.), and anomalous concentrations of U, Mo and relevant elements particularly Th, W, Bi, Ag and Sb etc. Our study will effectively provide new exploration geochemical indicators of volcanic-type U deposit.

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来源期刊
Journal of Geochemical Exploration
Journal of Geochemical Exploration 地学-地球化学与地球物理
CiteScore
7.40
自引率
7.70%
发文量
148
审稿时长
8.1 months
期刊介绍: Journal of Geochemical Exploration is mostly dedicated to publication of original studies in exploration and environmental geochemistry and related topics. Contributions considered of prevalent interest for the journal include researches based on the application of innovative methods to: define the genesis and the evolution of mineral deposits including transfer of elements in large-scale mineralized areas. analyze complex systems at the boundaries between bio-geochemistry, metal transport and mineral accumulation. evaluate effects of historical mining activities on the surface environment. trace pollutant sources and define their fate and transport models in the near-surface and surface environments involving solid, fluid and aerial matrices. assess and quantify natural and technogenic radioactivity in the environment. determine geochemical anomalies and set baseline reference values using compositional data analysis, multivariate statistics and geo-spatial analysis. assess the impacts of anthropogenic contamination on ecosystems and human health at local and regional scale to prioritize and classify risks through deterministic and stochastic approaches. Papers dedicated to the presentation of newly developed methods in analytical geochemistry to be applied in the field or in laboratory are also within the topics of interest for the journal.
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