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首页> 外文期刊>Journal of Geochemical Exploration: Journal of the Association of Exploration Geochemists >Geochemical patterns of schists from the Bushmanland Group:An artificial neural networks approach
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Geochemical patterns of schists from the Bushmanland Group:An artificial neural networks approach

机译:布什曼兰集团片岩的地球化学模式:人工神经网络方法

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The Mesoproterozoic Bushmanland Group is situated in the central region of the 1000 to 1200 Ma Namaqualand Metamorphic Complex (NMC).The NMC comprises a belt of highly deformed medium- to high-grade metamorphic rocks to the west of the Archean Kaapvaal Craton of southern Africa.The Bushmanland Group,one of the many supracrustal sequences that make up the NMC,is a metavolcano-sedimentary succession that hosts economically significant concentrations of sillimanite and base-metal sulfide deposits.The present investigation was carried out to study the geochemistry of a large set of representative samples of psammo-pelitic schists from the Bushmanland Group,which includes data from three different schist units:Namies Schist Formation,Shaft Schist Formation and Ore Equivalent Schist.The objective was three-fold:to test the lateral correlatability of these schist units as determined by field relationships,to identify the geochemical signature of the schists and to test the validity of an Artificial Neural Network approach as an exploration tool.Two multidimensional datasets,respectively comprising 10 major and 18 trace elements,were constructed using selected published schist analyses.Both schist datasets were analyzed using self-organizing neural maps for visualizing and clustering high-dimensional geochemical data.Geochemical differences between the various schists were visualized using colored two-dimensional maps that can be visually and quantitatively interpreted.The results of this study confirm the lateral correlatability of the schist units evaluated in this communication.It was also found that each schist unit or portions of them represent a distinct geochemical signature that is related to true lithological variations.The results show that the Artificial Neural Network approach can be used as a powerful tool for regional mineral exploration in poly-deformed and metamorphosed terrains where identification of stratigraphic units through lateral correlation by means of fieldwork and petrography remains highly speculative.
机译:中古生代布什曼兰群位于1000至1200 Ma Namaqualand变质复合体(NMC)的中部,NMC由南部非洲的太古宙Kaapvaal Craton以西的一带高度变形的中高品位变质岩带组成布什曼兰组是组成NMC的许多超壳层序列之一,是一种火山-火山沉积演替,其具有经济上重要的硅线石和贱金属硫化物矿床。本研究旨在研究大型矿物的地球化学。 Bushmanland集团的一组典型的轮生-片岩片岩样品,包括来自三个不同片岩单元的数据:纳米片岩片岩组,轴片岩片岩组和矿石当量片岩。目标是三方面的:测试这些片岩的横向相关性由田间关系确定的单位,以识别片岩的地球化学特征并测试Arti的有效性人工神经网络方法作为探索工具。使用选定的已发布片岩分析方法构建了两个多维数据集,分别包含10个主要元素和18个​​微量元素。使用自组织神经图对这两个片岩数据集进行了分析,以对高维地球化学数据进行可视化和聚类利用彩色的二维图可以可视化和定量地解释各个片岩之间的地球化学差异,这项研究的结果证实了在该通信中评估的片岩单元的横向相关性,还发现每个片岩单元或结果表明,人工神经网络方法可以用作在多变形和变质地形中进行区域矿物勘探的有力工具,在这种情况下可以通过横向识别地层单元相互关联实地考察和岩相学仍然是高度投机的。

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