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Integrating Geochemical and Geophysical Information to Improve Geological Mapping in Northeast China: A Data Transferring Technology for Characterization and Classification

机译:整合地球化学和地球物理信息,提高中国东北地质映射:数据传输技术,用于表征和分类

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In mainland China, there are large surface areas covered with shallow overburden (Regolith thickness < 100 m). Geological mapping is difficult here as there is not enough exposed bedrock. In this paper, we transfer geochemical and geophysical data to characteristics, and use it in geological mapping to identify the underlying bedrock. Above all, we define the "represent area of one geochemical sample" as one "statistical unit". All geophysical data in each unit (hundreds of data) are analyzed generating statistical parameters, such as mode, interquartile range, frequency, skewness, and kurtosis, which are combined together to describe the characteristics of rocks in this area. As for the geochemical data in each unit, some rock-forming elements are screened to build new parameters, such as SiO2, Na2O + K2O, CaO, Al2O3, FeO + MgO. All geophysical and geochemical parameters are regarded as characteristics of one unit. The merged array is normalized so that all the parameters have uniform weight and the comparability between different parameters is improved. Then, K-means clustering method is used to classify all the samples in a research area. As the K-value is very difficult to estimate, it is necessary to conduct further experiments with different K-values, determine and explain the relationship between different classifications, and then identify the best classifications. Using the field information as a reference, one or more classifications are judged as one geological body. The method in this paper is practiced using the Tahe area, located within a typical shallow overburden area in Northeast China, as a case study. We used seven elements of 334 geochemical samples and 41.222 geological samples. K-values of 6, 7, 8, and 9 are calculated. We were successful in identifying geological bodies, and gained a new understanding on the scope of some rocks types. It is believed that the method proposed in this paper is highly efficient, easy to conduct, and can provide more detailed and comprehensive information than traditional methods.
机译:在中国大陆,有大型表面积覆盖着浅层过度覆盖(极象厚度<100米)。这里的地质映射在这里很困难,因为没有足够的暴露的基岩。在本文中,我们将地球化学和地球物理数据转移到特征,并在地质映射中使用它以识别底层基岩。最重要的是,我们将“代表一个地球化学样本的区域”定义为一个“统计单位”。分析每个单元(数百个数据)中的所有地球物理数据,都会产生统计参数,例如模式,狭窄的范围,频率,偏振和峰值,它们组合在一起来描述该区域的岩石的特性。对于每个单元中的地球化学数据,筛选一些岩石形成元件以构建新参数,例如SiO2,Na2O + K 2 O,CaO,Al2O3,FeO + MgO。所有地球物理和地球化学参数都被视为一个单位的特征。合并数组归一化,以便所有参数具有均匀的重量,并且改善了不同参数之间的可比性。然后,k-means聚类方法用于对研究区域中的所有样本进行分类。由于K值非常难以估计,有必要以不同的k值进行进一步的实验,确定和解释不同分类之间的关系,然后识别最佳分类。使用现场信息作为参考,一个或多个分类被判断为一个地质体。本文中的方法是使用位于东北东北典型浅层覆盖地区内的塔赫地区实施。我们使用了七个334个地球化学样本的元素和41.222个地质样品。计算6,7,8和9的k值。我们在识别地质机构方面取得了成功,并获得了一些关于一些岩石类型的范围的新理解。据信,本文提出的方法是高效,易于进行的,并且可以提供比传统方法更详细和全面的信息。

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