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首页> 外文期刊>Computers & geosciences >MINLITH - an experience-based algorithm, for estimating, the likely mineralogical compositions of sedimentary rocks from bulk chemical analyses
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MINLITH - an experience-based algorithm, for estimating, the likely mineralogical compositions of sedimentary rocks from bulk chemical analyses

机译：MINLITH-一种基于经验的算法，用于通过大量化学分析估算沉积岩的可能矿物学组成

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摘要

The MINLITH algorithm is a toot for estimating the likely mineralogical compositions of sedimentary rocks, using information from bulk chemical analyses. It is an experience-based algorithm that represents compositions in terms of a simplified set of normative minerals. MINLITH has been designed to be applied principally to mature sedimentary rocks, but it can (with care) be applied also to immature sediments and to metasedimentary rocks; the compositions that MINLITH gives for metasedimentary rocks are approximations to the original (i.e. pre-metamorphic) mineralogical compositions. The experience base on which MINLITH is built is a collection of 600 reference samples of sedimentary rocks. The compositional regularities found in these samples have allowed empirical rules to be developed to predict how the oxides reported in a bulk chemical analysis should be partitioned among the minerals most likely to be present. The discrepancies between MINLITH-estimated compositions and physically determined modal compositions are relatively small for the most widespread types of mature sedimentary rocks; they are comparable in their magnitude to the discrepancies associated with other methods for estimating mineralogical compositions from bulk chemical analyses, and to the discrepancies associated with quantitative X-ray diffractometry. The MINLITH algorithm is of particular value: (1) for providing preliminary estimates of mineralogical composition, prior to precise modal analysis; (2) for identifying systematic compositional variation within suites of samples; (3) in generalised sample classification; (4) in the sedimentological interpretation of metasedimentary rocks. (C) 2004 Elsevier Ltd. All rights reserved.
机译：MINLITH算法是一种嘟嘟声，可使用来自大量化学分析的信息来估算沉积岩的可能的矿物学组成。它是一种基于经验的算法，可以根据一组简化的规范性矿物来表示成分。 MINLITH被设计为主要应用于成熟的沉积岩，但也可以（小心）应用于未成熟的沉积物和准沉积岩。 MINLITH给出的沉积沉积岩石的成分近似于原始的（即变质前）矿物学成分。建立MINLITH的经验基础是600份沉积岩参考样品的集合。这些样品中发现的成分规律性允许建立经验规则，以预测在大宗化学分析中报告的氧化物应如何在最可能存在的矿物之间分配。对于大多数分布类型的成熟沉积岩而言，MINLITH估计的成分与物理确定的模式成分之间的差异相对较小。它们的数量级可与通过大量化学分析估算矿物成分的其他方法相关的差异，以及与定量X射线衍射法相关的差异相媲美。 MINLITH算法具有特别的价值：（1）在精确模态分析之前，提供矿物成分的初步估计； （2）识别样品组中的系统组成变化； （3）广义样本分类； （4）在沉积学上解释了准沉积岩。 （C）2004 Elsevier Ltd.保留所有权利。

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