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Automated Textural Classification of Iron Ores Using 'Recognition' - A Specialised Software Package for Studying Iron Ores

机译:使用“识别”的铁矿石自动纹理分类 - 用于研究铁矿石的专业软件包

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Geological, textural and mineralogical classification of iron ores is routinely conducted by mine and exploration geologists on reverse circulation percussion, blasthole cone and bulk samples for grade control, defining ore and waste, determining lump:fines ratio and predicting downstream processing characteristics. Classification is based on physical hardness, colour, streak and other visual characteristics. However, logging is generally restricted to the five to 20 per cent of chips above about 2 mm. This means that most of the sample is too fine to be reliably logged for anything but colour or streak and the textural types must be inferred from other information. A recently developed software package called 'Recognition' allows automated identification and classification of iron ore textural types and gangue on a particle basis in polished section down to 0.01 mm using processed digital images. It is not designed to be used as a routine logging tool, but is applicable where higher definition is required at the resource evaluation stage on selected percussion, drill core or bulk samples. 'Recognition' processes data obtained after optical image analysis of fine iron ore samples. It contains several parts, like 'Recognition', 'Composition', 'Liberation by Total Iron', 'Liberation by Phases' and 'Statistics', which allow vast and diverse information to be obtained about the iron ore samples being studied. Identification and classification of the textural type of each particle is performed according to the CSIRO-Hamersley Iron Ore Group Classification Scheme. The recognition procedure encompasses a good knowledge of different ore types and a logical knowledge tree, managing fuzzy boundary problems and the potential for a few different ore types in the same particle to be considered. This new technology introduces strict criteria to make the process of identifying particles more objective. Automated recognition standardises the identification procedure and significantly saves time. It enables users without a strong background in mineralogy to perform accurate texture identification. The 'Recognition' package has a modern user-friendly interface and Word compatible output. The combination of all the features mentioned makes the package a convenient tool for detailed studies of fine iron ores.
机译:铁矿石的地质,纹理和矿物学分类是由矿山和勘探地质学家进行逆转循环冲击,Blasthole Cone和批量样品进行级别控制,定义矿石和废物,确定肿块:罚款比率和预测下游加工特性。分类是基于物理硬度,颜色,条纹和其他视觉特征。然而,测井通常限制在大约2毫米的5至20%的芯片。这意味着大多数样本太良好,无法可靠地记录任何东西,除了颜色或条纹,纹理类型必须从其他信息推断出来。最近开发的软件包称为“识别”,允许在抛光部分的粒度基础上自动识别和分类铁矿石纹理类型和圆形,其使用加工的数字图像将低至0.01 mm。它不设计为例行日志记录工具,但适用于在所选打击乐,钻机核心或批量样本上的资源评估阶段所需的更高定义。 '识别'处理细铁矿石样品的光学图像分析后获得的数据。它包含几个部分,比如“识别”,“组成”,“通过总铁”解放,“阶段”和“统计”,允许关于所研究的铁矿石样本来获得巨大和多样化的信息。根据Csiro-Hamersley铁矿石组分类方案进行每个粒子的识别和分类。识别程序包括对不同矿石类型和逻辑知识树的良好知识,管理模糊边界问题和待考虑相同粒子中的几种不同矿石类型的潜力。这项新技术引入了严格的标准,使术语识别粒子更客观。自动识别标准标识识别程序并显着节省时间。它使用户能够在矿物学中没有强大的背景,以执行准确的纹理识别。 “识别”包具有现代用户友好的界面和Word兼容输出。所提到的所有特征的组合使得包装是一种方便的精细铁矿石研究的工具。

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