Automated recognition of drill core textures: A geometallurgical tool for mineral processing prediction

In a previous work, the use of drill core texture as a geometallurgical indicator was explored for the Mont -Wright iron ore deposit. At this deposit, the link between texture and mineral performance during comminution and heavy liquid separation was assessed by laboratory tests. Additionally, the micro-texture associated to each macro-texture was characterized by Mineral Liberation Analyzer (MLA). As a result, a classification of drill core textures calibrated to mineral processing performance was established.To integrate the ore mesotexture into predictive block models, a core logging tool for automated textural pattern recognition is being developed. This paper presents the first step in this development: a methodology for the automated recognition of drill core textures. The proposed methodology is based on 2-D digital image analysis of drill cores. Texture information is extracted from digital images using gray level co-occurrence matrix (GLCM) and gray level run length matrix (GLRLM). Based on the information provided by these two methods, images were classified into six texture categories using multivariate discriminant analysis. A high classification success was obtained: 88% of the drill core images were correctly classified into their textural pattern category.

在先前的一项工作中，对蒙特 - 赖特铁矿床探索了将岩芯结构用作地质冶金指标的方法。在该矿床，通过实验室测试评估了破碎和重液分离过程中结构与矿物性能之间的联系。此外，利用矿物解离分析仪（MLA）对与每种宏观结构相关的微观结构进行了表征。结果，建立了一种根据矿物加工性能校准的岩芯结构分类。为了将矿石的中观结构整合到预测块体模型中，正在开发一种用于自动识别结构模式的岩芯测井工具。本文介绍了这一开发过程的第一步：一种自动识别岩芯结构的方法。所提出的方法基于对岩芯的二维数字图像分析。利用灰度共生矩阵（GLCM）和灰度行程长度矩阵（GLRLM）从数字图像中提取结构信息。基于这两种方法提供的信息，使用多元判别分析将图像分为六类结构类别。获得了较高的分类成功率：88%的岩芯图像被正确分类到其结构模式类别中。