Abstract
Identifying and counting individual mineral grains composing sand is an important component of many studies in environment, engineering, mineral exploration, ore processing and the foundation of geometallurgy. Typically, silt (32-128μm) and sand (128-1000μm) sized grains will be characterized under an optical microscope or a scanning electron microscope. In both cases, it is a tedious and costly process. Therefore, in this paper, we introduce an original computational approach in order to automate mineral grains recognition from numerical images obtained with a simple optical microscope. To the best of our knowledge, it is the first time that the current computer vision based on machine learning algorithms is tested for the automated recognition of such mineral grains. In more details, this work uses the simple linear iterative clustering segmentation to generate superpixels and many of them allow isolating sand grains, which is not possible with classical segmentation methods. Also, the approach has been tested using convolutional neural networks (CNNs). However, CNNs did not give as good results as the superpixels method. The superpixels are also exploited to extract features related to a sand grain. These image characteristics form the raw dataset. Prior to proceed with the classification, a data cleaning stage is necessary to get a usable dataset for machine learning algorithms. In addition, we present a comparison of performances of several algorithms. The overall obtained results are approximately 90% and demonstrate the concept of mineral recognition from a sample of sand grains provided by a numerical image.
Highlights
Identification or counting of minerals grains in sediments or sands is a critical task in many scientific endeavors
The work proposed in this paper aims at demonstrating that computer vision coupled with data science and machine learning allow to perform mineral recognition
We present some results for the recognition of mineral grains from an image provided by a stereo-zoom binocular microscope
Summary
Identification or counting of minerals grains in sediments or sands is a critical task in many scientific endeavors. The abundance of minerals such as gold (Au) or chalcopyrite (CuFeS2) in sediments or milled rock can indicate the proximity of a gold or copper mineral deposit (Averill, 2001) This technique is used on a vast scale by the diamond exploration industry, searching for grains of distinctive minerals such as chromium-bearing pyrope or diopside, minerals that are present with diamonds in kimberlite. Two approaches are typically used to identify and characterize minerals grains in sediments or milled rocks: visual sorting with optical microscopy and automated Scanning Electron Microscopy (SEM) (Gottlieb et al, 2000; Sutherland and Gottlieb, 1991) Techniques such as chemical analysis and X-ray diffraction of sands or milled rocks will not provide a real mineral count.
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