基于高斯混合的颜色聚类钨矿色选识别算法研究

A Method of Tungsten Mine Using Gauss Mixed Combined with Color Clustering Algorithm

摘要: 我国钨矿大部分矿脉厚度较窄，通常采用的采矿方法大多为浅孔留矿法及削壁充填法，原矿石中不可避免地含有大量的废石，采用人工手选的方式进行剔除部分废石，增加了劳动工人的劳动强度，在一定程度上增加了企业的生产成本。本文基于江西赣州钨矿的灰度特征，设计高斯混合的颜色聚类钨矿色选识别算法，该算法采用高斯混合模型进行识别运动矿石､CIELAB颜色空间的聚类算法检测矿石所含矿物成分，并进行试验研究，为钨矿石的初步分选提供依据。应用该算法脉石选出率高达90%，满足生产要求。对减轻劳动者负担，节约矿山选矿成本有着重要的工程意义。

Abstract: The thickness of most ore veins in tungsten mines in China is relatively narrow. The commonly used mining methods are the shallow-hole ore-retaining method and the wall-removing and filling method. In the original ore, a large amount of waste rock is inevitably contained. The use of artificial hand-selection to remove some of the waste stone has increased the labor intensity of laborers and increased the production cost. This paper designs a Gaussian mixture based on the gray features of the tungsten ore, and designs the color clustering of tungsten ore algorithm. The algorithm uses Gaussian mixture model to identify the movement ore, and uses CIELab color space clustering algorithm to detect the mineral composition contained in the ore, which provides a basis for the preliminary sorting of tungsten ore. The selected rate of gangue is 90%, which meets the production requirements. It has important engineering significance for reducing the labor burden and saving mine ore costs.