Development of a Remote Rock Fragmentation Size Distribution Measurement System for Surface Mines Using 3D Photogrammetry

Abstract

One of the factors that can affect the efficiency of a mining operation is the fragmentation size distribution of blasted rock. A consistent fragmentation size allows the company to streamline its process, and more importantly, minimize costs. In order to maintain this fragmentation size, monitoring must be done regularly so that adjustments can be made. Traditional methods such as manual sieving and visual estimation are have been used for this purpose, but limitations on sampling procedure and bias make these methods relatively inefficient. One of the solutions that were developed was to use digital image-based particle size analysis. The study proposes a cloud-based 3D photogrammetry rock fragmentation size distribution system that will make use of multiple images to create 3D models that can then be analyzed and segmented to provide a fragmentation size distribution. Several pictures of a muckpile using a smartphone are taken from an angle and compiled into a dataset. This is used as input for a Structure-from-Motion algorithm, which can create a 3D point cloud from the image data. This point cloud is then subjected to clustering so that the individual fragments can be represented and their dimensions could be measured. Finally, from these dimensions, a fragmentation size distribution can be created. As the system requires a large amount of computing power, it can be implemented in a remote server so that it can be accessible in the field. This system could provide surface mine operators an easy way to estimate size distribution using only a smartphone.