Better Predict the Dynamic of Geometry of In-Pit Stockpiles Using Geospatial Data and Polygon Models

TL;DR

This paper introduces a method to model and track the dynamic shape of in-pit stockpiles using geospatial data and polygon models, aiding in mine operation and financial valuation.

Contribution

It proposes a novel approach to infer stockpile geometry changes without reclaimed bucket data, utilizing GPS data and comparing two polygon modeling techniques.

Findings

Effective inference of stockpile shape changes during operations

Comparison of two polygon models for 2D shape creation

Enhanced accuracy in stockpile volume estimation

Abstract

Modelling stockpile is a key factor of a project economic and operation in mining, because not all the mined ores are not able to mill for many reasons. Further, the financial value of the ore in the stockpile needs to be reflected on the balance sheet. Therefore, automatically tracking the frontiers of the stockpile facilitates the mine scheduling engineers to calculate the tonnage of the ore remaining in the stockpile. This paper suggests how the dynamic of stockpile shape changes caused by dumping and reclaiming operations can be inferred using polygon models. The presented work also demonstrates how the geometry of stockpiles can be inferred in the absence of reclaimed bucket information, in which case the reclaim polygons are established using the diggers GPS positional data at the time of truck loading. This work further compares two polygon models for creating 2D shapes.