Heap Leach Pad Surface Moisture Monitoring Using Drone-Based Aerial Images and Convolutional Neural Networks: A Case Study at the El Gallo Mine, Mexico

An efficient metal recovery in heap leach operations relies on uniform distribution of leaching reagent solution over the pad surface. However, current practices for (HLP) surface moisture monitoring often rely manual inspection, which is labor-intensive, time-consuming, discontinuous, and intermittent. In order to complement process reduce frequency exposing technical manpower hazardous (e.g., dilute cyanide gold leaching), this manuscript describes a case study implementing an HLP method based drone-based aerial images convolutional neural networks (CNNs). Field data collection was conducted at El Gallo mine, Mexico. A commercially available hexa-copter drone equipped with one visible-light (RGB) camera thermal infrared sensor acquire RGB from The collected had high spatial temporal resolutions. high-quality were used generate maps two CNN approaches. generated provide direct visualization different zones across surface, such information can be detect potential operational issues related facilitate timely decision making operations.