Use of Visualization Tools for Drill and Blast Activities

With the promising development of new technologies, automation in Drill and Blasting (D&B) is on the horizon. The data generated in all D&B activities, which is currently segregated, can be linked to become information and carry a logical meaning. This information can be used to identify critical activities and make decisions regarding corrective measures to accomplish the goals of D&B. Minera Caserones, supporting Alert2Gain (A2G), and Dyno Nobel, has been working on the development of a quality assurance and quality control (QAQC), system for the mine involving all D&B activities. The main variables in the model for D&B include data from the geology of the deposit, drilling, explosive products, and material loading, among others. To create the model, the mine is divided into blasting polygons. Those polygons are part of a block model. The block model is assembled following different rules, such as: • Block sizes of 4m x 4m x 15m, • Classification of the blocks to a particular blasting polygon using a geospatial intersection algorithm, • Algorithms connect all the pieces of data automatically with the minimum of human intervention and populate the block model, Because measuring data for each block is impossible, another set of rules and algorithms completes the model. Dyno Nobel generates an essential set of data for the QAQC system. Explosive truck loading rates and explosive products, among other data, are also included in the system. The system autogenerates reports every hour, and a summary of the results of each mining day (a mining shift of 12 hours) is distributed by email to all responsible persons to identify the critical tasks and then to make the proper adjustments to accomplish the goals of D&B. The information is updated every hour and available through a dashboard for easy and straightforward interpretation. This paper explains the conceptual details of the QAQC system implemented in Minera Caserones, how the data is aggregated automatically from the different sources, shows examples of the type of information in the dashboard, and presents a successful case where a deviation was detected, and the corrective measures that were implemented to keep the performance of the D&B activity. The paper finally discusses the next steps toward an autonomous D&B system based on new AI technologies, with minimum human intervention.