Mining is an interdisciplinary industry that utilises equipment and technology intensively in daily operations. Mine-to-Mill is considered as a key concept for metal mining recently. Impact of underperformed basic upstream operations such as drilling and blasting will sustain this inefficiency in downstream processes, such as mineral processing. Data provided for each of these operations from software and hardware utilised on field reached a level where advanced data analytics becomes applicable. Data warehousing and data mining are alternative tools that rely on a robust data structure. This study gives insight into a data-driven framework for modern mines and presents a data mining implementation on real-time mining-related data for prediction of blasting performance. Random forest and adaptive boosting algorithm were utilised on an integrated data warehouse to discover major operational parameters for efficient blasting. The implementation on site improved the performance of drilling and blasting. The variables highlighted as important by random forest and adaptive boosting algorithm directed the experts of mine-to-mill on site to focus on the close control and detailed analysis of certain drilling- and blasting-related parameters.

采矿是一个跨学科行业，在日常运营中大量使用设备和技术。矿山到矿场最近被认为是金属采矿的关键概念。钻探和爆破等基本上游业务表现不佳的影响将在下游流程（例如矿物加工）中保持这种低效率。从现场使用的软件和硬件为每个操作提供的数据达到了可以应用高级数据分析的水平。数据仓库和数据挖掘是依赖可靠数据结构的替代工具。这项研究深入了解了现代矿山的数据驱动框架，并提出了基于实时采矿相关数据的数据挖掘实现，以预测爆破性能。在集成的数据仓库上利用随机森林和自适应增强算法发现有效爆破的主要操作参数。现场实施提高了钻孔和爆破性能。随机森林和自适应增强算法强调的重要变量指示矿山到现场的专家专注于对某些与钻探和爆破有关的参数的紧密控制和详细分析。