For the mining based oilsands industry, it is desirable to determine the quality of the ore delivered to the extraction processes in real‐time to make optimal operational decisions such as optimal ore blending to achieve maximal bitumen recovery. Currently, the industry determines the real‐time ore characteristics for any given shovel Global Positioning System (GPS) location by first determining the shovel elevation from the topological mine map and then using the determined geological coordinates in the 3D geological block model. It should be noted that the block model is built based on the widely spaced core hole samples, and it is updated only on a yearly basis due to high cost of narrower core hole sampling. Thus, the block model predictions are often inaccurate in between the core hole spacing. On the other hand, mining operations data are available that contain accurate ore characteristics information in the already mined area. Therefore, in this work, we present a just‐in‐time based data‐driven modelling strategy that utilizes the recently available mining operations data to obtain reliable ore characteristics given the GPS data. The prediction capability of ore characteristics using the proposed modelling strategy is validated at core hole locations. Further, the prediction of ore characteristics at non‐core hole points demonstrate promising results.

中文翻译：

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在采矿应用中使用GPS数据进行实时学习以预测油砂矿石特性

对于以采矿为基础​​的油砂行业，希望实时确定交付到提取过程的矿石质量，以制定最佳的运营决策，例如优化矿石掺合以实现最大的沥青回收率。当前，行业首先通过从拓扑矿图确定铲子海拔，然后在3D地质块模型中使用确定的地质坐标，来确定任何给定铲子全球定位系统（GPS）位置的实时矿石特征。应当注意的是，块模型是基于间隔较宽的岩心孔样本建立的，并且由于岩心孔采样较窄的成本较高，因此只能每年更新一次。因此，在芯孔间距之间，块模型预测常常是不准确的。另一方面，可获得的采矿作业数据包含已开采区域中的准确矿石特征信息。因此，在这项工作中，我们提出了一个基于时间的基于数据的建模策略，该策略利用最近可用的采矿作业数据来获得给定GPS数据的可靠矿石特性。使用所提出的建模策略对矿石特征的预测能力在芯孔位置得到了验证。此外，对非核心孔点矿石特征的预测显示出可喜的结果。使用所提出的建模策略对矿石特征的预测能力在芯孔位置得到了验证。此外，对非核心孔点矿石特征的预测显示出可喜的结果。使用所提出的建模策略对矿石特征的预测能力在芯孔位置得到了验证。此外，对非核心孔点矿石特征的预测显示出可喜的结果。