Inaccuracies in spatial modelling of coal properties can impact coal resource and reserve estimates. Geostatistical methods which account for compositional data have previously been recommended as a preferred method for modelling of coal properties. These methods can quantify uncertainty, can use auxiliary information to improve estimates, and ensure that compositional data (such as coal proximate analysis) is modelled in a mathematically consistent way. However, geostatistical methods have drawbacks in that have rigid statistical assumptions about the distribution and stationarity of the target variable and require variogram interpretation which can be onerous when then number of domains and target variables are numerous. Due to these drawback, inverse distance weighting (a simple deterministic method) remains the most popular method for modelling coal properties in Australian resource estimates. To address the drawbacks of geostatistical methods, a machine learning approach based on the quantile regression forest algorithm is proposed as an alternative method to spatially model coal properties. This newly proposed method, accounts for spatial arrangement of data, requires minimal pre-processing steps, can quantify uncertainty and can easily incorporate auxiliary information of various types. To evaluate the performance of this method, the accuracy of predictions of coal proximate analysis properties, and coal density are compared to the predictions of inverse distance weighting and regression kriging. All methods incorporate isometric log ratio transform and back-transform of data in order to account for the compositional nature of coal proximate analysis. Data from an active coal mine in the Bowen Basin, Queensland Australia was used as basis of the results. A unique feature of the mine site in this basin is the presence of intrusion which impacts coal quality properties to various degrees. Using evaluation metrics from leave-one-out cross-validation, this paper demonstrates that quantile regression forest has higher accuracy, lower bias and higher precision than inverses distance weighting across all coal properties. The paper also shows that results of the new method are very similar or better than regression kriging. It is also demonstrated that the prediction maps produced by quantile regression forest could be used to more accurately map out coal impacted by intrusion compared to inverse distance weighting, and that inverse distance weighting overestimates the impact of intrusion and intrusion extent. A drawback of the method compared to inverse distance weighting, is that it is more computationally demanding, less intuitive and is currently not available in existing geological packages.

煤炭特性空间建模的不准确性会影响煤炭资源和储量估计。考虑成分数据的地质统计学方法以前曾被推荐为煤特性建模的首选方法。这些方法可以量化不确定性，可以使用辅助信息来改进估计，并确保以数学上一致的方式对成分数据（例如煤炭近似分析）进行建模。然而，地统计方法的缺点在于对目标变量的分布和平稳性有严格的统计假设，并且需要变差函数解释，当域和目标变量的数量很多时，这可能是繁重的。由于这些缺点，反距离加权（一种简单的确定性方法）仍然是澳大利亚资源估算中用于模拟煤炭特性的最流行方法。为了解决地质统计学方法的缺点，提出了一种基于分位数回归森林算法的机器学习方法，作为对煤炭特性进行空间建模的替代方法。这种新提出的方法考虑了数据的空间排列，需要最少的预处理步骤，可以量化不确定性，并且可以轻松地合并各种类型的辅助信息。为了评估该方法的性能，将煤炭近似分析特性和煤炭密度的预测精度与反距离加权和回归克里金法的预测进行了比较。所有方法都结合了等距对数比率变换和数据的反向变换，以说明煤炭近似分析的组成性质。来自澳大利亚昆士兰州鲍文盆地的一个活跃煤矿的数据被用作结果的基础。该盆地矿区的一个独特特征是存在侵入体，这会在不同程度上影响煤质特性。使用留一法交叉验证的评估指标，本文证明分位数回归森林比所有煤炭属性的逆距离加权具有更高的准确性、更低的偏差和更高的精度。该论文还表明，新方法的结果与回归克里金法非常相似或更好。还表明，与逆距离加权相比，分位数回归森林生成的预测图可用于更准确地绘制受侵入影响的煤炭，并且逆距离加权高估了侵入的影响和侵入程度。与反距离加权相比，该方法的一个缺点是计算要求更高，直观性较差，并且目前在现有地质包中不可用。