Alberta Geological Survey, Alberta Energy Regulator, Edmonton, Alberta, Canada.
PLoS One. 2024 Mar 27;19(3):e0296881. doi: 10.1371/journal.pone.0296881. eCollection 2024.
Maps showing the thickness of sediments above the bedrock (depth to bedrock, or DTB) are important for many geoscience studies and are necessary for many hydrogeological, engineering, mining, and forestry applications. However, it can be difficult to accurately estimate DTB in areas with varied topography, like lowland and mountainous terrain, because traditional methods of predicting bedrock elevation often underestimate or overestimate the elevation in rugged or incised terrain. Here, we describe a machine learning spatial prediction approach that uses information from traditional digital elevation model derived estimates of terrain morphometry and satellite imagery, augmented with spatial feature engineering techniques to predict DTB across Alberta, Canada. First, compiled measurements of DTB from borehole lithologs were used to train a natural language model to predict bedrock depth across all available lithologs, significantly increasing the dataset size. The combined data were then used for DTB modelling employing several algorithms (XGBoost, Random forests, and Cubist) and spatial feature engineering techniques, using a combination of geographic coordinates, proximity measures, neighbouring points, and spatially lagged DTB estimates. Finally, the results were contrasted with DTB predictions based on modelled relationships with the auxiliary variables, as well as conventional spatial interpolations using inverse-distance weighting and ordinary kriging methods. The results show that the use of spatially lagged variables to incorporate information from the spatial structure of the training data significantly improves predictive performance compared to using auxiliary predictors and/or geographic coordinates alone. Furthermore, unlike some of the other tested methods such as using neighbouring point locations directly as features, spatially lagged variables did not generate spurious spatial artifacts in the predicted raster maps. The proposed method is demonstrated to produce reliable results in several distinct physiographic sub-regions with contrasting terrain types, as well as at the provincial scale, indicating its broad suitability for DTB mapping in general.
显示基岩以上沉积物厚度的地图（基岩深度或 DTB）对于许多地球科学研究非常重要，并且对于许多水文地质、工程、采矿和林业应用也是必要的。然而，在地形变化较大的地区（如低地和山区），准确估计 DTB 可能会很困难，因为传统的预测基岩高程的方法往往会低估或高估崎岖或切割地形的高程。在这里，我们描述了一种机器学习空间预测方法，该方法使用传统数字高程模型衍生的地形形态学和卫星图像的信息，并辅以空间特征工程技术，在加拿大艾伯塔省进行 DTB 预测。首先，从钻孔岩心测井中编译的 DTB 测量值用于训练自然语言模型，以预测所有可用岩心测井中的基岩深度，从而显著增加了数据集的大小。然后，使用几种算法（XGBoost、随机森林和 Cubist）和空间特征工程技术，结合地理坐标、接近度度量、相邻点和空间滞后的 DTB 估计值，对组合数据进行 DTB 建模。最后，将结果与基于辅助变量的 DTB 预测结果进行对比，以及使用反距离加权和普通克里金方法的常规空间插值进行对比。结果表明，与仅使用辅助预测因子和/或地理坐标相比，使用空间滞后变量来纳入训练数据的空间结构信息可以显著提高预测性能。此外，与直接将相邻点位置用作特征的一些其他测试方法不同，空间滞后变量不会在预测的栅格地图中产生虚假的空间伪影。该方法在具有不同地形类型的几个不同的地貌分区以及省级范围内都能产生可靠的结果，表明其在一般的 DTB 制图中具有广泛的适用性。
