In the last decade, machine learning algorithms have shown their superior performance in the spatial interpolation of environmental properties compared to classical interpolation models. In particular, the random forest ensemble model has provided the best adjustment. In this work, we compare the performance of support vector machines (SVM), simple trees (ST), random forests (RF) and extremely random forests (ERF), using discrete depths obtained by vertical electrical sounding (VES) from the hydrogeological basement of a sedimentary basin in Argentina; the coordinates are not gridded but almost aligned. On the other hand, in different artificial intelligence applications, the ERF algorithm has surpassed several methods of machine learning, including random forests. To the best of our knowledge, we hereby report the first spatial regression application of the novel ERF algorithm, which predicted—even better than RF—values it had not been trained for with an average $R^2$ score of $97.6\%$. This allowed us to obtain a satisfactory generalization of VES depths in the form of a three-dimensional approximation of the basement. The ERF algorithm also outperformed RF in computation time and smoothness of the surface generated. The primary significance of the results reported here lies in the relative independence that this technique has to offer, considering the area of application and gridding. Added to this, the nature of the method by means of which the discrete data are obtained is independent as well, as these could not only be derived from the VES technique, but also from well data or from different geophysical inversions.

在过去的十年中，与经典插值模型相比，机器学习算法在环境属性的空间插值方面表现出优越的性能。特别是随机森林集成模型提供了最好的调整。在这项工作中，我们比较了支持向量机 (SVM)、简单树 (ST)、随机森林 (RF) 和极端随机森林 (ERF) 的性能，使用从水文地质基础的垂直电测深 (VES) 获得的离散深度阿根廷的一个沉积盆地；坐标没有网格，但几乎对齐。另一方面，在不同的人工智能应用中，ERF算法已经超越了包括随机森林在内的几种机器学习方法。据我们所知，${\mathrm{电阻}}^2$ 得分 $97.6\%$. 这使我们能够以基底的 3 维近似形式获得令人满意的 VES 深度概括。ERF 算法在计算时间和生成的表面平滑度方面也优于 RF。考虑到应用领域和网格划分，此处报告的结果的主要意义在于该技术必须提供的相对独立性。除此之外，获得离散数据的方法的性质也是独立的，因为这些数据不仅可以来自 VES 技术，还可以来自井数据或不同的地球物理反演。