The primary goal of mineral prospectivity mapping (MPM) is to narrow the search for mineral resources by producing spatially selective maps. However, in the data-driven domain, MPM products vary depending on the workflow implemented. Although the data science framework is popular to guide the implementation of data-driven MPM tasks, and is intended to create objective and replicable workflows, this does not necessarily mean that maps derived from data science workflows are optimal in a spatial sense. In this study, we explore interactions between key components of a geodata science-based MPM workflow on the geospatial outcome, within the modeling stage by modulating: (1) feature space dimensionality, (2) the choice of machine learning algorithms, and (3) performance metrics that guide hyperparameter tuning. We specifically relate these variations in the data science workflow to the spatial selectivity of resulting maps using uncertainty propagation. Results demonstrate that typical geodata science-based MPM workflows contain substantial local minima, as it is highly probable for an arbitrary combination of workflow choices to produce highly discriminating models. In addition, variable domain metrics, which are key to guide the iterative implementation of the data science framework, exhibit inconsistent relationships with spatial selectivity. We refer to this class of uncertainty as workflow-induced uncertainty. Consequently, we propose that the canonical concept of scientific consensus from the greater experimental science framework should be adhered to, in order to quantify and mitigate against workflow-induced uncertainty as part of data-driven experimentation. Scientific consensus stipulates that the degree of consensus of experimental outcomes is the determinant in the reliability of findings. Indeed, we demonstrate that consensus through purposeful modulations of components of a data-driven MPM workflow is an effective method to understand and quantify workflow-induced uncertainty on MPM products. In other words, enlarging the search space for workflow design and experimenting with workflow components can result in more meaningful reductions in the physical search space for mineral resources.

矿产前景图 (MPM) 的主要目标是通过制作空间选择性地图来缩小矿产资源的搜索范围。然而，在数据驱动领域，MPM 产品因实施的工作流程而异。尽管数据科学框架在指导数据驱动的 MPM 任务的实施方面很流行，并且旨在创建客观且可复制的工作流程，但这并不一定意味着从数据科学工作流程导出的地图在空间意义上是最优的。在本研究中，我们通过调节以下内容，在建模阶段探索基于地理数据科学的 MPM 工作流程的关键组件之间关于地理空间结果的相互作用：(1) 特征空间维度，(2) 机器学习算法的选择，以及 (3) ）指导超参数调整的性能指标。我们特别将数据科学工作流程中的这些变化与使用不确定性传播生成的地图的空间选择性联系起来。结果表明，典型的基于地理数据科学的 MPM 工作流程包含大量局部最小值，因为工作流程选择的任意组合很可能产生高度区分的模型。此外，可变域度量是指导数据科学框架迭代实现的关键，它与空间选择性表现出不一致的关系。我们将此类不确定性称为工作流程引起的不确定性。因此，我们建议应遵循更大的实验科学框架中科学共识的规范概念，以便量化和减轻作为数据驱动实验的一部分的工作流程引起的不确定性。科学共识规定，实验结果的共识程度是研究结果可靠性的决定因素。事实上，我们证明，通过有目的地调整数据驱动的 MPM 工作流程组件达成共识是理解和量化 MPM 产品工作流程引起的不确定性的有效方法。换句话说，扩大工作流设计的搜索空间并试验工作流组件可以更有意义地减少矿产资源的物理搜索空间。