The Effects of Smoothing-Induced Weak Annotation in Mineral Prospectivity Mapping

Abstract The meaning of targets in data-driven mineral prospectivity mapping (DD-MPM) is foremost dependent on the quality of data. The resolving power of annotated data depends on the ratio of annotated area or volume and target footprint. At sufficiently large-scales of DD-MPM, it is impossible to match annotated area and covariate resolution to the footprint of mineral deposits. Consequently, information exposed to data modelling is unrestricted to between a target and its features. By definition, this type of annotation is a form of weak annotation, whose predictor–predictand relationship is weakened geostatistically through smoothing (a high ratio of annotated area or volume to target footprint). Annotation strength in DD-MPM is unstudied, although it is a research frontier in adjacent domains such as medical image segmentation. In this study, we present experimental results that reveal the spatial and aspatial significance of smoothing-induced weak annotation. Major findings include: (a) model performance improves with increasing smoothing, which is consistent with the linearization of the predictor–predictand relationship; (b) prospective area decreases with increasing smoothing, as smoothing weakens the signature of positive samples; (c) multifocal workflow uncertainty increases, caused by the same mechanism as in (b); and (d) the advantage of nonlinear and more expressive models is diminished with increasing smoothing. Therefore, although annotation strategy in DD-MPM seems innocuous, it has profound effects on the meaning of target area and exploration models. These effects impact: (a) the in-field validation of targets or model deployment expectations; (b) the mathematical meaning of trained models and targets; (c) the choice of model complexity; and (d) comparative analysis of workflow design (e.g., algorithms).