Accurate fault detection is crucial for 3D subsurface structural modeling, yet the success of the method fundamentally depends on a critical property: the spatial continuity of fault-traces. In this study, we illustrated that spatial continuity strongly depends on spatial additivity of machine-learning-based classification. Spatial additivity dictates whether a machine-learning (ML) model yields consistent predictions when evaluating an entire structural domain versus its localized spatial subsets. While supervised ML can be used to map tectonic discontinuities, the impact of data preprocessing on the important properties (spatial additivity, spatial continuity) remains unaddressed. This study compares three data-scaling workflows influencing a Support Vector Machine (SVM) classifier: independent subset scaling, per-surface local scaling, and canonical ML scaling. Validated on structural data from the Kraków-Silesian Homocline, the results demonstrate that local and independent scaling frameworks violate spatial additivity, introducing potential artifacts. Conversely, the canonical global architecture satisfies spatial additivity, ensuring stable, continuous fault networks. Crucially, maintaining spatial additivity is vital for the incremental updating of geological models, enabling the seamless integration of newly acquired borehole data without triggering complete recalculations of the global feature space.