Geophysical methods are becoming commonly used for hydrogeological applications such as hydrostratigraphic unit delineation, contaminant plume mapping, and the identification of preferential groundwater flow pathways (among others). Despite the value of geophysics, challenges such as non-uniqueness, scales of measurement, and complex geology can introduce high uncertainty in models and interpretations. Addressing this uncertainty not only helps focus project objectives but also strengthens confidence among geophysicists and stakeholders. With recent advances in systems like the towed transient electromagnetic (tTEM) and TEM2Go systems, which allow rapid collection of large resistivity datasets, there is an increasing necessity to move beyond purely qualitative interpretation toward a more semi-quantitative approach to get the full value of these data. Here, we present real data examples demonstrating the use of Python-based algorithms to automatically sample properties from geophysical models, such as electrical resistivity and seismic velocity, across lithologic borehole intervals or other defined interval groupings. By applying consistent sampling rules, these workflows enable site-specific physical property classifications for different lithologies, unit contacts, and hydrostratigraphic units, ultimately improving the interpretability of the geophysical models. We illustrate this approach using various geophysical techniques. Our overarching goal is to capture sources of uncertainty inherent to the geophysical models and the borehole classifications, and then visually convey that uncertainty in the form of graphs and probability sections. This approach not only supports more accurate geophysical interpretations but also highlights where uncertainty is greatest during the interpretation process and where additional data or analysis may be needed to reduce that uncertainty.