exploration “Return” and the uncertainty-related “Risk,” the method supports more balanced and defensible selections of priority areas for follow-up sampling, drilling, and broader exploration activities. It also enables operators to tailor the final target selection to real-world constraints such as budget, time, personnel, and logistical limitations. In the present case study, the outcomes of the Return–Risk analysis were independently reviewed and validated by the Geological Survey team, confirming the geological plausibility of the identified targets. Although the project remains ongoing, the preliminary results demonstrate the practical value and robustness of this approach, and additional details will be reported as the study progresses. ACKNOWLEDGEMENTS The author gratefully acknowledges Dr. Fabian Kohlmann (Lithodat Pty Ltd) and the Geological Survey of Saudi Arabia for their generous provision of the datasets employed in this work. REFERENCES Caers, J.K., (2011). Modeling Uncertainty in Earth Sciences. Wiley. Chile`s, J.P., Delfiner, P., (2012). Geostatistics: Modeling Spatial Uncertainty, 2nd ed John Wiley. Groves, D.I., Santosh, M., (2015). Province-scale commonalities of some world-class gold deposits: Implications for mineral exploration. Geoscience Frontiers. 6(3), 389-399. Sadeghi, B., Cohen, D., (2023). Decision-making within geochemical exploration data based on spatial uncertainty- A new insight and a futuristic review. Ore Geology Reviews. 161, 105660. Sadeghi, B., (2024). Fractals and Multifractals in the Geosciences. ELSEVIER, 302 p. Sadeghi, B., (2025). Clustering in Geo-Data Science: Navigating Uncertainty to Select the Most Reliable Method. Ore Geology Reviews. 181C, 106591. Sadeghi, B., Grunsky, E., Pawlowsky-Glahn, V., (2023). Uncertainty Quantification. In: Daya Sagar B., Cheng Q., McKinley J., Agterberg F. (eds) Encyclopedia of Mathematical Geosciences. Encyclopedia of Earth Sciences Series. Springer, Cham. 1583-1589. Scheidt, C., Caers, J.K., (2009). Representing spatial uncertainty using distances and kernels. Mathematical Geosciences. 41, 397-419.
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