map, highlighting the most promising locations for follow-up sampling, field validation, and drilling. A key strength of the method is that the operator—not the algorithm—decides the number of target areas to prioritize. This flexibility allows the selection to be tailored to practical constraints such as available time, budget, field personnel, and broader exploration strategy, making the workflow both robust and operationally realistic (Figure 4). 10 target points 30 target points Figure 4 – The target points detected by the Return-Risk method 4. Conclusions The Return–Risk decision-making framework provides a flexible and user-friendly approach that allows decision-makers—even those without a geological background—to evaluate target areas by integrating both frequentist and Bayesian perspectives. By jointly considering
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