163 several layers of uncertainty—from laboratory analytical noise and mapping errors to inconsistencies between software platforms, modelling algorithms, and even subjective geological interpretations. These uncertainties move through every stage of the workflow: interpolation, anomaly delineation, ranking, and ultimately the investment decisions that determine whether a prospect advances or not. Spatial uncertainty, in particular, remains a dominant issue during early exploration (Chile`s and Delfiner, 2012). Any attempt to extrapolate limited samples into unsampled areas introduces estimation uncertainty. As shown by Sadeghi et al., 2023, this uncertainty can be separated into stochastic components, driven by natural geological variability, and systemic components, which arise from data acquisition and modelling choices. Distinguishing between these helps us build a more realistic view of mineralization patterns rather than relying on deterministic maps that imply confidence we do not actually have. Uncertainty also plays a critical role in selecting analytical and interpretive methods (Caers, 2011). Different clustering or classification algorithms can produce very different outcomes, even on the same dataset. As demonstrated in Sadeghi (2025), evaluating the robustness of clustering performance is fundamental for choosing the most reliable method instead of assuming one technique will always perform better. The same principle applies when comparing kriging variants, machine learning models, multifractal approaches, or hybrid AI–geostatistical workflows (Sadeghi, 2024). What matters is understanding when and why a particular method is appropriate, and how sensitive it is to the structure and quality of the data. These considerations highlight a broader point: modern exploration cannot rely solely on deterministic maps or method-driven workflows. We need adaptive, uncertainty-aware approaches that reflect the real state of our knowledge, especially in frontier terrains where information is scarce. By embedding uncertainty into spatial modelling, method selection, and decision-making, exploration teams can allocate resources more effectively and defend their choices more transparently. This perspective naturally leads to the development of new AI- and data-driven frameworks that integrate heterogeneous datasets and quantify uncertainty in a systematic way. The Return–Risk concept in this research builds on this need by combining geoscientific understanding with probabilistic modelling and explainable AI to identify targets that maximize geochemical “Return” while reducing uncertainty-related “Risk.” Such a framework supports more consistent and unbiased decisions, helping teams move from intuition-driven targeting toward a more transparent and economically grounded exploration strategy. This Return–Risk concept in this research was initially developed by Sadeghi and Cohen (2023), which was successfully applied to the till samples in entire Sweden, and soil samples in Cyprus for VMS Cu exploration. Here, this method was applied to critical minerals/battery group (Li, Ni, Co, Ta, V, MnO, MgO, and Al2O3) in a case study in Jebal Ibrahim, Saudi Arabia, using 666 stream sediment samples focusing on Li exploration (Figure 1). Figure 1 – The study area and sample locations
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