values (Figure 3), enabling a balanced assessment of lithium “Return” relative to the spatial and statistical “Risk” associated with each sample location. This method provides a structured way to evaluate exploration decisions by explicitly accounting for uncertainty in geochemical datasets. The workflow quantifies both the “Return,” defined as the expected geochemical enrichment or exploration reward, and the “Risk,” which reflects the spatial and statistical uncertainty associated with that enrichment. The method combines frequentist and Bayesian principles to estimate uncertainty, integrates these measures into a unified Return–Risk matrix, and then ranks targets based on their balance between potential reward and reliability. This approach helps distinguish genuinely strong anomalies from those that appear promising but are dominated by uncertainty, making the final decisions more defensible and transparent. Figure 3 – The Return-Risk schematic workflow (Sadeghi, 2020; Sadeghi and Cohen 2023) Here, the Sequential Gaussian Simulation (SGSIM) was applied to the Li-CLR dataset to generate 1000 geostatistical realizations (i.e., scenarios). To quantify the spatial uncertainty represented by the variability among these realizations, the Euclidean distance (- representing the spatial dissimilarity- see Scheidt and Caers, 2009) was calculated for each pair of models. Because the resulting dissimilarity matrix is high-dimensional, Multidimensional Scaling (MDS) was used to reduce its dimensionality and provide a more interpretable representation of the spatial uncertainty structure. The Return–Risk plot derived from these outputs allows the operator to select the subset of models that achieve the highest possible geochemical return with the lowest associated uncertainty. In this example, two scenarios were tested: selecting the 10 best target points and selecting the 30 best target points. After choosing these preferred clusters of models, they were transformed back into their spatial form and averaged to produce an EType map representing the central tendency of the selected realizations. This final output serves as the target
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