with a weighting scheme that emphasizes the spatial characteristics most representative of the deposit signature. The resulting workflow aims to produce a transparent, data-driven similarity map that highlights regions whose multiscale geophysical patterns most closely match the known mineralized zone signature. To compare the regional geophysical response with that of the known deposit, the grids are sampled using overlapping sliding windows whose dimensions approximate the footprint of the deposit anomaly. Each sliding window is flattened into a feature vector, and the full set of n windows forms the structured data matrix = [ 1 ⊤ 2 ⊤ ⋮ ⊤] ∈ℝ × , = × Each row corresponds to one sliding window, and the known deposit corresponds to one specific row of , denoted by index , which serves as the reference signature against which all other windows are compared. In the multivariate case, co-located sliding windows are extracted simultaneously from all geophysical grids so that the sampled windows retain both spatial context and cross-variable relationships. Spatial principal component analysis is then applied in pixel space, where the features are window pixels rather than distinct geophysical variables. After column-wise standardization, ̃ = − , ̃ = ⊤ where and are the column-wise mean and standard deviation, respectively. For constant features, is set to 1 to avoid division by zero. In this decomposition, contains the spatial loadings and contains the PCA scores, which quantify how strongly each window expresses the retained spatial patterns. Only the first retained components are used in the similarity calculation, where is chosen as the smallest number of principal components (PC) that still provides an adequate representation of the deposit signature. Deposit-specific weights are then derived from the deposit scores, = 2 ∑ 2 =1 , = 1,…, , so that components expressed more strongly by the deposit contribute more to the comparison. Similarity is then computed from the difference between the score vector of each window and that of the deposit, Δ , =Z , −Z , , 2 =∑ =1 Δ , 2 , =√ 2. Windows with smaller are therefore those whose multiscale spatial patterns most closely reproduce the deposit signature. Ranking all windows by increasing produces the similarity map used for regional targeting. Where other known deposits are available, the ranked predictions are validated against them using cumulative footprint recovery, counting unique overlap area so that overlapping prediction windows within the same deposit are not counted more than once, and recording the first rank at which a test deposit reaches a minimum coverage threshold of 50%.
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