approaches, which combine multiple evidence layers to predict favorable locations for mineralization [1, 15, 17]. Despite substantial progress, several limitations remain. Many methods still depend on relatively large labeled datasets, which is problematic where only a few known deposits may be available [18, 19], and many workflows remain difficult to interpret and act upon [20, 21, 22]. Existing approaches also often use known deposits mainly as labeled locations, while making limited use of their actual characteristics [23]. While studies have established the feasibility of pattern-based geophysical comparison [24] and enhanced multi-source integration using spatially weighted principal component analysis [25], these advances still leave an opportunity for frameworks that leverage deposit-specific internal spatial structure for quantitative regional comparison while supporting interpretable decision-making under high uncertainty and data sparsity [18, 26, 19]. Therefore, this paper aims to develop a geophysical prospecting method that identifies geophysical anomalies analogous to those of a single known economic deposit, as characterized by a resource assessment based on existing drilling. It aims to provide a quantitative, reproducible framework for detecting the similarity between geophysical anomalies and the geophysical signature of the known deposit. We intend to show a method that introduces a quantitative metric of spatial similarity based on spatial principal component analysis (sPCA). By decomposing into spatial eigenfrequencies, sPCA captures the specific geophysical signature of the reference deposit and quantifies how closely other regions reproduce that structure. The aim is to enable the detection of deposit-specific spatial patterns without labeled data or filters. Beyond being fully data-driven, the approach is designed to complement and enhance expert-driven exploration workflows. It seeks to enable quantitative pattern recognition that can sit atop existing geological knowledge, allowing experts to iteratively refine or validate hypotheses through an interpretable, data-informed similarity metric. We intend to show this approach in a brownfield case study in the Carajás Mineral Province, Brazil, applying it to magnetic and radiometric datasets to identify IOCG-related anomalies consistent with a known copper deposit. Study Area and Data The study area lies in the Carajás Mineral Province of Pará, northern Brazil, an Archean granite– greenstone and volcano-sedimentary terrane of the Itacaiúnas Supergroup that hosts numerous Feoxide and IOCG systems (Figure 4). The aim is to detect locations within this region whose internal spatial patterns resemble those expressed by the geophysical signature of a known IOCG deposit and to rank them by similarity.
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