124 AUTOMATED GEOPHYSICAL PATTERN RECOGNITION FOR MINERAL EXPLORATION: CASE STUDY OF CARAJÁS, BRAZIL USING IOCG DEPOSITS Sofia Mantilla Salas1**, Pablo Mejia-Herrera2, Jonas Kloeckner1, Adel Asadi1 David Zhen1,Jef Caers1 ** (Presenting author: sofiams@stanford.edu) 1 Mineral-X, Earth and Planetary Sciences Department, Stanford University 2Northisle Copper and Gold ABSTRACT Mineral exploration targeting is a high-impact and high-uncertainty decision problem, and the regional-to-camp phase of exploration targeting often remains slow, manual, and difficult to reproduce. This paper develops a quantitative, reproducible, and interpretable geophysical prospecting method that identifies anomalies analogous to a known economic deposit using spatial principal component analysis (sPCA). The approach samples regional geophysical grids with sliding windows, decomposes the reference anomaly of the known deposit into spatial modes, and ranks candidate windows by deposit-weighted similarity in PCA space. We demonstrate the method in the Carajás Mineral Province, Brazil, using total magnetic intensity (TMI) and radiometric U (U) data for IOCG targeting. In a univariate case using the Paulo Afonso deposit as reference, sPCA recovered 54.1% of the total test-deposit area by prediction rank 250, compared with 10.2% for a raw pixel-to-pixel TMI value baseline. In a multivariate case using Alemão as reference, the fused TMI + U workflow recovered 77.7% of the total test-deposit area and outperformed multivariate pixelto-pixel raw and sPCA univariate rankings. These results indicate that sPCA provides a practical framework for similarity-based geophysical prospecting in brownfield exploration. 1. INTRODUCTION Mineral exploration targeting is a decision problem characterized by high impact and high uncertainty [1, 2, 3, 4, 5, 6, 7]. Exploration teams reduce this uncertainty through a phased process that narrows the search from regional scale to target scale, using geological, geophysical, and geochemical data to identify anomalies and reserving drilling for only the best prospects [8, 3, 9, 10]. However, the regionalto-camp targeting phase often remains slow, manual, and difficult to reproduce, and is still strongly dependent on expert judgment [3, 11, 12, 13]. This creates space for complementary data-driven methods that can rank targets faster, more transparently, and more consistently [14, 15]. Data-driven methods are of growing interest because they can help extract meaningful targeting information from large, multi-source datasets and support more systematic and reproducible exploration workflows [16, 15]. Current work is dominated by mineral prospectivity mapping 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 multisource 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.
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