demonstrated strong capability in identifying predictive patterns within complex spatial datasets. By learning relationships directly from data, ML-based approaches can reduce interpretative bias and improve consistency in mineral prospectivity. However, despite growing adoption of ML in exploration, challenges remain regarding reproducibility, transparency, systematic integration of heterogeneous datasets, and spatial validation robustness. There is a need for automated and scalable workflows that objectively integrate multidisciplinary geological information while preserving geological interpretability. In this context, the objective of this study is to develop and evaluate an automated, reproducible Machine Learning workflow for copper porphyry prospectivity mapping in the Arequipa region of southern Peru. The proposed framework integrates multidisciplinary datasets within a unified and scalable spatial modeling environment to evaluate predictive performance and geological coherence. By reducing subjectivity, standardizing data integration, and accelerating target generation, it supports more efficient exploration investment decisions and contributes to faster, smarter, and more responsible mineral discovery. 2. MATERIALS AND METHODS 2.1 Study area and Multidimensional Datasets The study was conducted in the Arequipa region, southern Peru, a zone of significant CuMo porphyry mineralization. The regional geology comprises Mesozoic sedimentary rocks (Yura Group), intrusive bodies of the Coastal Batholith, Cretaceous carbonate units, and Cenozoic volcanic sequences (Tacaza and Barroso Groups), as documented in regional geological quadrangle studies (Avendaño et al., 2000; Bellido & Guevara, 1963; García Márquez, 1968; Guevara Rosillo, 1969; Guizado Jol, 1968; Pecho Gutiérrez & Morales Serrano, 1969) , which host deposits such as Cerro Verde, Tía María, and Zafranal. All data were obtained from public repositories to ensure reproducibility and scalability and datasets were standardized and aggregated into a unified 500 × 500 m spatial grid to ensure consistent feature representation across variables. Eight ASTER satellite images (HDF format 15 x 15 m resolution) were downloaded from NASA's Earthdata platform. Public geochemical data (stream sediment and rock samples), structural data (lineaments, faults), and geological cartography were acquired from the GEOCATMIN portal of INGEMMET (Figure 1). The geophysical data consisted of a magnetic anomaly grid with Reduction-to-the-Pole (RTP) correction (TIF format).
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