The Bou Dahar Pb-Zn district, located in the Moroccan High Atlas, is a typical carbonate-hosted Pb-Zn ore district (>30 Mt at 4 wt.% Pb, 4 wt.% Zn). In situ trace element analysis was performed using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) on galena and cerussite from different ore types. The galena is generally enriched in Ag and Sb, secondarily enriched in Cu, with a trace amount of Cd and As, but extremely depleted in Bi and Tl. The main substitution mechanism in galena is (Ag, Cu)⁺ + Sb³⁺ ↔ 2Pb²⁺, and at high Sb concentrations, the further substitution of 2Sb³⁺ + □ ↔ 3Pb²⁺ (where □ represents a vacancy) took place. Micro-inclusions of Cu-Sb-bearing minerals (such as tetrahedrite) and Ag-bearing minerals (such as acanthite) may exist in some situations. The features of trace elements in galena show the existence of different coupled substitutions in vein-related ore, breccia-related ore, and strata-bound ore. This suggests that the Bou Dahar district experienced multistage mineralization. The MVT model alone cannot fully explain the ore-forming process. The cerussite replacing strata-bound galena is enriched in Sr, Ba, Ag, and Cu, with minor Sb, As, and Tl. Strontium and Ba are directly substituted with Pb in the cerussite lattice. Copper and Ag are likely present in cerussite as nano-inclusions, which differs from the coupled substitution mechanism of the original galena. High concentrations of Ag may occur due to minor electrum inclusions. The enrichment of Ag, Cu, and Au in cerussite during the oxidation process may guide the optimization of ore processing, especially in extracting valuable trace/minor elements.

Morocco is rich in Mississippi Valley Type (MVT) copper deposits. Currently, geochemical surveying is being conducted in the Goulmima region in pursuit of breakthroughs in mineral exploration. This paper focuses on the delineation of prospecting targets in the Goulmima area based on the ongoing 1:100,000 geochemical survey work in Morocco. The study employs compositional data transformation to perform isometric log-ratio (ilr) transformations on raw data, followed by the Spectrum-Area (S-A) fractal processing, and then uses the Random Forest (RF) algorithm for mineral prediction. Finally, the prediction results are further delineated using the Concentration-Area (C-A) fractal model to identify high-probability areas, marking two prospecting targets. The results show: (1) the ilr transformation reduces the closure problem of the original data and improves their symmetry, thereby more effectively revealing the spatial structural features of the elements; (2) the principal component analysis (PCA) performed on the ilr-transformed data successfully identifies two main element combinations, representing high-temperature hydrothermal environments (Mo-Sn-Ti-W-U) and low-temperature mineralization environments (CaO-Pb-Zn), consistent with the regional mining history; (3) the application of the S-A multifractal model effectively distinguishes between anomalies and background distributions in the geochemical data of the study area, and combines fault buffer zones as the basis for mineral prediction; (4) the C-A fractal model further subdivides the prediction results, dividing potential mining areas into high, medium, and low probability zones, and ultimately identifies two prospecting targets.