Petrophysical characterization of rocks is essential for the reliable interpretation of gravity and magnetic fields in ore districts where sedimentary, volcanogenic, and intrusive rocks occur in complex structural relationships. This study uses a database comprising 643 bulk-density determinations and 650 magnetic-susceptibility determinations obtained from drill-core and hand-specimen samples from the southeastern part of the Sarysu–Teniz uplift, Central Kazakhstan. In this study, these measurements are presented and interpreted in an aggregated form by the main lithostratigraphic and lithological groups; for each group, the number of determinations, weighted-average values, and diagnostic petrophysical characteristics are reported. The unit-level weighted-average density values range from 2.14 to 2.73 g/cm³, whereas mean magnetic-susceptibility values vary from 0 to 913 × 10⁻⁵ SI. Carbonate-dominated units, including limestones, marls, dolomites, and dolomitized limestones, are non-magnetic to practically non-magnetic, mostly at 0–14 × 10⁻⁵ SI. The highest values are recorded in andesite-basalts and quartz syenite porphyries, reaching 766–913 × 10⁻⁵ SI; granodiorites have an average value of approximately 452 × 10⁻⁵ SI. Density values partly overlap between sedimentary and intrusive rocks, especially within the interval range of 2.48–2.66 g/cm³, whereas magnetic susceptibility provides a more reliable criterion for distinguishing carbonate host rocks from magnetite-bearing magmatic assemblages. The combined density–magnetic susceptibility framework indicates that exploration targeting should prioritize bodies with elevated magnetic susceptibility or sharp magnetic gradients, especially where χ > 450 × 10⁻⁵ SI, provided that these features coincide with faults, lithological contacts, practically non-magnetic carbonate host rocks, and independent geochemical evidence or known mineral occurrences. Consequently, magnetic anomalies are interpreted as indirect structural–lithological indicators rather than as direct evidence of ore bodies.

Mineral prospectivity mapping (MPM) increasingly relies on machine learning classifiers, but model performance may be overstated when spatial dependence, extrapolation beyond the training domain, and poorly calibrated prediction scores are not adequately addressed. This study presents a reproducible workflow for regional Au-Ag prospectivity mapping in Nevada that integrates positive–unlabeled learning with Random Forest models and spatial GroupKFold cross-validation. Predictions were restricted to the Area of Applicability (AOA), defined using the 0.95 quantile of a training-derived feature-space dissimilarity index, resulting in areal coverage of 54.331% for Ag and 73.118% for Au. Discrimination based on pooled out-of-fold (OOF) predictions was strong for both commodities (Ag:AP = 0.745, ROC-AUC = 0.908; Au:AP = 0.777, ROC-AUC = 0.912). Post hoc OOF calibration with isotonic regression reduced Brier scores and improved probability reliability within the supported prediction domain. Area-based validation showed that the highest-ranked 10% of the AOA captured 85.6% of the Ag and 84.2% of the Au pooled-OOF positives, corresponding to targeting efficiencies of 8.6-fold and 8.4-fold relative to random spatial selection. Ensemble dispersion was used to map predictive uncertainty and to identify areas where additional data may be most useful for reducing uncertainty. SHAP importance and partial-dependence diagnostics consistently highlighted proximity to Quaternary faults, elevated slip and dilation tendency, and potential-field edges associated with intrusive or volcanic contacts, while surface heat flow acted mainly as a permissive control. The resulting AOA-masked prospectivity maps, uncertainty layers, and top-k targeting products provide a transparent regional framework for prioritizing follow-up exploration and allocating reconnaissance effort.

The southeastern Sarysu–Teniz Uplift in Central Kazakhstan is partly concealed beneath Cenozoic sediments, which complicates the correlation and age assignment of its Paleozoic magmatic rocks. This study combines new zircon U–Pb ages from four felsic volcanic samples with whole-rock major-element data for volcanic and intrusive rocks to constrain the timing of magmatism and assess their compositional relationships. The principal zircon populations yield concordia ages of 414 ± 3, 415 ± 2, 421 ± 2, and 414 ± 2 Ma, with uncertainties reported at 2σ, indicating Late Silurian–Early Devonian magmatism. Older populations at 440 ± 6 and 438 ± 6 Ma and a single ca. 2.67 Ga zircon document older components, although their provenance remains unresolved. Comparison with previously published intrusive ages supports partial temporal overlap between volcanic and plutonic activity, but does not establish a single magmatic system. Major-element variations are compatible with differentiation involving fractional crystallization, while crustal melting, assimilation, magma mixing, and source heterogeneity remain possible contributors. The available geochemical data do not uniquely identify source lithologies or tectonic setting. These results refine the chronology of the sampled volcanic rocks and provide a basis for revising their regional correlation and relationship with intrusive magmatism.