Minerals

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19 pages, 9965 KB

Open AccessEditor’s ChoiceArticle

Zircon U–Pb and Lu–Hf Signatures for the Gold Porphyry Deposits in the Alta Floresta Gold Province (Amazonian Craton, Brazil): Implications for Regional Exploration

by Léo Adriano de Oliveira, Natã José de França, Antônio João Paes de Barros, André Campos Rocha Pinto, Guilherme Loriato Potratz, Armando Dias Tavares, Luiz Pinheiro and Mauro Cesar Geraldes

Minerals 2026, 16(8), 789; https://doi.org/10.3390/min16080789 - 29 Jul 2026

Viewed by 385

Abstract

The present work focuses on investigating U–Pb and Lu–Hf ages of zircon grains obtained from Au mineralized granitic rocks from Alta Floresta Gold Province (AFGP), in the Amazonia craton, Brazil. Also, detrital zircon grains from recent Au-rich sediments were analyzed for U–Pb ages [...] Read more.

The present work focuses on investigating U–Pb and Lu–Hf ages of zircon grains obtained from Au mineralized granitic rocks from Alta Floresta Gold Province (AFGP), in the Amazonia craton, Brazil. Also, detrital zircon grains from recent Au-rich sediments were analyzed for U–Pb ages and Lu–Hf isotopic signatures to apply to regional gold exploration in the AFGP to define targets consisting of deep-seated mineralized bodies using isotopic tracers. U–Pb ages of magmatic rocks, obtained from zircon grains, indicate crystallization ages ranging from 1896 Ma to 1825 Ma for rocks associated with gold deposits, and TDM model ages between 2.65 and 1.96 Ga and εHf values between +8.0 and −4.6. U–Pb ages obtained from 89 detrital zircon grains indicate peaks at 2055–1980 Ma, 1889–1985 Ma, 1790–1700 Ma, and 1600–1550 Ma. The first group is related to the basement, represented by the rocks of the Cuiu-Cuiu Complex (TDM 2.0–1.9 Ga and εHf from +8.7 to −1.32). The second group concerns the mineralized magmatic rocks in the AFPG, and the ages range from 18,896 to 1825 Ma, TDM ages ranging from 2.90 to 2.75 Ga, and the εHf values from +15 to −8. The age ranging from 1790 to 1700 is associated with anorogenic magmatism (TDM 2.0–1.9 Ga and εHf from +5.36 to −8.46). The youngest group comprises ages from 1600 to 1550 Ma, probably correlated to the Serra da Providencia suite with TDM of 1,6 to 1,50 Ga and εHf from +5,0 to −10. In this way, it was possible to identify the age range of detrital zircon grains concerning the ages of mineralized granites and characterize the Hf signatures associated with the mineralization processes. In this sense, the zircon grains associated with the magmatic processes responsible for the concentration of noble metals exhibited U–Pb ages and ε_Hf values interpreted as having been generated in magmas originating from a depleted mantle. As suggested in the literature, the magma characteristics reported here are coherent with a hot, hydrous, Fe-rich, high-K calc-alkaline, and subvolcanic emplacement. Based on the model that correlates gold deposits with porphyritic magmatic processes, significant discoveries with world-class volumes may result from better characterization of the deposit types found in the province. Full article

(This article belongs to the Special Issue Feature Papers in Mineral Exploration Methods and Applications 2025)

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22 pages, 7747 KB

Open AccessEditor’s ChoiceArticle

Integrated Multiphysics Inversion for Geothermal and Lithium Exploration in Dixie Valley, Nevada

by Michael S. Zhdanov, Michael Jorgensen, Leif H. Cox and Alex Gribenko

Minerals 2026, 16(8), 774; https://doi.org/10.3390/min16080774 - 25 Jul 2026

Viewed by 239

Abstract

Dixie Valley, located in west-central Nevada within the Basin and Range Province, is one of the most important geothermal systems in the western United States and an increasingly attractive target for critical-mineral exploration. The valley combines active extensional tectonics, major range-front and intrabasin [...] Read more.

Dixie Valley, located in west-central Nevada within the Basin and Range Province, is one of the most important geothermal systems in the western United States and an increasingly attractive target for critical-mineral exploration. The valley combines active extensional tectonics, major range-front and intrabasin fault systems, high heat flow, hydrothermal alteration, and thick sedimentary basins that may provide favorable conditions for the development of geothermal reservoirs and lithium-bearing brines or clays. This paper presents an integrated multiphysics interpretation of gravity, magnetic, helicopter-borne time-domain electromagnetic (HeliTEM), and magnetotelluric (MT) data from Dixie Valley, with emphasis on the Grover Point area investigated by the Basin and Range Investigation for Developing Geothermal Energy (BRIDGE) program. We apply joint Gramian inversion of gravity and magnetic data to recover mutually consistent density and magnetization models, including separate induced and remanent magnetization components. We also perform rigorous 3D inversion of HeliTEM data and cooperative 3D inversion of HeliTEM and MT data to obtain a resistivity model extending from the shallow basin fill to deeper fault-controlled geothermal structures. The integrated interpretation identifies low-density sedimentary basins, induced magnetization highs related to magnetic basement or intrusive rocks, remanent magnetization variations associated with basement architecture and hydrothermal alteration, and conductive corridors interpreted as clay-rich alteration zones and possible hydrothermal pathways. These results demonstrate that integrated gravity, magnetic, HeliTEM, and MT inversion can substantially reduce interpretation ambiguity and improve targeting of concealed geothermal systems and associated lithium resources in extensional terranes. Full article

(This article belongs to the Special Issue Feature Papers in Mineral Exploration Methods and Applications 2025)

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24 pages, 29255 KB

Open AccessArticle

Stochastic Inversion of Geophysical Data by Sequential Bayesian Updating Under a Non-Stationary Gaussian Process Prior

by Jef Karel Caers, Peng Li, Jonas Kloeckner, Juan Pablo Daza, David Zhen Yin and Céline Scheidt

Minerals 2026, 16(7), 736; https://doi.org/10.3390/min16070736 - 14 Jul 2026

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Abstract

The acquisition of geophysical data is becoming increasingly important in the context of critical mineral exploration. Geophysical data and inversion products are essential to map many components of the critical mineral system by detecting geophysical anomalies that can be interpreted by expert geologists. [...] Read more.

The acquisition of geophysical data is becoming increasingly important in the context of critical mineral exploration. Geophysical data and inversion products are essential to map many components of the critical mineral system by detecting geophysical anomalies that can be interpreted by expert geologists. However, the inversion of airborne geophysical data acquired along flightlines into subsurface petrophysical properties remains an outstanding challenge. Many inversion techniques rely either on 1D deterministic inversion or on stochastic inversion on a local scale. The outcome of our work is the stochastic inversion along flightlines of 2D panels (flightline direction vs. depth), while at the same time producing plausible spatial variation in the petrophysical properties. Our method relies on a sequential application of Bayesian inversion, where we invert a sequence of 2D panels such that the variation in petrophysical properties avoids generation of artifacts across the panel boundaries. We show that our method can be used in a practical setting in the context of mineral exploration in the Cape Smith Belt of Canada. Full article

(This article belongs to the Special Issue Feature Papers in Mineral Exploration Methods and Applications 2025)

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33 pages, 21041 KB

Open AccessEditor’s ChoiceArticle

Machine Learning- and Remote Sensing-Based Lithological Mapping Using VNIR + SWIR PRISMA Hyperspectral and ASTER Multispectral Datasets in Northwest of Queensland

by Laleh Jafari, Ioan V. Sanislav, Ben Jarihani, Stephanie Duce and Jack Koci

Minerals 2026, 16(7), 720; https://doi.org/10.3390/min16070720 - 9 Jul 2026

Viewed by 964

Abstract

Lithological mapping is essential for geological studies, mineral exploration, and environmental assessment. Satellite remote sensing combined with machine learning provides a scalable, cost-effective approach for regional lithological discrimination. This study evaluates hyperspectral and multispectral satellite imagery for lithological mapping in a geologically complex [...] Read more.

Lithological mapping is essential for geological studies, mineral exploration, and environmental assessment. Satellite remote sensing combined with machine learning provides a scalable, cost-effective approach for regional lithological discrimination. This study evaluates hyperspectral and multispectral satellite imagery for lithological mapping in a geologically complex region of northwestern Queensland, Australia. The study area, within the Mount Isa Inlier, comprises diverse sedimentary, volcanic, intrusive, and metamorphic lithologies. PRISMA hyperspectral and ASTER multispectral imagery were analyzed using supervised classification algorithms, including Support Vector Machine (SVM), Mahalanobis Distance (MaDC), Minimum Distance (MDC), and Maximum Likelihood (MLC). Image-derived endmembers from representative lithologies were used as training data. Classification accuracy was assessed using confusion matrices, Overall Accuracy (OA), and the Kappa coefficient. PRISMA imagery outperformed ASTER data. SVM achieved the highest performance for PRISMA (OA = 82.03%, Kappa = 0.81), whereas MLC achieved the highest performance for ASTER (OA = 33.29%, Kappa = 0.30). Classification accuracy was evaluated using an independent set of validation ROIs that were spatially separated from the training samples, providing a more reliable estimate of model performance. These results highlight the benefits of hyperspectral remote sensing with machine learning for lithological discrimination in complex terrain and emphasise the importance of spatially independent validation. The approach demonstrates strong potential for regional-scale applications and may support more efficient mineral exploration and geological mapping workflows. Full article

(This article belongs to the Special Issue Feature Papers in Mineral Exploration Methods and Applications 2025)

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16 pages, 7787 KB

Open AccessEditor’s ChoiceArticle

Advanced 3D Inversion of Airborne EM and Magnetic Data with IP Effects and Remanent Magnetization Modeling: Application to the Mpatasie Gold Belt, Ghana

by Michael S. Zhdanov, Leif H. Cox, Michael Jorgensen and Douglas H. Pitcher

Minerals 2025, 15(12), 1305; https://doi.org/10.3390/min15121305 - 15 Dec 2025

Cited by 2 | Viewed by 1503

Abstract

We present an integrated methodology for three-dimensional inversion of large-scale airborne electromagnetic (AEM) and magnetic survey data that simultaneously recovers electrical conductivity, chargeability, and both induced and remanent magnetizations. A central feature of the AEM component is the explicit incorporation of induced polarization [...] Read more.

We present an integrated methodology for three-dimensional inversion of large-scale airborne electromagnetic (AEM) and magnetic survey data that simultaneously recovers electrical conductivity, chargeability, and both induced and remanent magnetizations. A central feature of the AEM component is the explicit incorporation of induced polarization (IP) effects. Neglecting IP responses can lead to biased conductivity models, particularly in mineralized systems where disseminated sulfides contribute strongly to chargeability. Using the Generalized Effective-Medium Theory of Induced Polarization (GEMTIP), the inversion produces physically consistent 3D distributions of conductivity and chargeability. To enhance magnetic interpretation, we also implement a vector magnetic inversion that resolves both induced and remanent magnetization from Total Magnetic Intensity (TMI) data, enabling geologically realistic magnetization models in terranes with significant remanence. This integrated workflow was applied to airborne AEM and TMI datasets collected over the Asankrangwa Gold Belt in central Ghana. The inversion results delineate a key exploration target defined by coincident magnetic low and elevated chargeability, interpreted as sulfide-rich gold mineralization and subsequently confirmed by drilling. These results demonstrate that jointly accounting for IP and remanent magnetization in 3D inversion substantially improves subsurface characterization and provides a powerful tool for mineral exploration in structurally and lithologically complex environments. Full article

(This article belongs to the Special Issue Feature Papers in Mineral Exploration Methods and Applications 2025)

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16 pages, 24769 KB

Open AccessArticle

Phase Transformation of the Analytic Signal for Enhancing the Resolution of Potential Field Data

by Saulo Pomponet Oliveira, Milton José Porsani, Maximilian Fries and Marieli Machado Zago

Minerals 2025, 15(12), 1266; https://doi.org/10.3390/min15121266 - 29 Nov 2025

Cited by 2 | Viewed by 1270

Abstract

Enhancement methods based on first-order derivatives are well established tools for gravity and magnetic data processing. Higher-resolution filters have been developed using high-order derivatives, but they are generally more sensitive to noise. Based on a transformation that sharpens the instantaneous phase of the [...] Read more.

Enhancement methods based on first-order derivatives are well established tools for gravity and magnetic data processing. Higher-resolution filters have been developed using high-order derivatives, but they are generally more sensitive to noise. Based on a transformation that sharpens the instantaneous phase of the complex analytic signal, which corresponds to the tilt angle map, we obtain an enhancement filter that improves the resolution of the total horizontal gradient (THG) without the need for additional derivative calculations. The steps of the proposed method are as follows: (1) compute the horizontal and vertical derivatives of the data; (2) compute the tilt angle and the analytic signal amplitude; (3) apply a sigmoidal-type transformation to the tilt angle; and (4) reconstruct the THG from the analytic signal amplitude and the transformed tilt angle. The reconstructed THG provides sharper boundary estimates than the true THG, as qualitatively shown with noise-corrupted synthetic data and aeromagnetic data from the Seival copper mining area in Caçapava do Sul, Brazil. Full article

(This article belongs to the Special Issue Feature Papers in Mineral Exploration Methods and Applications 2025)

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16 pages, 3342 KB

Open AccessArticle

Geoscientific Input Feature Selection for CNN-Driven Mineral Prospectivity Mapping

by Arya Kimiaghalam, Kyubo Noh and Andrei Swidinsky

Minerals 2025, 15(12), 1237; https://doi.org/10.3390/min15121237 - 23 Nov 2025

Cited by 1 | Viewed by 989

Abstract

In recent years, machine learning techniques such as convolutional neural networks have been used for mineral prospectivity mapping. Since a diverse range of geoscientific data is often available for training, it is computationally challenging to select a subset of features that optimizes model [...] Read more.

In recent years, machine learning techniques such as convolutional neural networks have been used for mineral prospectivity mapping. Since a diverse range of geoscientific data is often available for training, it is computationally challenging to select a subset of features that optimizes model performance. Our study aims to demonstrate the effect of optimal input feature selection on convolutional neural network model performance in mineral prospectivity mapping applications. We demonstrate results from both exhaustive and algorithmic feature selection methods in the context of copper porphyry prospectivity modeling and analyze the performance and stability of optimally trained models. Using the QUEST dataset from central interior British Columbia, such a feature selection technique improves model performance by 6.8% over models that use all available features, yet consumes around 2.2% of the computational resources needed to exhaustively search for the optimal feature subset. Full article

(This article belongs to the Special Issue Feature Papers in Mineral Exploration Methods and Applications 2025)

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29 pages, 16357 KB

Open AccessArticle

Evaluation of Heterogeneous Ensemble Learning Algorithms for Lithological Mapping Using EnMAP Hyperspectral Data: Implications for Mineral Exploration in Mountainous Region

by Soufiane Hajaj, Abderrazak El Harti, Amin Beiranvand Pour, Younes Khandouch, Abdelhafid El Alaoui El Fels, Ahmed Babeker Elhag, Nejib Ghazouani, Mustafa Ustuner and Ahmed Laamrani

Minerals 2025, 15(8), 833; https://doi.org/10.3390/min15080833 - 5 Aug 2025

Cited by 6 | Viewed by 2387

Abstract

Hyperspectral remote sensing plays a crucial role in guiding and supporting various mineral prospecting activities. Combined with artificial intelligence, hyperspectral remote sensing technology becomes a powerful and versatile tool for a wide range of mineral exploration activities. This study investigates the effectiveness of [...] Read more.

Hyperspectral remote sensing plays a crucial role in guiding and supporting various mineral prospecting activities. Combined with artificial intelligence, hyperspectral remote sensing technology becomes a powerful and versatile tool for a wide range of mineral exploration activities. This study investigates the effectiveness of ensemble learning (EL) algorithms for lithological classification and mineral exploration using EnMAP hyperspectral imagery (HSI) in a semi-arid region. The Moroccan Anti-Atlas mountainous region is known for its complex geology, high mineral potential and rugged terrain, making it a challenging for mineral exploration. This research applies core and heterogeneous ensemble learning methods, i.e., boosting, stacking, voting, bagging, blending, and weighting to improve the accuracy and robustness of lithological classification and mapping in the Moroccan Anti-Atlas mountainous region. Several state-of-the-art models, including support vector machines (SVMs), random forests (RFs), k-nearest neighbors (k-NNs), multi-layer perceptrons (MLPs), extra trees (ETs) and extreme gradient boosting (XGBoost), were evaluated and used as individual and ensemble classifiers. The results show that the EL methods clearly outperform (single) base classifiers. The potential of EL methods to improve the accuracy of HSI-based classification is emphasized by an optimal blending model that achieves the highest overall accuracy (96.69%). The heterogeneous EL models exhibit better generalization ability than the baseline (single) ML models in lithological classification. The current study contributes to a more reliable assessment of resources in mountainous and semi-arid regions by providing accurate delineation of lithological units for mineral exploration objectives. Full article

(This article belongs to the Special Issue Feature Papers in Mineral Exploration Methods and Applications 2025)

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