The exploration of carbonate-hosted Pb-Zn mineralization is challenging due to the complex structural-geological settings and costly using geophysical and geochemical techniques. Hydrothermal alteration minerals and structural features are typically associated with this type of mineralization. Application of multi-sensor remote sensing satellite imagery as a fast and inexpensive tool for mapping alteration zones and lithological units associated with carbonate-hosted Pb-Zn deposits is worthwhile. Multiple sources of spectral data derived from different remote sensing sensors can be utilized for detailed mapping a variety of hydrothermal alteration minerals in the visible near infrared (VNIR) and the shortwave infrared (SWIR) regions. In this research, Landsat-8, Sentinel-2, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and WorldView-3 (WV-3) satellite remote sensing sensors were used for prospecting Zn-Pb mineralization in the central part of the Kashmar–Kerman Tectonic Zone (KKTZ), the Central Iranian Terrane (CIT). The KKTZ has high potential for hosting Pb-Zn mineralization due to its specific geodynamic conditions (folded and thrust belt) and the occurrence of large carbonate platforms. For the processing of the satellite remote sensing datasets, band ratios and principal component analysis (PCA) techniques were adopted and implemented. Fuzzy logic modeling was applied to integrate the thematic layers produced by image processing techniques for generating mineral prospectivity maps of the study area. The spatial distribution of iron oxide/hydroxides, hydroxyl-bearing and carbonate minerals and dolomite were mapped using specialized band ratios and analyzing eigenvector loadings of the PC images. Subsequently, mineral prospectivity maps of the study area were generated by fusing the selected PC thematic layers using fuzzy logic modeling. The most favorable/prospective zones for hydrothermal ore mineralizations and carbonate-hosted Pb-Zn mineralization in the study region were particularly mapped and indicated. Confusion matrix, field reconnaissance and laboratory analysis were carried out to verify the occurrence of alteration zones and highly prospective locations of carbonate-hosted Pb-Zn mineralization in the study area. Results indicate that the spectral data derived from multi-sensor remote sensing satellite datasets can be broadly used for generating remote sensing-based prospectivity maps for exploration of carbonate-hosted Pb-Zn mineralization in many metallogenic provinces around the world.

Since the Gol-Gohar iron ore mine (GGIOM), which is located in southeastern Iran, is currently one of the biggest iron mines in this region, increasing the accuracy of its mineral resources model has become a challenge for geologists, metallurgists and mining engineers. Given that an accurate classification of the mining blocks into ore or waste is highly significant in strategic mine planning, three approaches for simulating the iron grades were compared against the true grades obtained from production data. The comparison was done by calculating the ratio between the total number of blocks correctly classified as ore and waste and the total number of misclassified blocks, and it was conducted for each approach in three mined benches at the GGIOM. The results reveal that the grade simulation that ignores the geological boundaries and the grade simulation based on a deterministic geological interpretation are much less accurate than the hierarchical approach, which consists of simulating both the geological boundaries and the grades.

The McMurdo Dry Valleys (DVs) of South Victoria Land, Antarctica, constitute the largest ice-free region on the continent and one of Earth’s most Mars-analog environments. Their hyper-arid polar desert conditions offer a unique setting for investigating surface weathering and mineralogical processes under extreme climates. This study presents the first regional-scale mapping of alteration and crystalline weathering minerals across the McMurdo DVs. It uses Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral data; visible and near-infrared (VNIR) and shortwave infrared (SWIR) bands were analyzed through a Spectral Hourglass Workflow, endmember extraction, and spectral unmixing with Matched Filtering (MF) and Constrained Energy Minimization (CEM). Inter-algorithm consistency analysis between MF and CEM yielded 78.83% overall agreement with a Kappa coefficient of 0.75, indicating strong methodological consistency in mineral discrimination using ASTER VNIR+SWIR data. It should be noted that this agreement reflects internal algorithmic robustness rather than independent geological validation. Geological reliability is instead supported by documented field observations, lithological map comparisons, and spectral correspondence with the USGS spectral library. Validation employed documented field observations, lithological maps, and the USGS spectral library. Results reveal distinct spatial distributions of hematite-limonite/goethite, jarosite, kaolinite/smectite-illite-pyrophyllite-alunite, muscovite, hydrous silica/sericite/jarosite/hematite, epidote/chlorite, and calcite, closely associated with lithological units and unconsolidated deposits in Taylor, Wright, Victoria, and McKelvey Valleys. An inter-algorithm consistency check achieved 78.83% overall accuracy with a Kappa coefficient of 0.75, underscoring the robustness of ASTER VNIR+SWIR data for Antarctic mineral discrimination despite localized spectral mixing. Beyond refining the geological understanding of the McMurdo DVs, these results establish ASTER as an effective tool for regional mineralogical mapping in inaccessible polar terrains. The findings further strengthen the role of the Dry Valleys as a terrestrial analog for Mars, where similar mineralogical assemblages and spectral ambiguities have been observed, thereby contributing to both Antarctic geoscience and planetary exploration frameworks.

Modeling and estimating radon concentration are of crucial interest to support health protection campaigns. In the literature, many studies concentrated on indoor radon, while few of them investigated the outdoor radon spatial distribution and the factors that influence its formation. In this context, the vast possibilities of the artificial intelligence systems, based on machine learning techniques, can show remarkable capabilities. This paper focuses on the optimization of the architecture and the parameters of an artificial neural network (ANN) for inferring outdoor radon concentrations. More specifically, in the development of alternative ANN models, the Feed‐Forward Back propagation with the Levenberg–Marquardt is performed with different hidden layers to train the models and a bootstrap resampling method is applied to improve the model generalization. Some evaluation metrics and a sensitivity analysis are also included in order to assess the prediction accuracy among the ANN models.

The presence of radioactivity in the environment results in substantial effects to human health, thus a full understanding of contributing factors in this context is of great interest. The present study considers geological features, permeability, and climatic variations factors, as well as the concentrations of uranium, thorium, and potassium in the soil and the radon in soil gas data collected from 32 sites in the province of Lecce, Italy, in 2012 to assess radon exposure risks by using Artificial Neural Network (ANN) method, which offers valuable insights into potential health implications and aids in devising risk mitigation strategies. In particular, this study uses the distance of lithological units based on uranium content in bed rock to highlight regions with both high and low uranium concentrations. Geological inhomogeneities, including faults, caves, dolines and sinkholes significantly influence radon concentrations, with proximity to faults correlating with higher radon emissions. Distances to these geological features and their densities are considered as an important factor to predict radon concentrations. The effect of soil permeability on radon concentration is evaluated by taking into account the available water capacity, bulk density and fine fraction in the area under study. Finally, the influence of climatic variations, including barometric pressure, air temperature and wind speed, is considered to forecast radon concentration in soil. Using this comprehensive dataset, the ANN method is employed to predict and analyze radon exposure risks. The resulting ANN model demonstrates exceptional accuracy and reliability and a high Goodness-of-Prediction. Subsequently, drawing on forecasting radon concentrations across the province, a detailed contour map is obtained for illustrating the spatial distribution of radon concentration in different areas. Notably, specific regions in the central and south-western areas show radon in soil gas concentrations exceeding 30 KBq/m³. This model offers critical insights for evaluating exposure risks and guiding future research and mitigation strategies. It also underscores the importance of considering uranium content, geological factors, and climate variations. Finally, it serves as a robust tool for policymakers and researchers to make informed decisions to address environmental challenges and prioritize public safety. Acknowledgement We would like to extend our heartfelt gratitude to Dr. Giorgia Cinelli for invaluable assistance and guidance throughout the course of this project, which greatly enriched our study. References De Iaco, S., Maggio, S., & Palma, M. (2017). Radon Predictions with Geographical Information System Covariates: From Spatial Sampling to Modeling. Geographical Analysis, 49(2), 215-235. Oni, O. M., Aremu, A. A., Oladapo, O. O., Agboluaje, B. A., & Fajemiroye, J. A. (2022). Artificial neural network modeling of meteorological and geological influences on indoor radon concentration in selected tertiary institutions in Southwestern Nigeria. Journal of Environmental Radioactivity, 251, 106933. Farai IP, Fajemiroye JA, Oni OM, Aremu AA. Artificial neural network modeling of soil gas radon concentration on different lithologies for Southwest Nigeria. J Radiat Res Appl Sci. 2022 Sep;15(3):346–52. Neznal M, Neznal M, Matolín M, Barnet I, Miksova J. The new method for assessing the radon risk of building sites Nová metodika stanovení radonového indexu pozemku.