Search Results (71)

Detecting trace metals in soil across geologically diverse terrains remains challenging due to complex mineral–metal interactions and the limited spatial coverage of traditional geochemical tests. This study develops a scalable VIS–NIR–SWIR spectroscopy and machine learning (ML) framework to predict and map soil concentrations of Cr, As, Cu, and Cd in the Aligudarz District, located within the geotectonically complex Sanandaj–Sirjan Zone of western Iran. Laboratory reflectance spectra (~350–2500 nm) obtained from 110 soil samples were pre-processed using derivative filtering, scatter-correction techniques, and genetic algorithm (GA)-based wavelength optimisation to enhance diagnostic absorption features linked to Fe-oxides, clay minerals, and carbonates. Multiple ML-based approaches, including artificial neural networks (ANNs), support vector regression (SVR), and partial least squares regression (PLSR), as well as stepwise multiple linear regression (SMLR), were compared using nested, spatial, and external validation. Nonlinear models, particularly ANNs, exhibited the highest predictive accuracy, with strong generalisation confirmed via an independent test set. GA-selected wavelengths and derivative-enhanced spectra revealed mineralogical controls on metal retention, confirming that spectral predictions reflect underlying geological processes. Ordinary kriging of spectral-ML residuals generated spatially consistent metal-distribution maps that aligned well with local and regional geological features. The integrated framework demonstrates high predictive accuracy and operational scalability, providing a reproducible, field-ready method for rapid geochemical assessment. The findings highlight the potential of VIS–NIR–SWIR spectroscopy, combined with advanced modelling and geostatistics, to support environmental monitoring, mineral exploration, and risk assessment in geologically complex terrains. Full article

Accurate global mapping of lunar iron oxide (FeO) abundance is essential for understanding the Moon’s geological evolution and for supporting future in situ resource utilization (ISRU). While hyperspectral data from the Moon Mineralogy Mapper (M3) provide a unique combination of high spectral dimensionality, hectometre-scale spatial resolution, and near-global coverage, existing FeO retrieval approaches struggle to fully exploit the high dimensionality, nonlinear spectral variability, and planetary-scale volume of the Global Mode dataset. To address these limitations, we present an integrated machine learning pipeline for estimating lunar FeO abundance from M3 hyperspectral observations. Unlike traditional methods based on raw reflectance or empirical spectral indices, the proposed framework combines Discrete Wavelet Transform (DWT), deep autoencoder-based feature compression, and ensemble regression to achieve robust and scalable FeO prediction. M3 spectra (83 bands, 475–3000 nm) are transformed using a Daubechies-4 (db4) DWT to extract 42 representative coefficients per pixel, capturing the dominant spectral information while filtering high-frequency noise. These features are further compressed into a six-dimensional latent space via a deep autoencoder and used as input to a Random Forest regressor, which outperforms kernel-based and linear Support Vector Regression (SVR) as well as Lasso regression in predictive accuracy and stability. The proposed model achieves an average prediction error of 1.204 wt.% FeO and demonstrates consistent performance across diverse lunar geological units. Applied to 806 orbital tracks (approximately $3.5\times {10}^9$ pixels), covering more than 95% of the lunar surface, the pipeline produces a global FeO abundance map at 150 m per pixel resolution. These results demonstrate the potential of integrating multiscale wavelet representations with nonlinear feature learning to enable large-scale, geochemically constrained planetary mineral mapping. Full article

This study investigates the spatial distribution of the hydromagnesite (HM) mineral in Akgöl, a closed basin located in the arid southwestern region of Türkiye, through the integration of geochemical analyses and remote sensing techniques. A total of 70 sediment samples were analyzed using X-ray Fluorescence (XRF), X-ray Diffraction (XRD), and spectroradiometry to determine their mineralogical composition. The resulting data were integrated with ASTER satellite imagery, and mineral distribution maps were generated across 13,293 pixels using multiple linear regression and Kriging interpolation techniques within the ArcGIS environment. The findings indicate that hydromagnesite is predominantly concentrated in the central part of the lake, where it represents the dominant mineral phase in contrast to lower concentrations observed along the periphery. The endorheic nature of Akgöl is comparable to other saline lakes with similar geological and climatic settings, such as Salda and Acıgöl, supporting the applicability of this methodological approach to mineral exploration in other arid and semi-arid environments. The study contributes not only to the regional assessment of mineral potential but also to the advancement of remote sensing and GIS-based analytical methods in geoscientific research. Full article

The Longido region (Tanzania) hosts a distinct corundum–zoisite paragenesis, renowned for its ornamental value and geological significance as a tracer of Pan-African tectonothermal events. Through integrated analyses—including electron probe microanalysis (EMPA), LA-ICP-MS, XRF mapping, and SEM-EDS on five representative samples—we posit that the genetic model for the zoisite–corundum is that pargasite and early-stage corundum were the protolith, which experienced zoisitization prior to hydrothermal fluid influx. This fluid event induced the replacement of zoisite and mechanical compression by newly crystallized corundum. Key findings include textural–chemical concordance: rubies exhibit Al₂O₃ >98 wt.% with Si anomalies (>5000 ppm) in transitional zones, indicative of fluid-mediated replacement of precursor zoisite. Combined mineralogy, this study explored the debated genesis of Longido red corundum–zoisite assemblages, and a rough model was obtained. Full article

Multispectral remote sensing offers powerful capabilities for mineral exploration, particularly in regions with complex geological settings. This study investigates the mineralization potential of the Tidili region in Morocco, located between the South Atlasic and Anti-Atlas Major Faults, using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery to extract hydrothermal alteration zones. Key techniques include band ratio analysis and Principal Components Analysis (PCA), supported by the Crósta method, to identify spectral anomalies associated with alteration minerals such as Alunite, Kaolinite, and Illite. To validate the remote sensing results, field-based geological mapping and mineralogical analysis using X-ray diffraction (XRD) were conducted. The integration of satellite data with ground-truth and laboratory results confirmed the presence of argillic and phyllic alteration patterns consistent with porphyry-style mineralization. This integrated approach reveals spatial correlations between alteration zones and structural features linked to Pan-African and Hercynian deformation events. The findings demonstrate the effectiveness of combining multispectral remote sensing images analysis with field validation to improve mineral targeting, and the proposed methodology provides a transferable framework for exploration in similar tectonic environments. Full article

Integrating renewable energy requires robust, large-scale storage solutions to balance intermittent supply. Underground hydrogen storage (UHS) in geological formations, such as salt caverns, depleted hydrocarbon reservoirs, or aquifers, offers a promising way to store large volumes of energy for seasonal periods. This review focuses on the biological aspects of UHS, examining the biogeochemical interactions between H₂, reservoir minerals, and key hydrogenotrophic microorganisms such as sulfate-reducing bacteria, methanogens, acetogens, and iron-reducing bacteria within the gas–liquid–rock–microorganism system. These microbial groups use H₂ as an electron donor, triggering biogeochemical reactions that can affect storage efficiency through gas loss and mineral dissolution–precipitation cycles. This review discusses their metabolic pathways and the geochemical interactions driven by microbial byproducts such as H₂S, CH₄, acetate, and Fe²⁺ and considers biofilm formation by microbial consortia, which can further change the petrophysical reservoir properties. In addition, the review maps 76 ongoing European projects focused on UHS, showing 71% target salt caverns, 22% depleted hydrocarbon reservoirs, and 7% aquifers, with emphasis on potential biogeochemical interactions. It also identifies key knowledge gaps, including the lack of in situ kinetic data, limited field-scale monitoring of microbial activity, and insufficient understanding of mineral–microbe interactions that may affect gas purity. Finally, the review highlights the need to study microbial adaptation over time and the influence of mineralogy on tolerance thresholds. By analyzing these processes across different geological settings and integrating findings from European research initiatives, this work evaluates the impact of microbial and geochemical factors on the safety, efficiency, and long-term performance of UHS. Full article

Strategies to interpret short-wave infrared hyperspectral images of rocks involve the application of analysis and classification steps that guide the extraction of geological and mineralogical information with the aim of creating mineral maps. Pre-existing strategies often rely on the use of statistical measures between reference and image spectra that are scene dependent. Therefore, classification thresholds based on statistical measures to create mineral maps are also scene dependent. This is problematic because thresholds must be adjusted between images to produce mineral maps of the same accuracy. We developed an innovative, knowledge-based strategy to perform mineralogical analyses and create classifications that overcome this problem by using physics-based wavelength positions of absorption features that are invariant between scenes as the main sources of mineral information. The strategy to interpret short-wave infrared hyperspectral images of rocks is implemented using the open source Hyperspectral Python package (HypPy) and demonstrated on a series of hyperspectral images of hydrothermally altered rock samples. The results show how expert knowledge can be embedded into a standardized processing chain to develop reproducible mineral maps without relying on statistical matching criteria. Full article

Alkaline carbonatite complexes are formed by magmatic, hydrothermal, and weathering geological events, which modify the minerals present in the rocks, resulting in ores with varied metallurgical behavior. To better spatially distinguish ores with distinct plant responses, creating a 3D geometallurgical block model was necessary. To establish the clusters, four different algorithms were tested: K-Means, Hierarchical Agglomerative Clustering, dual-space clustering (DSC), and clustering by autocorrelation statistics. The chosen method was DSC, which can consider the multivariate and spatial aspects of data simultaneously. To better understand each cluster’s mineralogy, an XRD analysis was conducted, shedding light on why each cluster performs differently in the plant: cluster 0 contains high magnetite content, explaining its strong magnetic yield; cluster 3 has low pyrochlore, resulting in reduced flotation yield; cluster 2 shows high pyrochlore and low gangue minerals, leading to the best overall performance; cluster 1 contains significant quartz and monazite, indicating relevance for rare earth elements. A hierarchical indicator kriging workflow incorporating a stochastic partial differential equation (SPDE) trend model was applied to spatially map these domains. This improved the deposit’s circular geometry reproduction and better represented the lithological distribution. The elaborated model allowed the identification of four geometallurgical zones with distinct mineralogical profiles and processing behaviors, leading to a more robust model for operational decision-making. Full article

Amid increasing interest in enhanced oil recovery and carbon geological sequestration programs, improved static reservoir lithofacies models are emerging as a requirement for well-guided project management. Building reservoir models can leverage seismic attribute clustering for seismic facies mapping. One challenge is that machine learning (ML) seismic facies mapping is prone to a wide range of equally possible outcomes when traditional unsupervised ML classification is used. There is a need to constrain ML seismic facies outcomes to limit the predicted seismic facies to those that meet the requirements of geological plausibility for a given depositional setting. To this end, this study utilizes an unsupervised comparative hierarchical and K-means ML classification of the whole 3D seismic data spectrum and a suite of spectral bands to overcome the cluster “facies” number uncertainty in ML data partition algorithms. This comparative ML, which was leveraged with seismic resolution data preconditioning, predicted geologically plausible seismic facies, i.e., seismic facies with spatial continuity, consistent morphology across seismic bands, and two ML algorithms. Furthermore, the variation of seismic facies classes was validated against observed lithofacies at well locations for the Mississippian carbonates of Kansas. The study provides a benchmark for both unsupervised ML seismic facies clustering and an understanding of seismic facies implications for reservoir/saline-aquifer aspects in building reliable static reservoir models. Three-dimensional seismic reflection P-wave data and a suite of well logs and drilling reports constitute the data for predicting seismic facies based on seismic attribute input to hierarchical analysis and K-means clustering models. The results of seismic facies, six facies clusters, are analyzed in integration with the target-interval mineralogy and reservoir porosity. The study unravels the nature of the seismic (litho) facies interplay with porosity and sheds light on interpreting unsupervised machine learning facies in tandem with both reservoir porosity and estimated (Umaa-RHOmaa) mineralogy. Full article

This study investigates the mineral composition of the lunar Dryden Crater using Moon Mineralogy Mapper (M³) data. A RGB false-color composite reveals distinct pyroxene, anorthosite, and possibly spinel distribution patterns. Orthopyroxenes, excavated from deep crustal layers, dominate steep slopes, while plagioclase-rich materials align with magma ocean models of lunar crustal formation. Minor clinopyroxenes indicate impact melt origins. While space weathering and shock metamorphism pose analytical challenges, integrating spectral data with geological context elucidates the crater’s complex history. The resulting mineral distribution map supports targeted exploration during upcoming lunar missions, resource prospecting and resource utilization initiatives within this geologically complex region. Full article

We incorporated a comprehensive dataset encompassing recent measurements from satellites such as the Macau Science Satgellite-1 (MSS-1), Swarm, and CHAMP, as well as aero and ocean magnetic measurements, alongside ground-based data from 1936 to 2000. This amalgamation is the basis for constructing a lithospheric magnetic field model of the Chinese mainland, employing the three-dimensional Surface Spline (3DSS) model. Additionally, we used the World Digital Magnetic Anomaly Map (WDMAM)-2.1 and CHAOS-7.13 models to address data gaps horizontally and vertically. To evaluate the efficacy of the new model, we compared it not only with established models such as SHA1050, NGDC720, and LCS-1 but also with the new model excluding the MSS-1 data. The results show a high agreement between the 3DSS model and other global models at a spatial resolution of 0.05°. Furthermore, we inspected the rapid variations in the magnetic field with increasing altitude, demonstrating a smooth transition across the altitudes covered by the three satellites. Error analyses reflected the importance of MSS-1 data, which contributed notably to modelling by capturing finer-scale magnetic structures. The increased data availability correlated positively with the model’s accuracy, as evidenced by the Root Mean Square Error (RMSE), registering an optimal value of 0.02 nT. The new model reveals additional geological details in southern Tibet, northeastern Inner Mongolia, and the adjacent areas of Liaoning and Jilin provinces, which are not discernible in other global models. The relationship between these anomalies and heat flow in northeastern China appears less evident, suggesting a complex interplay of orogenic processes and surface mineralogy in shaping these magnetic signatures. Full article

The Napo Sumaco Aspiring UNESCO Global Geopark (NSAUGG) in Ecuador represents a genuine variety of geological, cultural, and natural heritage, which aims to promote sustainable development through geotourism. This study describes the significance of NSAUGG, emphasizing its geological diversity which includes a variety of geosites, and focusing on three recently annexed geosites: the Wawa Sumaco Quarry, Puka Urku, and the Pucuno River, where geological analyses, including petrographic and mineralogical assessments, were conducted. To enhance community engagement and educational outreach, a multi-platform mobile application, “SumAppGeo”, was developed using ArcGIS and Flutterflow. This application serves as an interactive tool for visitors and local communities, providing detailed geological information, interactive maps, and educational content. The findings reveal the presence of significant geological features, such as haüyne-bearing alkaline rocks, which indicate specific volcanic activity in this region and are an element of geodiversity, validating the Wawa Sumaco Quarry, Puka Urku, and the Pucuno River as geosites. The implementation of SumAppGeo aims to foster a deeper understanding of the region’s geodiversity while promoting responsible tourism practices. This initiative not only supports the recognition of NSAUGG as part of the UNESCO Global Geoparks Network but also contributes to the socio-economic development of local communities through sustainable tourism practices. Full article

Hydrothermal alteration provides critical information for both the exploration and scientific research of hydrothermal uranium deposits. The Xiangshan uranium ore field, the largest volcanic-hosted uranium deposit in China, is characterized by different alterations, including hematitization, illitization, sericitization, chloritization, carbonation and silicification. However, the mineralogical and geochemical characteristics of hydrothermal alterations and their relationships with uranium mineralization remain unclear. In this study, we conducted detailed petrography, TIMA mapping, μ-XRF analyses, mass balance calculations and thermodynamic modeling on the hematitized and illitized porphyritic lava from the Zoujiashan deposit in the Xiangshan ore field. During hematitization, hematite and albite are produced, while quartz, K-feldspar, chlorite, sericite and biotite are consumed, consistent with the increase in Na₂O, Al₂O₃, Fe₂O₃-T, U, As, Pb, Cu, Sc, V, Zr, Y, Hf and Th and the loss of K₂O, MgO, Li, Zn, Ni and Ba. The production of hydrothermal hematite, illite and sericite indicates that the ore fluids are acidic and oxidized. Such physiochemical conditions are favorable for uranium transport as UO₂Cl₂(aq), UO₂SO₄(aq) and UO₂OH⁺. Geological processes such as fluid–rock interactions, fluid mixing and fluid boiling could cause fO₂(g) decrease, pH increase and temperature decrease and therefore result in the decrease in uranium solubility and mineralization. Full article

In this case study, exploratory techniques were applied for the selection of potential targets for rare-earth elements (REEs) in the Fazenda Buriti Mafic–Ultramafic Complex, part of the Goiás Alkaline Province. The results of the processing and interpretation of aeromagnetic and radiometric data associated with the direct measurements of magnetic susceptibility and radiometry in rock samples collected in the study area allowed for the characterization and delimitation of the geological units. The application of Boolean logic in the radiometric data of uranium (U), thorium (Th), and the U/Th ratio allowed for the generation of a prospective map with the delimitation of two exploration targets. A 100 m deep exploratory drill hole was drilled at the main target, intercepting REE mineralization and validating the developed prospective technique. The results contributed to the detailing of a 1:25,000 scale geological map and the interpretation of shallow and deep magnetic structures. Petrophysical data allowed for the estimation of the magnetite content in the main units of the study area. The delimitation of targets with the applied method proved to be efficient after positive geochemical results for REE from the drilled rocks. The total sum of ∑REEs reached 19,629 ppm in the superficial part of the hole and 3,560 ppm in the fresh rock. Mineralogical results in two follow-up drill core samples indicated that monazite was the main REE mineral. Total REE ranged from 34,746 ppm in HG1 to 30,017 ppm in HG2, with LREEs in its majority. The bulk and clay XRD analyses indicated that monazite consisted of 5.7% (HG1) and 5.1% (HG2). The mineral abundance from the TIMA-X analysis indicated 4.2% (HG1) and 4.4% (HG2) in monazite content. Full article

The inexplicable distribution of souring wells (presence of H₂S gas) of the unconventional Montney Formation hydrocarbon resource (British Columbia; BC) is investigated by analysing sulphur and oxygen isotopes, coupled with XRD mineralogy, scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDX). The sulphur isotopic analysis indicates that the sulphur isotopic range for Triassic anhydrite (δ34S 8.9 to 20.98‰ VCDT) is the same as the H₂S sulphur that is produced from the Montney Formation (δ34S 9.3 to 20.9‰ VCDT). The anhydrite in the Triassic rocks is the likely source of the sulphur in the H₂S produced in the Montney Formation. The deeper Devonian sources are enriched in ³⁴S and are not the likely source for sulphur (δ34S 17.1 and 34‰ VCDT). This is contradictory to studies on Montney Formation producers in Alberta, with heavier (³⁴S-enriched) sulphur isotopic signatures in H₂S gas of all souring Montney Formation producers. These studies conclude that deep-seated faults and fractures have provided conduits for sulphate and/or H₂S gas to migrate from deeper sulphur sources in the Devonian strata. There are several wells that show a slightly heavier (³⁴S-enriched) isotopic signature (δ34S 18 to 20‰ VCDT) within the Montney Formation H₂S gas producing within close proximity to the deformation front. This variation may be due to such deep-seated faults that acted as a conduit for Devonian sulphur to migrate into the Montney Formation. Our geological model suggests the sulphate-rich fluids have migrated from the Charlie Lake Formation prior to hydrocarbon generation in the Montney Formation (BC). Sulphate has concentrated in discrete zones due to precipitation in conduits like fracture and fault systems. The model fits the observation of multi-well pads containing both sour- and sweet-producing wells indicating that the souring is occurring in very narrow and discrete zones with the Montney Formation (BC). Government agencies and operators in British Columbia should map the anhydrite-rich portions of the Charlie Lake Formation, together with the structural elements from three-dimensional seismic to reduce the risk of encountering unexpected souring. Full article