Search Results (26)

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

Groundwater is a critical resource in semi-arid regions like Morocco’s Guire Basin, yet pollution and overexploitation threaten its sustainability. This study evaluates the groundwater quality of the Guire aquifer (Eastern High Atlas) using an integrated approach combining hydrochemical, isotopic (δ¹⁸O, δ²H, δ¹³C), multivariate statistical, and Geographic Information System (GIS) analyses alongside the Water Quality Index (WQI). Sixteen wells were monitored for physicochemical parameters (pH: 7–7.9; EC: 480–3004 μS/cm; BOD5: 1.03–30.5 mg/L; COD: 10.2–45.75 mg/L) and major ions, revealing widespread exceedances of Moroccan standards for Cl⁻, HCO₃⁻, Mg²⁺, Ca²⁺, and NH₄⁺. WQI classified 81% of samples as “Poor” to “Unsuitable for drinking” (WQI: 51–537), driven by elevated Cl⁻, Na⁺, and SO₄²⁻ from Triassic evaporite dissolution and NO₃⁻ (up to 45 mg/L) from agricultural runoff. Stable isotopes (δ¹⁸O: −7.73‰ to −5.08‰; δ²H: −66.14‰ to −44.20‰) indicate Atlantic-influenced recharge at 900–2200 m altitudes, with a δ¹⁸O-δ²H slope of 5.93 reflecting evaporation during infiltration. Strontium (Sr²⁺/Ca²⁺: 0.0024–0.0236) and bromide (Br/Cl: 8.47 × 10⁻⁵–9.88 × 10⁻⁴) ratios further confirm evaporitic dominance over anthropogenic contamination. This work provides actionable insights for policymakers, advocating for targeted restrictions on fertilizers, enhanced monitoring near evaporite zones, and artificial recharge initiatives. By linking geogenic/anthropogenic contamination to governance strategies, this study advances sustainable groundwater management in semi-arid regions. Full article

High-accuracy gully erosion susceptibility maps play a crucial role in erosion vulnerability assessment and risk management. The principal purpose of the present research is to evaluate the predictive power of individual machine learning models such as random forest (RF), decision tree (DT), and support vector machine (SVM), and ensemble machine learning approaches such as stacking, voting, bagging, and boosting with k-fold cross validation resampling techniques for modeling gully erosion susceptibility in the Oued El Abid watershed in the Moroccan High Atlas. A dataset comprising 200 gully points, identified through field observations and high-resolution Google Earth imagery, was used, alongside 21 gully erosion conditioning factors selected based on their importance, information gain, and multi-collinearity analysis. The exploratory results indicate that all derived gully erosion susceptibility maps had a good accuracy for both individual and ensemble models. Based on the receiver operating characteristic (ROC), the RF and the SVM models had better predictive performances, with AUC = 0.82, than the DT model. However, ensemble models significantly outperformed individual models. Among the ensembles, the RF-DT-SVM stacking model achieved the highest predictive accuracy, with an AUC value of 0.86, highlighting its robustness and superior predictive capability. The prioritization results also confirmed the RF-DT-SVM ensemble model as the best. These findings highlight the superiority of ensemble learning models over individual ones and underscore their potential for application in similar geo-environmental contexts. Full article

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. Full article

This research investigates the post-earthquake performances of structures in four rural villages in the Moroccan Atlas, emphasizing common construction system characteristics and design flaws that render buildings susceptible to seismic events. Village selection was based on a prior multispectral satellite-image study, proving effective for planning high-impact, post-earthquake field campaigns. The significance of this research resides in on-site data collection, facilitating the physical assessment of earthquake-induced damage and identification of inherent vulnerabilities in construction systems. The constructions in the study area exhibited structural design deficiencies, inadequate construction techniques, and urban modifiers, leading to damage extensively documented in the literature, as well as less-documented unique damage. Predominant seismic-design shortcomings in the study area included subpar material quality, insufficient earthquake-resistant design, and unskilled labor. In situ data were complemented by a global geospatial approach using differential synthetic aperture radar interferometry with Copernicus Sentinel 1 data. Once calibrated the proposed methodology with field data, the analysis of remote sensing processing results, allow assessing the damages in other earthquake-affected areas, including those not visited in the field but also impacted by the seismic event. Full article

Recently, microalgae have tended to be used as a biological treatment for wastewater decontamination. The present study aimed to investigate the Cr(VI) removal using the freshwater microalgae ‘Craticula subminuscula’ and their biobased adsorbant, isolated from a Moroccan river in the High Atlas Mountain. The optimum operational conditions for maximum Cr(VI) biosorption by the biobased adsorbent form (95.32%) were determined at (pH = 1.09, adsorbent dose = 10.91 mg L⁻¹, and treatment duration = 129.47 min) using response surface methodology (RSM). Under those optimal conditions, the biosorption process of Cr(VI) by C. subminuscula is endothermic, spontaneous and follows Langmuir and a pseudo-second-order model with a constant rate; the theoretical and experimental biosorption capacity of 0.0004 g/mg/min was 289.01 mg g⁻¹ and 277.57 mg g⁻¹, respectively. Fourier Transform Infrared Spectroscopy (FTIR) analyses of the biomass and scanning electron microscopy (SEM) revealed that the principal mechanism to remove Cr(VI) by C. subminuscula was the affinity of Cr(VI) by the cell walls of microalgae. Thus, the positive results of desorption cycles promise increased potential utilization of these algae in continuous systems within industrial processes. The findings contribute valuable insights into the effectiveness of C. subminuscula as a biobased remediation agent for Cr(VI) in wastewater treatment. Full article

Titanite is a widespread accessory mineral in igneous, metamorphic, and hydrothermal rocks, but few comply with gem-grade requirements. Previous studies on Moroccan titanite focused on elementary composition and U-Pb dating. In this study, two gem-grade titanites (MA-1 and MA-2) from the Moroccan Central High Atlas were investigated through gemological and chemical studies, including infrared spectrum, Raman spectrum, SEM-EDS, and LA-ICP-MS. Two titanite samples are yellow, transparent–translucent with a greasy luster, 3.5 and 2.5 mm long. MA-1 and MA-2 have similar gemological properties, the refractive index (RI) is beyond the range of the refractometer (>1.78), the specific gravity (SG) values fall in the range of 3.52~3.54 and both are inert to short-wave and long-wave UV radiation. The spectral characteristics have high consistency with the RRUFF database. The major elements’ composition shows a negative correlation between Al, Fe, V, and Ti, suggesting the titanites underwent substitutions such as (Al, Fe³⁺) + (F, OH) ↔ Ti + O. The titanite samples, characterized by a low abundance of REE (802~4088 ppm) and enriched in LREE, exhibit positive Eu (δEu: 1.53~7.79) and Ce (δCe: 1.08~1.33) anomalies, indicating their formation in a hydrothermal environment with low oxygen fugacity. The ²³⁸U/²⁰⁶Pb and ²⁰⁷Pb/²⁰⁶Pb ratios of the titanites yield lower intercept ages of 152.6 ± 2.2 and 151.4 ± 5.3 Ma (1s), consistent with their weighted average ²⁰⁶Pb/²³⁸U ages of 152.3 ± 2.0 and 150.7 ± 3.2 Ma (1s) respectively. The results of U-Pb dating are matched with the second main magmatic activities in the High Atlas intracontinental belt of Morocco during the Mesozoic to Cenozoic period. Moreover, the two titanite samples have almost no radiational damage. All the results show that the titanite from High Atlas, Morocco, has the potential to be a reference material for LA-ICP-MS U–Pb dating, but further experiments are needed to be sure. Full article

Local agrobiodiversity in remote areas such as the Moroccan High Atlas is poorly studied, despite being of great importance for the sustainability and resilience of mountainous populations. This includes important species such as wheat (Triticum spp.), barley (Hordeum vulgare), fava beans (Vicia faba), peas (Pisum sativum), and alfalfa (Medicago sativa). This study aimed to better understand varietal naming by farmers and the traits they use for assessing the current diversity of the five species, in 22 locations, distributed across three hubs of the High Atlas. The data were provided by 282 Amazigh informants during focus-group discussions, household surveys, and market surveys, with the support of the Diversity Assessment Tool for Agrobiodiversity and Resilience (DATAR). The use of local terminology for variety names and systematically collected morphological, ecological, and use descriptors appears to be a valuable way to assess local intraspecific diversity, and further comparisons with genomic results are recommended. Furthermore, the results also indicate low diversity at the household level, which contrasts with the greater diversity at the community level. Larger areas are still planted with landraces compared to areas planted with modern varieties, although the levels of richness (number) of both landraces and modern varieties are equivalent overall. Many factors influence this diversity: the biophysical characteristics of the sites, the socio-economic and management practices of farmers, and the availability of varietal diversity and of modern varieties or landraces. Although selection processes have reduced the local diversity available for economically important crops, we found that farmers still rely greatly on landraces, which present traits and variability that allow them to adapt to local conditions. Full article

Accurate lithological mapping is a crucial juncture for geological studies and mineral exploration. Hyperspectral data provide the opportunity to extract detailed information about the geology and mineralogy of the Earth’s surface. Machine learning (ML) and deep learning (DL) techniques provide an accurate and effective mapping of various types of lithologies in arid and semi-arid regions. This article discusses the use of machine learning algorithms, specifically Support Vector Machines (SVM), one-dimensional Convolutional Neural Network (1D-CNN), random forest (RF), and k-nearest neighbor (KNN), for lithological mapping in a complex area with strong hydrothermal alteration. The study evaluates the performance of the four algorithms in three different zones in the Ameln valley shear zone (AVSZ) area at eastern Kerdous inlier, Moroccan western Anti-Atlas. The results demonstrated that 1D-CNN achieved the best classification results for most lithological units. Additionally, the LK-SVM demonstrated good mapping results compared to the other SVM models, as well as RF and KNN. Our study concludes that the combination of the CNN and HyMap data can provide the most accurate lithologic mapping for the three selected region, with an overall accuracy of ~95%. However, this study highlights the challenges in identifying different lithological units using remotely sensed data due to spectrum similarities induced by similar chemical and mineralogical compositions. This study emphasizes the importance of carefully considering and evaluating ML and DL methods for lithological mapping studies, then recommends the high-resolution hyperspectral data and DL models for accurate results. The implications of this study would be fascinating to exploration geologists for Mineral Prospectivity Mapping (MPM), especially in selecting the most appropriate techniques for highly accurate mineral mapping in metallogenic provinces. Full article

The northwest-southeast convergence of the Eurasian and Nubian (African) plates in the western Mediterranean region propagates inside the Nubian plate and affects the Moroccan Meseta and the neighboring Atlasic belt. Five continuous Global Positioning System (cGPS) stations were installed in this area in 2009 and provide significant new data, despite a certain degree of errors (between 0.5 and 1.2 mm year⁻¹, 95% confidence) due to slow rates. The cGPS network reveals 1 mm year⁻¹ North/South shortening accommodated within the High Atlas Mountains, and unexpected 2 mm year⁻¹ north-northwest/south-southeast extensional-to-transtensional tectonics within the Meseta and the Middle Atlas, which have been quantified for the first time. Moreover, the Alpine Rif Cordillera drifts towards the south-southeast against its Prerifian foreland basins and the Meseta. In this context, the geological extension foreseen in the Moroccan Meseta and Middle Atlas agrees with a crustal thinning due to the combined effect of the anomalous mantle beneath both the Meseta and Middle-High Atlasic system, from which Quaternary basalts were sourced, and the roll-back tectonics in the Rif Cordillera. Overall, the new cGPS data provide reliable support for understanding the geodynamic mechanism that built the prominent Atlasic Cordillera, and reveal the heterogeneous present-day behavior of the Eurasia-Nubia collisional boundary. Full article

Soil organic carbon (SOC) is an essential component, which soil quality depends on. Thus, understanding the spatial distribution and controlling factors of SOC is paramount to achieving sustainable soil management. In this study, SOC prediction for the Ourika watershed in Morocco was done using four machine learning (ML) algorithms: Cubist, random forest (RF), support vector machine (SVM), and gradient boosting machine (GBM). A total of 420 soil samples were collected at three different depths (0–10 cm, 10–20 cm, and 20–30 cm) from which SOC concentration and bulk density (BD) were measured, and consequently SOC stock (SOCS) was determined. Modeling data included 88 variables incorporating environmental covariates, including soil properties, climate, topography, and remote sensing variables used as predictors. The results showed that RF (R² = 0.79, RMSE = 1.2%) and Cubist (R² = 0.77, RMSE = 1.2%) were the most accurate models for predicting SOC, while none of the models were satisfactory in predicting BD across the watershed. As with SOC, Cubist (R² = 0.86, RMSE = 11.62 t/ha) and RF (R² = 0.79, RMSE = 13.26 t/ha) exhibited the highest predictive power for SOCS. Land use/land cover (LU/LC) was the most critical factor in predicting SOC and SOCS, followed by soil properties and bioclimatic variables. Both combinations of bioclimatic–topographic variables and soil properties–remote sensing variables were shown to improve prediction performance. Our findings show that ML algorithms can be a viable tool for spatial modeling of SOC in mountainous Mediterranean regions, such as the study area. Full article

The distribution and abundance of the various truffle species are influenced by the climate, soil, and vegetation conditions. The setting of these characteristics is necessary for the potential species’ cultivation. Here, we describe the ecological characteristics of Moroccan truffles and desert truffles and their associations with host plants. We also determine the climate and soil parameters relating to the geographic distribution and fructification of truffles and desert truffles. In contrast to truffles, which are found in sub-humid environments, desert truffles are found in semi-arid and arid regions of Morocco. The dissemination of desert truffles in the Mamora forest and oriental regions of Morocco is typically linked to the presence of Helianthemum sp., whereas the existence of truffles (Tuber spp.) in the Middle Atlas depends on the subsistence of Quercus ilex and Q. faginea. The truffles’ and desert truffles’ fructification depends mainly on the precipitation frequency. Terfezia arenaria and Tuber oligospermum, the two major desert truffles of Mamora forest, require an annual rainfall of 435 mm on average in slightly acidic soil. While the oriental and Highland desert truffles, namely Terfezia boudieri, T. claveryi, and Tirmania spp., require an annual precipitation average of 123 to 267 mm and a high CaCO₃ content. Otherwise, there is Tuber aestivum, localized in humid regions with a rainfall rate of more than 650 mm, and found under calcareous soil rich in organic matter with the presence of potential host plants, such as oaks, cedars, and pines. Our findings open up the possibility of successful cultivation of truffles and desert truffles having an economic interest through understanding their ecological requirements in Morocco. Full article

Development and concentration of many ore deposits at the regional and district scales closely depend on structural geology, especially in polydeformed basements. The superposition of many deformation periods highlights the complexity of the structural context and expected potential location of mineralization zones. The formation and concentration of hydrothermal ore deposits is highly dependent on structural controls. On the NE flank of the Saghro massif (Eastern Anti-Atlas, Morocco), the Imiter silver mining region has been affected by multiple tectonic events since the Precambrian and throughout the Phanerozoic. In this investigation, a structural analysis of the different geological units revealed multi-stage deformation, beginning with the late Pan-African-Cadomian event, and ending with the last Cenozoic exhumation of the area. At least eight tectonic regimes have been identified. The Imiter basement, formed by the Cryogenian-early Ediacaran “flysch-like” Saghro Group, has been folded in low-grade metamorphic conditions, followed by an ENE-WSW brittle compressive event. These deformations occurred before to the early Ediacaran during the compressional and/or transpressional late Pan-African-Cadomian events (600–580 Ma). The unconformably overlaying deposition of the late Ediacaran Ouarzazate Group takes place in a WNW-ESE extensional setting and then involved in a NNW-SSE compressional event that occurred concurrently with a regional exhumation and erosion stages. A similar extensional event appears to have controlled the middle Cambrian sedimentation, the oldest Paleozoic deposits in this area. During the late Carboniferous, Variscan shortening was recorded by NW-SE transpressional deformation responsible for combined dextral strike-slip and southward thrusts. The Imiter silver mining region is part of the Moroccan Sub-Meseta Zone along with Paleozoic inliers of the Skoura and Tamlelt on the southern side of the High Atlas. The Mesozoic evolution began with the Late Triassic NNW-SSW transtensional tectonic regime with a northeast trending CAMP (Central Atlantic Magmatic Province) dyke during the Pangea breakup. Ultimately, the Imiter silver mining region experienced NNW-SSE Atlasic shortening during the uplift of the adjacent High Atlas. Over time, the direction of implemented tectonic stress and its effect on various geological units can elucidate the relationship between tectonism and hydrothermal silver mineralization in the Imiter region. In conclusion, structural analysis and investigation of paleostress development can be one of the most important factors for successful exploration plan and resource recovery in the Imiter region. An analysis of geological structures in determining feasible mineralization zones is crucial for future safe mining operation in the study area and can be extrapolated to other ore mining regions. Full article

Mapping seasonal snow cover dynamics provides essential information to predict snowmelt during spring and early summer. Such information is vital for water supply management and regulation by national stakeholders. Recent advances in remote sensing have made it possible to reliably estimate and quantify the spatial and temporal variability of snow cover at different scales. However, because of technological constraints, there is a compromise between the temporal, spectral, and spatial resolutions of available satellites. In addition, atmospheric conditions and cloud contamination may increase the number of missing satellite observations. Therefore, data from a single satellite is insufficient to accurately capture snow dynamics, especially in semi-arid areas where snowfall is extremely variable in both time and space. Considering these limitations, the combined use of the next generation of multispectral sensor data from the Landsat-8 (L8) and Sentinel-2 (S2), with a spatial resolution ranging from 10 to 30 m, provides unprecedented opportunities to enhance snow cover mapping. Hence, the purpose of this study is to examine the effectiveness of the combined use of optical sensors through image fusion techniques for capturing snow dynamics and producing detailed and dense normalized difference snow index (NDSI) time series within a semi-arid context. Three different models include the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatio-temporal data fusion model (FSDAF), and the pre-classification flexible spatio-temporal data fusion model (pre-classification FSDAF) were tested and compared to merge L8 and S2 data. The results showed that the pre-classification FSDAF model generates the most accurate precise fused NDSI images and retains spatial detail compared to the other models, with the root mean square error (RMSE = 0.12) and the correlation coefficient (R = 0.96). Our results reveal that, the pre-classification FSDAF model provides a high-resolution merged snow time series and can compensate the lack of ground-based snow cover data. Full article

This work aimed to characterize and compare the physicochemical, ascorbic acid, phenolic, and flavonoid compounds, as well as the antioxidant properties, pollen spectra, and sugar profiles of twenty-three organic honeys produced in the Middle Atlas of Morocco. As results, the pollen analysis showed 22 taxa and revealed the dominance of Ziziphus lotus pollens for all monofloral honeys. The moisture content ranged from 15.9 to 19.0%, pH values werebetween 3.9 and 4.8, electrical conductivity varied from 100 to 581 µs/cm, ash content varied from 0.1 to 2.4%, and the invertase activity ranged from 3.5 to 36 U/kg. Moreover, hydroxymethylfurfural(HMF) varied from 1.2 to 13.5 mg/kg, which confirmed the freshness of our honey samples. For the sugar profiles, there were no significant differences between the examined groups of honeys (p > 0.05) for both fructose and glucose. Additionally, our study showed good antioxidant properties (total antioxidant activity ranged from 34.18 to 131.20 mg AAE/g; DPPH IC₅₀ values ranged from 8.14 to 45.20 mg/mL; ABTS IC₅₀ values ranged from 8.19 to 32.76 mg/mL) and high amounts of phenolic compounds ranging between 20.92 ± 0.03 and 155.89 ± 0.03 mg GAE/100 g, respectively; flavonoid compounds ranged from 5.52 to 20.69 mg QE/100 g, and ascorbic acid ranged from 8.01 to 23.26 mg/100 g. Overall, the proximate composition and the general characterization of organic monofloral and polyfloral honeys as sustainable and health-promising functional products may increase their commercial values, promote their marketability, and might have a significant impact on the basic circular/sustainable economy as a solid lever for solidarity economic development, especially in the rural/poor Moroccan communities. The investigated features may allow and support the incorporation of Moroccan organic honeys and their biovaluable ingredients in the nutraceutical and food industries for multiple purposes. Full article