Spatial pattern in solid minerals distribution in Cross River State, Nigeria

Njar, G. N.

doi:10.4314/jasem.v22i10.23

Cited by 4 publications

(2 citation statements)

References 5 publications

(5 reference statements)

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“…This further suggests that the manganese minerals within the study area occur as structurally controlled emplacements within the iron oxides and areas with intense lateritic alterations [53]. The map (Figure 17) corroborates the reports of [2,11,[53][54][55] on the occurrence of manganese, iron ores, and silicate minerals within the Akampa local government area of Cross-River state, Nigeria.…”

Section: Discussionsupporting

confidence: 84%

Field Observations and Geophysical Research Applied to the Detection of Manganese (Mn) Deposits in the Eastern Part of Oban Massif, South-Eastern Nigeria: An Integrated Approach

et al. 2022

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The growing need for an industrialized world, especially in Africa, cannot be feasible without adequate mineral resources. Thus, the search for more mineral deposits will continue to be necessary. An integrated approach involving geological mapping and a high-resolution geophysical (aeromagnetic) investigation was conducted to assess the manganese mineralization in parts of the Oban Massif, southeast Nigeria. The aeromagnetic data were processed using regional-residual anomaly separation techniques, first vertical derivative (1VD), analytical signals, source parameters imaging (SPI), and Euler deconvolution to better understand magnetic source distributions and their depths of occurrence. The geological investigation revealed a dominant variety of metamorphic rock types, including migmatitic (banded) gneisses hornblende granite gneisses, amphibolites, charnockites, and some quartzite ridges. Also present are some indications of pockets of dolerites. The study area also observed epithermal Mn+Fe+Qtz vein type mineralization associated with hydrothermal alteration zones whose orientation coincides with dominant structural orientation from aeromagnetic interpretation. Analysis of aeromagnetic data shows that the study area is dominated by ENE, NNE, and E-W structural directions (near-surface basement structures), with the ENE trends related to mineralization in the area. The manganese mineralization within Oban Massif is structurally controlled. The depths of the magnetic anomalies in the study area were estimated using SPI and Euler decomposition algorithms. SPI delineated the shallow, intermediate, and deep magnetic anomalies at 84–142 m, 152–200 m, and 215–656 m, respectively. Euler decomposition, however, revealed that shallow, intermediate, and deep depths occurrence of the magnetic anomalies are at 200–377 m, 393–472 m, and 499–793 m, respectively.

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Section: Discussionsupporting

confidence: 84%

Field Observations and Geophysical Research Applied to the Detection of Manganese (Mn) Deposits in the Eastern Part of Oban Massif, South-Eastern Nigeria: An Integrated Approach

et al. 2022

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“…The main geology/ parent materials underlying soils in the study area are basement complex rocks known as Oban Massif, limestone, basalt, shale, and sandstone (Ofomata 1975;Eshett et al 1990;Aki et al 2014). The predominant mineralogy consists of kaolinite, quartz, Fe and Al oxides, montmorillonite, microcline, and hematite (Njar 2018). In general, the study area is characterized by a tropical wet or monsoon climate according to the Koppen-Geiger climate classification (Beck et al 2018).…”

Section: Research Locationmentioning

confidence: 99%

Soil quality estimation using environmental covariates and predictive models: an example from tropical soils of Nigeria

et al. 2022

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Background Information addressing soil quality in developing countries often depends on results from small experimental plots, which are later extrapolated to vast areas of agricultural land. This approach often results in misinformation to end-users of land for sustainable soil nutrient management. The objective of this study was to estimate the spatial variability of soil quality index (SQI) at regional scale with predictive models using soil–environmental covariates. Methods A total of 110 composite soil samples (0–30 cm depth) were collected by stratified random sampling schemes at 2–5 km intervals across the Cross River State, Nigeria, and selected soil physical and chemical properties were determined. We employed environmental covariates derived from a digital elevation model (DEM) and Sentinel-2 imageries for our modelling regime. We measured soil quality using two approaches [total data set (TDS) and minimum data set (MDS)]. Two scoring functions were also applied, linear (L) and non-linear (NL), yielding four indices (MDS_L, MDS_NL, TDS_L, and TDS_NL). Eleven soil quality indicators were used as TDS and were further screened for MDS using principal component analysis (PCA). Random forest (RF), support vector regression (SVR), regression kriging (RK), Cubist regression, and geographically weighted regression (GWR) were applied to predict SQI in unsampled locations. Results The computed SQI via MDS_L was classified into five classes: $$\le$$ ≤ 0.38, 0.38–0.48, 0.48–0.58, 0.58–0.68, and $$\ge$$ ≥ 0.68, representing very low (class V), low (class IV), moderate (class III), high (class II) and very high (class I) soil quality, respectively. GWR model was robust in predicting soil quality (R2 = 0.21, CCC = 0.39, RMSE = 0.15), while RF was a model with inferior performance (R2 = 0.02, CCC = 0.32, RMSE = 0.15). Soil quality was high in the southern region and low in the northern region. High soil quality class (> 49%) and moderate soil quality class (> 14%) dominate the study area in all predicted models used. Conclusions Structural stability index, sand content, soil oganic carbon content, and mean weight diameter of aggregates were the parameters used in establishing regional soil quality indices, while land surface water index, Sentinel-2 near-infrared band, plane curvature, and clay index were the most important variables affecting soil quality variability. The MDS_L and GWR are effective and useful models to identify the key soil properties for assessing soil quality, which can provide guidance for site-specific management of soils developed on diverse parent materials.

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Interpretation of Airborne Magnetic and Geo-electric Data: Resource Potential and Basement Morphology of the Ikom–Mamfe Embayment and Environs, Southeastern Nigeria

2020

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Spatial pattern in solid minerals distribution in Cross River State, Nigeria

Cited by 4 publications

References 5 publications

Field Observations and Geophysical Research Applied to the Detection of Manganese (Mn) Deposits in the Eastern Part of Oban Massif, South-Eastern Nigeria: An Integrated Approach

Field Observations and Geophysical Research Applied to the Detection of Manganese (Mn) Deposits in the Eastern Part of Oban Massif, South-Eastern Nigeria: An Integrated Approach

Soil quality estimation using environmental covariates and predictive models: an example from tropical soils of Nigeria

Interpretation of Airborne Magnetic and Geo-electric Data: Resource Potential and Basement Morphology of the Ikom–Mamfe Embayment and Environs, Southeastern Nigeria

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