The lithofacies and depositional paleoenvironment of the Bima Sandstone in Girei and Environs, Yola Arm, Upper Benue Trough, Northeastern Nigeria

Finthan, Boniface; Mamman, Y. D.

doi:10.1016/j.jafrearsci.2020.103863

Cited by 10 publications

(13 citation statements)

References 15 publications

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“…The depositional conditions of these sandstone facies may be linked to migrating current ripples which are controlled by a lower ow regime (Miall, 1978, 1996), however, Rust (1977 dened this depositional facies as a product of wanning ow sheet ood. These facies were reported from the Girei area by Finthan and Mamman, (2020).…”

Section: Ripple Cross-laminated Sandstone (Sr)mentioning

confidence: 64%

“…The sedimentology, stratigraphy, and geochemistry of this formation have been documented in several papers e.g. (Carter, 1963;Guiraud and Maurin, 1992;Zaborski et al, 1997;Samaila et al, 2006;Samaila, 2007;Sarki Yandoka et al, 2014, 2015a, 2015bTukur et al, 2015;Shettima et al, 2018;Aliyuda et al, 2019;Shettima et al, 2020aShettima et al, , 2020bFinthan and Mamman, 2020;Valdon et al, 2021;Finthan et al, 2022), these previous papers have improved the understanding of the Bima Formation. The Bima Formation is the target reservoir rock as well as a member source rock linked to its uvio-lacustrine facies (Sarki Yandoka et al, 2015a;Shettima et al, 2018).…”

Section: Introductionmentioning

confidence: 87%

“…According to (Miall, 2010(Miall, , 1978(Miall, , 1977Rust, 1977) these lithofacies form due to migrating sinuous 3-3 dunes that stack up to create bar forms in the channel. The details of these facies descriptions can also be obtained in (Sarki Yandoka et al, 2014;Shettima et al, 2018;Finthan and Mamman, 2020) Planar cross-bedded sandstone (Sp) The planar cross-bedded sandstone facies (Sp) is made up of sub-rounded, medium to coarsegrained poorly sorted sandstone. The Sp facies is commonly overlying the trough crossbedded sandstone facies (St), and the individual forset's thickness varies between 1-3 cm and 35 cm to 1.2 m as coset (Fig.…”

Section: Trough Cross-bedded Sandstone (St)mentioning

confidence: 99%

“…According to (Miall, 1977(Miall, , 1978(Miall, , 1996(Miall, , 2010Rust, 1977) such lithofacies are transverse bars formed due to a low ow regime. (Sarki Yandoka et al, 2014;Shettima et al, 2018;Finthan and Mamman, 2020) for the descriptions related to these facies.…”

Section: Trough Cross-bedded Sandstone (St)mentioning

confidence: 99%

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The primary depositional structures of the Upper Bima Member from Fufore and environs, Yola Arm of the Upper Benue Trough, Northeastern Nigeria: implications for paleoenvironment

Seli¹,

Finthan²

2022

dujopas

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Primary depositional structures are important parameters that can provide the means of unraveling the processes and process responses in sedimentary depositional settings. This paper is aimed at determining the paleoenvironment of the Upper Bima Member in Fufore and environs using the primary depositional structures. This research was carried out in two phases which involved fieldwork that dealt with logging the outcrops sections and desk work whereby the logged sections and other field data are digitized, remodeled and analyzed using relevant computer programs. The result of the study shows that the Upper Bima Member is composed of five lithofacies succession; trough cross-bedded sandstone facies (St), planar cross-bedded sandstone facies (Sp), horizontal bedded sandstone facies (Sh), ripple cross-laminated sandstone facies (Sr), and mudstone facies (Fm). These lithofacies gave rise to two facies associations; sand dominated and fine-grained facies dominated facies association. The cross-stratified facies show an overall paleocurrent trend in the NW direction. These facies succession represent both shallow perennial and ephemeral sand-bed in a braided river depositional environment.

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Section: Ripple Cross-laminated Sandstone (Sr)mentioning

confidence: 64%

Section: Introductionmentioning

confidence: 87%

Section: Trough Cross-bedded Sandstone (St)mentioning

confidence: 99%

Section: Trough Cross-bedded Sandstone (St)mentioning

confidence: 99%

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The primary depositional structures of the Upper Bima Member from Fufore and environs, Yola Arm of the Upper Benue Trough, Northeastern Nigeria: implications for paleoenvironment

Seli¹,

Finthan²

2022

dujopas

View full text Add to dashboard Cite

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“…Different types of mud shale rocks can be effectively distinguished through classification based on the lithofacies of fine-grained sedimentary rocks. Mineral composi-tion, sedimentary structure, and organic matter abundance are the frequently employed classification indicators for the lithofacies of fine-grained sedimentary rocks [42][43][44][45][46]. For the crucial step of rock naming, this paper adheres to existing research findings and adopts a three-end-member petrological classification [47][48][49][50]; clay minerals, felsic minerals (landderived debris), and carbonate minerals (primarily dolomite) are considered as the three end members.…”

Section: Division Of Mud Shale Lithofacies Typesmentioning

confidence: 99%

Application and Comparison of Machine Learning Methods for Mud Shale Petrographic Identification

et al. 2023

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Machine learning is the main technical means for lithofacies logging identification. As the main target of shale oil spatial distribution prediction, mud shale petrography is subjected to the constraints of stratigraphic inhomogeneity and logging information redundancy. Therefore, choosing the most applicable machine learning method for different geological characteristics and data situations is one of the key aspects of high-precision lithofacies identification. However, only a few studies have been conducted on the applicability of machine learning methods for mud shale petrography. This paper aims to identify lithofacies using commonly used machine learning methods. The study employs five supervised learning algorithms, namely Random Forest Algorithm (RF), BP Neural Network Algorithm (BPANN), Gradient Boosting Decision Tree Method (GBDT), Nearest Neighbor Method (KNN), and Vector Machine Method (SVM), as well as four unsupervised learning algorithms, namely K-means, DBSCAN, SOM, and MRGC. The results are evaluated using the confusion matrix, which provides the accuracy of each algorithm. The GBDT algorithm has better accuracy in supervised learning, while the K-means and DBSCAN algorithms have higher accuracy in unsupervised learning. Based on the comparison of different algorithms, it can be concluded that shale lithofacies identification poses challenges due to limited sample data and high overlapping degree of type distribution areas. Therefore, selecting the appropriate algorithm is crucial. Although supervised machine learning algorithms are generally accurate, they are limited by the data volume of lithofacies samples. Future research should focus on how to make the most of limited samples for supervised learning and combine unsupervised learning algorithms to explore lithofacies types of non-coring wells.

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Hydrochemistry of nutrients in groundwater under farmland in the Benue River Basin, North-Cameroon

et al. 2022

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In this study, we investigated the distribution and the principal sources of macronutrients (N, P, K, S) in groundwater in agro-pastorally dominated North Cameroon. Graphical methods and multivariate statistics were used to understand the impact of land use and regional geology on nutrient concentrations (K+, NO3−, SO42−, PO43−). Groundwater was acidic (median pH 6.8) and more mineralized (EC range = 35–2500 µS/cm, median = 376 µS/cm) than surface water (EC range = 50–222 µS/cm, median = 81 µS/cm). The median concentration of nutrients was (in mg/L) in the order NO3− (41.3) > SO42− (7.7) > K+ (5.2) > PO43− (0.1) in groundwater and rather K+ (3.3) > NO3− (0.3) > SO42− (0.2) > PO43− (0.2) in surface water. Using hierarchical cluster analysis and Schoeller diagram, we identified three distinct water quality groups. Group I, which contained cluster 1 and 2 were generally less mineralized and characterised as Na + K − HCO3 water type found in the urban area such as Garoua and Lagdo. Group II, containing cluster 3 was much higher mineralized and characterised as the Na + K − HCO3 water type with elevated nitrate. The occurrence of these groups is linked to the outcrops of cretaceous terrestrial sediments of the Garoua basin. Group III, which contained cluster 4 were heavily mineralized and characterised as the Ca + Mg − NO3 water type. The occurrence was controlled by the outcrop of the crystalline Proterozoic basement. The nitrate median (41 mg/L) found in groundwater demonstrates the influence exerted by anthropogenic activities. Management strategies are required for the whole study area to limit further deterioration of groundwater resources in the wake of the planned expansion of agricultural productivity in the North Region of Cameroon.

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The lithofacies and depositional paleoenvironment of the Bima Sandstone in Girei and Environs, Yola Arm, Upper Benue Trough, Northeastern Nigeria

Cited by 10 publications

References 15 publications

The primary depositional structures of the Upper Bima Member from Fufore and environs, Yola Arm of the Upper Benue Trough, Northeastern Nigeria: implications for paleoenvironment

The primary depositional structures of the Upper Bima Member from Fufore and environs, Yola Arm of the Upper Benue Trough, Northeastern Nigeria: implications for paleoenvironment

Application and Comparison of Machine Learning Methods for Mud Shale Petrographic Identification

Hydrochemistry of nutrients in groundwater under farmland in the Benue River Basin, North-Cameroon

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