Experimental investigation of factors affecting compressional and shear wave velocities in shale and limestone of Ewekoro formation of Southern Nigeria sedimentary basin

Abatan, Adio Olufemi; Akinyemi, Olukayode D.; Olowofela, J. A.; Ajiboye, G. A.; Salako, F. K.

doi:10.1007/s12665-016-6229-6

Cited by 10 publications

(2 citation statements)

References 16 publications

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“…Some of the works include: factors causing differentials in the shear velocities, Lithotype representation by Nuclear Magnetic Resonance (NMR) and blastability properties, investigation of allochemical and orthochemical components of the limestone formation, ground vibration and noise generated during blasting, gas generating potential and prospects, estimation of thermal conductivity, assessment of reservoir potential, isotopic elements composition and diagenesis. Others include: Organic geochemical analysis and appraisal, elemental analysis, distribution of tree oxides and groundwater composition [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] .…”

Section: Experimental Design Methods and Materialsmentioning

confidence: 99%

Geostatistical exploration of dataset assessing the heavy metal contamination in Ewekoro limestone, Southwestern Nigeria

2017

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The dataset for this article contains geostatistical analysis of heavy metals contamination from limestone samples collected from Ewekoro Formation in the eastern Dahomey basin, Ogun State Nigeria. The samples were manually collected and analysed using Microwave Plasma Atomic Absorption Spectrometer (MPAS). Analysis of the twenty different samples showed different levels of heavy metals concentration. The analysed nine elements are Arsenic, Mercury, Cadmium, Cobalt, Chromium, Nickel, Lead, Vanadium and Zinc. Descriptive statistics was used to explore the heavy metal concentrations individually. Pearson, Kendall tau and Spearman rho correlation coefficients was used to establish the relationships among the elements and the analysis of variance showed that there is a significant difference in the mean distribution of the heavy metals concentration within and between the groups of the 20 samples analysed. The dataset can provide insights into the health implications of the contaminants especially when the mean concentration levels of the heavy metals are compared with recommended regulatory limit concentration.

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Section: Experimental Design Methods and Materialsmentioning

confidence: 99%

Geostatistical exploration of dataset assessing the heavy metal contamination in Ewekoro limestone, Southwestern Nigeria

2017

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“…To date, point load test have yielded the best estimation and prediction of UCS index compared to other tests (Sonmez et al [35]; Diamantis et al [12]; Yilmaz and Yuksek [44]; Basu and Kamran [10]; Heidari et al [17]; Kohno and Maeda [23]; and Wong Li and Diyuan [42]; Khanlari and Abdi-lor [26]). Furthermore, block punch and cylinder punch tests have been used for predicting uniaxial compressive strength of different types of rocks (Van der Schrier [41]; Ulusay and Gokceoglu [39]; Gokceoglu and Aksoy [14]; Ulusay et al [40]; Sonmez et al [34][35]; Sonmez and Tunusluoglu [36]; Aksoy [3]; Aksoy et al [4][5]; Karakul et al [22]; Jafari et al [19]; Mishra and Basu [32]; Khanlari et al [24][25][26]; Abatan et al [1]; Khanlari and Naseri [27]; Heidari et al [18]). More recently, a wide variety of statistical methods have been utilized for developing a proper correlation between UCS index and other engineering properties of rocks, among which different statistics analysis models, multiple regression analysis, ANN model, fuzzy models, and ANFIS models have received a greater attention (Alvarez and Babuska [6]; Sonmez et al [34]; Yilmaz and Yuksek [44]; Kahraman et al [21]; Heidari et al [16][17]; Manouchehrian et al [29]; Mishra and Basu [32]; Torabi-Kaveh et al [38]; Armaghani et al [2]; Jalali [20]).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Uniaxial Compressive Strength and Modulus of Elasticity in Calcareous Mudstones Using Neural Networks, Fuzzy Systems, and Regression Analysis

Mahdiabadi¹,

Khanlari²

2018

Period. Polytech. Civil Eng.

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The uniaxial compressive strength (UCS) and modulus of elasticity (E) are two important rock geomechanical parameters that are widely used in rock engineering projects such as tunnels, dams, and rock slope stability. Since the acquisition of high-quality core samples is not always possible, researchers often indirectly estimate these parameters. In the present study, prediction of UCS and E was investigated in calcareous mudstones of Aghajari Formation using multiple linear regression (MLR), multiple nonlinear regression (MNLR), artificial neural networks (ANN), and adaptive neuro-fuzzy ınference system (ANFIS). For this purpose, 80 samples from calcareous mudstones were subjected to the point loading, block punch, and cylinder punch tests. The performance of developed models was assessed based on determination coefficients (R2), mean absolute percentage error (MAPE), and variance accounted for (VAF) indices. The comparison of the obtained results revealed that, among the studied methods, ANFIS is the most suitable one for predicting UCS and E. Moreover, the results showed that ANN and MLNR respectively predict UCS and E better than MLR and a meaningful relationship between the observed and estimated UCS values in all regressions.

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Multivariate interrelatedness of geotechnical and petrophysical properties towards developing near-surface lithology clusters in a sedimentary terrain

Akinyemi

2022

Bull Eng Geol Environ

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The study reports the approach of interrelating geotechnical and petrophysical parameters to develop near-surface lithology clusters in a sedimentary terrain. Field-based and laboratory-based geophysical, geotechnical, and petrophysical measurements were carried out with a view to finding correlations among related parameters, grouping the parameters into depth clusters, and determining trend correlations between field-acquired and laboratory-obtained parameters. Four agglomerate hierarchical cluster signatures were identified at depth 0–30 cm, three at 30–60 cm, and five at 0–90 cm. Principal component 1 retained 74% of data variation and differentiated the petrophysical parameters according to Poisson’s ratio (v) and velocity ratio (Vp/Vs). Principal component 2 explained another 14% of variability in the original responses and separated parameters based on elastic modulus (E) and clay fraction (C). Degree of correlation of Vp/Vs with porosity (Φ), sand fraction (S), C, unconfined compressive strength (U), and California bearing ratio (R) followed order Φ > S > C, and U > R. Trend correlation of shear velocity at 1 m showed good agreement (R2 = 0.992) between field and laboratory data with an amplification factor of 4.13 separating the corresponding values. Lithology determined from soil analysis agreed with that inferred from measured electrical resistivity while geotechnical, petrophysical, and geophysical spatial analysis supported site lithology configuration patterns. Thus, multivariate association of geotechnical and petrophysical parameters exhibited potentials for rapid and exploratory near-surface lithology mapping even at a regional scale.

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Experimental investigation of factors affecting compressional and shear wave velocities in shale and limestone of Ewekoro formation of Southern Nigeria sedimentary basin

Cited by 10 publications

References 16 publications

Geostatistical exploration of dataset assessing the heavy metal contamination in Ewekoro limestone, Southwestern Nigeria

Geostatistical exploration of dataset assessing the heavy metal contamination in Ewekoro limestone, Southwestern Nigeria

Prediction of Uniaxial Compressive Strength and Modulus of Elasticity in Calcareous Mudstones Using Neural Networks, Fuzzy Systems, and Regression Analysis

Multivariate interrelatedness of geotechnical and petrophysical properties towards developing near-surface lithology clusters in a sedimentary terrain

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