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# Assessment and Analysis of Precambrian Basement Soil Deposits Using Grain Size Distribution

###### Recibido: 20 Nov 2019
Detalles de la revista
Formato
Revista
eISSN
1854-7400
Primera edición
30 Mar 2016
Calendario de la edición
4 veces al año
Idiomas
Inglés
Introduction

The grain size analysis is one of the tests that can be performed to determine the percentage of different grain size contained within the soil. It provides very useful information on the classification of sedimentary environment and the transportation of the sediments. The grain size distribution provides good quantification for soil studies and reveals the weathering characteristics of sedimentary processes and provenance [1,2,3,4]. The results of Abuodha [5] have helped to clarify the sedimentary environment and its transport dynamism.

The benefits of mathematical representation of grain size analysis cannot be overemphasised which include the soil classification using the best-fit parameters. Second, the mathematical equation can be used as the basis for analysis related to estimating the soil–water characteristic curve. Third, a mathematical equation provides a method of representing the entire curve between the measured data points [6]. Representing the soil as a mathematical function also provides increased the flexibility in searching for similar soils in the database.

The development of computerized data analysis has enhanced the knowledge of the calculation of different statistical parameters to determine the transportation history of the sediments as in the kurtosis, the average size of grain, sorting and skewness. They are designated by different methods and characterised the particle-size distribution in sediments [7,8,9]. Various researchers [10,11,12,13,14,15,16,17,18] have established different formulae for the statistical parameters but the most widely used among the formulae are those proposed by Folk and Ward [19].

The measures of quartile, phi scale among others are some of the frequently used statistical measures of grain size distribution. Seven different points on the cumulative frequency curve are directly selected (at 5, 16, 25, 50, 75, 84 and 95 percentiles) for the computation of the parametric statistics [20]. Sediment transportation is the movement of organic or inorganic particles (sediments) by water, the sediments can also be carried by gravity, glaciers and fluid in which the sediment is entrained. Most mineral sediments are as a result of weathering and erosion [21]. Transportation of sediments was often responsible for the intermixing of geologic features by carrying mineral particles away from their origin [22].

According to Adegoke and Layade [23], a geophysical investigation had been carried out within Gbede, the study area which revealed the proximity of the iron ore in form of magnetite and hematite to the ground surface. Vents, supposedly the ore source, were also identified in the area which shows the presence of the ore in the area as a result of sediment transportation that took place for years irrespective of the geological constituent of the area. This research is aimed at analyzing the grain size of the soil samples collected from the study area in other to classify the samples based on their textural properties and determine the transportation history of the samples.

Geology and Description of the Study Area

The study area is located in Gbede of Surulere L.G.A of Oyo State, Southwest Nigeria. It is accessible through Ogbomoso – Gambari – Ilorin road, and is about 30 km from Ilorin Airport. The area is bounded within latitudes 8°17′37.7″ and 8°17′49.8″ North and between longitudes 4°20′45.9″ and 4°20′58.8″ East. It has an undulating topography with an average elevation of 370 m above the mean sea level. Past studies [24,25] have identified the hydrogeology of Sub-Saharan African as represented in Nigeria into four provinces; the Precambrian basement rocks, volcanic rocks, unconsolidated sediments and consolidated sedimentary rocks. However, the province of the Precambrian basement is located on the study area, and it comprises crystalline and metamorphic rocks.

Materials and Method
Sample Collection

Grain size analysis can be determined using various analytical techniques among which were sieving methods adopted for this research. The low investment, ease of handling and high accuracy make the sieve analysis a commonly used procedure to determine the soil texture. Fourteen fresh samples were collected at different locations using Soil Auger. This Auger used at a different point was properly rinsed before and after each sample collection for good analysis. A small polythene bag was used to transport the samples to the laboratory to begin the sieving procedure and further analysis. For proper identification, each polythene bag was labelled GB1 to GB14 (GB means Gbede, while the figures represent the number of strata being sampled).

Sieve Analysis

A weighing balance was used to weigh 100 g of each sample already arranged according to their depth. Since collected samples were fresh at the point of collection, it was then oven-dried at 70oC so that it will be free from trace moisture and thereafter passed through the mechanical sieving process using the Ro-tap shaker. The result of this sieving was tabulated and analysed. From the histogram chart, the cumulative frequency weight percent plotted against grain size (Phi) were generated and statistical parameters such as graphic mean, standard deviation skewness were computed from the graph. Seven points were identified as percentiles (5, 16, 25, 50, 75, 84 and 95 percentiles) and the results presented in Tables 1–6, respectively. The trend of grain size distribution was then determined from the total average value of each computed parameter. Appendices 1 and 2 represent the histogram and cumulative arithmetic curve plotted together from each sample.

Classification of the graphic mean.

Graphic meanClassification
∅ – 1 to ∅ 0Very coarse sand
∅ 0 to ∅ 1Coarse sand
∅ 1 to ∅ 2Medium sand
∅ 2 to ∅ 3Fine sand
∅ 3 to ∅ 4Very fine sand

Graphic Standard deviation with classes of sorting.

Graphic Standard DeviationClasses of Sorting
∅ 0.35 to ∅ 0.50well sorted
∅ 0.50 to ∅ 0.71moderately well sorted
∅ 0.71 to ∅ 1.00moderately sorted
∅ 1.00 to ∅ 2.00poorly sorted

Classification scale describing the skewness.

Classification scaleSkewness
∅ 0.1 to ∅ 0.3Fine skewed
∅ −0.1 to ∅ 0.1Near symmetrical
∅ −0.3 to ∅ −0.1Coarse-skewed

Classification scale and description of Kurtosis.

Classification scaleKurtosis
<∅ 0.67Very Platykurtic
∅ 0.67 to ∅ 0.90Platykurtic
∅ 0.90 to ∅ 1.11Mesokurtic
∅ 1.11 to ∅ 1.50Leptokurtic
∅ 1.50 to ∅ 3.00Very leptokurtic

Comparative result of the Grain Size Analysis for soil samples in phi (Φ).

SampleMeanSTDSkewnessKurtosis
GB11.461.01−0.051.35
GB21.550.54−0.121.36
GB31.960.64−0.131.50
GB41.290.950.181.03
GB51.091.260.031.17
GB61.190.910.061.72
GB72.061.20−0.251.66
GB81.501.260.010.97
GB92.330.84−0.081.21
GB101.690.420.201.40
GB112.030.570.260.58
GB121.370.870.021.66
GB131.940.62−0.131.67
GB141.190.910.061.72
Average1.620.954.3E-31.36

Description of the Soil samples with Grain Size Analysis.

Sample pointsDescriptions
GB1Medium sand, poorly sorted, ear symmetrical and leptokurtic.
GB2Medium sand, moderately well sorted, coarse-skewed and leptokurtic.
GB3Medium sand, moderately well sorted, coarse-skewed and leptokurtic.
GB4Medium sand, moderately sorted, fine skewed and mesokurtic.
GB5Medium sand, poorly sorted, near symmetrical and leptokurtic.
GB6Medium sand, moderately sorted, near symmetrical and very leptokurtic.
GB7Fine sand, poorly sorted, coarse-skewed and very leptokurtic.
GB8Medium sand, poorly sorted, near symmetrical and mesokurtic.
GB9Fine sand, moderately sorted, near symmetrical and leptokurtic.
GB10Medium sand, moderately sorted, fine skewed and leptokurtic
GB11Medium sand, moderately sorted, near symmetrical and leptokurtic
GB12Medium sand, moderately sorted, near symmetrical and very leptokurtic.
GB13Medium sand, moderately well sorted, coarse skewed and very leptokurtic.
GB14Medium sand, moderately sorted, near symmetrical and very leptokurtic.
AverageMedium sand, moderately sorted, near symmetrical and leptokurtic
Results and Discussion
Graphic Mean

Graphic mean is one of the statistical parameters to understand the transport history of the sediments. It depends on the size of available sediments and the amount of energy impacted to the sediments. The result of the classification of samples with graphic mean is presented in Table 1 while Figure 1 shows the variogram of the mean for the soil samples. $Mz=∅16+50+843$Mz = \emptyset {{16 + 50 + 84} \over 3} On the basis of the classification of as given different researchers [26,27,28] and using Equation (1), the range from 1.09 to 2.33 was obtained and the average value for the distribution within the analysed samples was 1.61. The two sediments category identified from the study area were coarse-grained and fine-grained sediments. The range values of the coarse grained are from 0.6 to 1.0, which suggests the samples were transported farther than other groups, while the value of fine-grained samples is greater than 2 which is a result of low energy of transportation that is associated with coarse conglomeratic of soil [29].

Sorting (Standard Deviation)

Sorting indicates how effective the depositional medium in separating different classes of grains. The expression for graphic standard deviation is given in Equation (2) followed by its interpretation as shown in Table 2. According to [30], the various ranges of sorting in sandstones indicate the various environments of the sand. $σ1=∅(84−16)4+∅(95−5)6.6$\sigma _1 = \emptyset {{\left( {84 - 16} \right)} \over 4} + \emptyset {{\left( {95 - 5} \right)} \over {6.6}}

Figure 2 shows the range of sorted values lies between 0.54 and 1.42. This statistical calculation revealed two different categories, namely moderately sorted and poorly sorted. But moderately sorted is the most dominant, suggesting the samples were transferred farther away from the point of collection. From the result, the classification class of 0.71–1.0 represented the moderately sorted grain, while the latter category is within the range of 1.0–2.0. The energy and transportation of sediment distance are all functions of the distance of sorting values; therefore, the more the sediment is transferred from the source, the more the sample is moderately sorted and the closer the sediments to the source, the poor the samples sorted.

Skewness

Another parameter for the transportation history of sediments is Skewness and its determined using Equation (3) with the results presented in Table 3. It simply determines or measures symmetry in the scatter of distribution as well as degree of lopsidedness of a curve (Figure 3). Skewness is directly related to the fine and coarse tails of the size distribution, and hence suggestive of energy of deposition. $Sk1=(∅16+∅84−2∅50)2(∅84−∅16)+(∅5+∅95−2∅50)2(∅95−∅5)$S_{k1} = {{\left( {\emptyset 16 + \emptyset 84 - 2\emptyset 50} \right)} \over {2\left( {\emptyset 84 - \emptyset 16} \right)}} + {{\left( {\emptyset 5 + \emptyset 95 - 2\emptyset 50} \right)} \over {2\left( {\emptyset 95 - \emptyset 5} \right)}} On the basis of the result of this parameter calculated, all the sediments are positively and negatively skewed. The values ranged from −0.01 to 0.26. The most significant classifications identified are near symmetrically skewed (from −0.1 to 0.1) and finely skewed samples ranged from 0.1 to 0.3. This is an indication that samples are transported from various sources. The positive and negative values are the confirmation that the sediments were transported to and away from the source.

Kurtosis

The kurtosis is the peakedness of the distribution and measures the ratio between the sorting in the tails and central portion of the curve as given by Equation (4). The result of the classification scale for kurtosis is presented in Table 4 while the range of Kurtosis is 0.58–1.72 as shown in Figure 4. From the classifications (platykurtic, leptokurtic, very leptokurtic and mesokurtic), the classes of leptokurtic are the most predominant in the study area with 50% of the samples. This implies the central portions are better sorted at the tails and strongly suggests that the samples are located at the water concentrated zone. $KG=(∅95−∅5)2.44(∅75−∅25)$K_G = {{\left( {\emptyset 95 - \emptyset 5} \right)} \over {2.44\left( {\emptyset 75 - \emptyset 25} \right)}}

The Cross Plot Analysis

A graph of graphic mean values versus standard deviation, skewness against standard deviation as shown in Figures 5 and 6 respectively was used to determine the paleoenvironment of deposition of the soil samples from grain size analysis. Therefore, the graphical plots depict that all the samples analysed from the study area were deposited by the transitional environment of geological effects [31]. The multiple directional patters of the paleoenvironment of deposition of soil samples were suggested to be responsible for the moderately sorted impact on the soil samples.

Conclusion

The transportation history of the soil deposit of the Gbede area has been assessed and analysed using grain size distribution through statistical parameters of mean, standard deviation, skewness, kurtosis and cross plot analysis, respectively. The geological environment of the soil samples could be responsible for the poorly and moderately sorted characteristics, and near symmetrical and leptokurtic nature exhibited by the samples deposited in the location [32]. All locations are characterised by soil samples input from a mineral source.

#### Graphic Standard deviation with classes of sorting.

Graphic Standard DeviationClasses of Sorting
∅ 0.35 to ∅ 0.50well sorted
∅ 0.50 to ∅ 0.71moderately well sorted
∅ 0.71 to ∅ 1.00moderately sorted
∅ 1.00 to ∅ 2.00poorly sorted

#### Comparative result of the Grain Size Analysis for soil samples in phi (Φ).

SampleMeanSTDSkewnessKurtosis
GB11.461.01−0.051.35
GB21.550.54−0.121.36
GB31.960.64−0.131.50
GB41.290.950.181.03
GB51.091.260.031.17
GB61.190.910.061.72
GB72.061.20−0.251.66
GB81.501.260.010.97
GB92.330.84−0.081.21
GB101.690.420.201.40
GB112.030.570.260.58
GB121.370.870.021.66
GB131.940.62−0.131.67
GB141.190.910.061.72
Average1.620.954.3E-31.36

#### Description of the Soil samples with Grain Size Analysis.

Sample pointsDescriptions
GB1Medium sand, poorly sorted, ear symmetrical and leptokurtic.
GB2Medium sand, moderately well sorted, coarse-skewed and leptokurtic.
GB3Medium sand, moderately well sorted, coarse-skewed and leptokurtic.
GB4Medium sand, moderately sorted, fine skewed and mesokurtic.
GB5Medium sand, poorly sorted, near symmetrical and leptokurtic.
GB6Medium sand, moderately sorted, near symmetrical and very leptokurtic.
GB7Fine sand, poorly sorted, coarse-skewed and very leptokurtic.
GB8Medium sand, poorly sorted, near symmetrical and mesokurtic.
GB9Fine sand, moderately sorted, near symmetrical and leptokurtic.
GB10Medium sand, moderately sorted, fine skewed and leptokurtic
GB11Medium sand, moderately sorted, near symmetrical and leptokurtic
GB12Medium sand, moderately sorted, near symmetrical and very leptokurtic.
GB13Medium sand, moderately well sorted, coarse skewed and very leptokurtic.
GB14Medium sand, moderately sorted, near symmetrical and very leptokurtic.
AverageMedium sand, moderately sorted, near symmetrical and leptokurtic

#### Classification scale describing the skewness.

Classification scaleSkewness
∅ 0.1 to ∅ 0.3Fine skewed
∅ −0.1 to ∅ 0.1Near symmetrical
∅ −0.3 to ∅ −0.1Coarse-skewed

#### Classification of the graphic mean.

Graphic meanClassification
∅ – 1 to ∅ 0Very coarse sand
∅ 0 to ∅ 1Coarse sand
∅ 1 to ∅ 2Medium sand
∅ 2 to ∅ 3Fine sand
∅ 3 to ∅ 4Very fine sand

#### Classification scale and description of Kurtosis.

Classification scaleKurtosis
<∅ 0.67Very Platykurtic
∅ 0.67 to ∅ 0.90Platykurtic
∅ 0.90 to ∅ 1.11Mesokurtic
∅ 1.11 to ∅ 1.50Leptokurtic
∅ 1.50 to ∅ 3.00Very leptokurtic

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