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

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).

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.

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. (1)Mz=∅16+50+843Mz = \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].

Figure 1

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. (2)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.

Figure 2

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. (3)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.

Figure 3

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. (4)KG=(∅95−∅5)2.44(∅75−∅25)K_G = {{\left( {\emptyset 95 - \emptyset 5} \right)} \over {2.44\left( {\emptyset 75 - \emptyset 25} \right)}}

Figure 4

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.

Figure 5

Figure 6