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The analysis of grain-size sediments was carried out to determine the grading of textural parameters which result in the description of the energy environment, that of deposition and information on sediment transport. A German standard sieve set of mesh with shaker was used for the analysis. 12 sets of sediment sizes resulting in 84 samples from se...
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... geological materials used include: hammer, chisel, German standard sieve of sizes ( Figure 2), sample bags (for collection of stream sediments), measuring tape, paper tape, marker, global positioning system, compass clinometer, weighing balance, digital camera (to capture the stream sediments collected), electrical vibratory machine, hand lens, computer with a spread sheet program, field note (to record necessary the observation) and topographical map. The procedure for the analysis involves disaggregation of eight samples by soaking in water for 24 hours to obtain individual grains and was exposed to sun for two days to ensure the samples are free of water. ...
Citations
... Standard deviation indicates the degree of sediment sorting. Sorting can be used to identify energy and deposition environments where well-sorted sediments will be deposited in high-energy depositional environments [10] . The calculation results in Table 9 show that each sample has a very good degree. ...
... The result of skewness calculations in each sample shown in Table 9 found that the skewness value of the research area is nearly symmetrical to fine skewed. Maity and Maiti (2016) in Atat et al. (2021) said that skewed values that are close to negative or tend to be small are associated with high energy environment [12] [10]. It is shown by fifth to eight samples in Figure 5 that the skewness value is decreasing and related with the decreasing current strength from the water as fluvial medium. ...
The flow of volcanic material from Semeru Volcano as one of the most active volcano on Java Island is still flowing and deposited along the Besuk Kobokan River which empties into Bambang Beach, Lumajang. The sediments as volcanic products are transported through the river environment with water as its transport agent. This transportation and deposition process has certain grain texture characteristics that can be identified through granulometry analysis. Geological observations and sediment sampling were carried out at 8 points along the Besuk Kobokan River. Granulometry analysis aims to identify the grain texture distribution of Semeru Volcano sediments that have been transported. The results of granulometric analysis that can identify the distribution of grain texture show that the first point to the fifth point at the sampling location was influenced by lahars flow and pyroclastic flow, resulting in randomness in grain size and rock texture. While the sixth to eighth points show the fluvial sedimentation process.
... Kurtosis, (Folk and Ward, 1957;Folk, 1968;Gandhi and Raja, 2014;Oladipo et al., 2018;Atat et al., 2018;Atat et al., 2022). ...
Stream sediments were collected from different areas in Ifelodun county within latitude 8°451N to 8°51N and longitude 4°461E to 5°61E. 13 samples of sediments were obtained from each of the eight locations totaling 108. A German standard sieve set of mesh with shaker was used for grain size analysis. The results of grain size analysis range from medium to very coarse sand as the majority of sediment composition before investigation of textural parameters. The result of textural parameters was obtained and used to differentiate the depositional environment of the sediments. The mean, sorting, skewness and kurtosis defined the sediment as very coarse, poorly sorted, vary from very fine skewed to near symmetrical with mesokurtic environment, although the other extreme tails also exist. Positively skewed and poorly sorted sediments characterized low energy environment.
... The use of statistical parameters for environmental reconstruction helps to discriminate ancient environments [4]. Textural parameter such as skewness is an environmental indicator [5][6][7][8]. Texture refers to the properties of sediment such as particle size, shape, roundness and sorting. A well sorted lithology (matrix) is one in which the grains are all about the same size. ...
... By his second law, this body will move in the direction in which the force acts [9]. A greater energy is needed for a greater force to be applied to move sediment and where this energy cannot supply the minimum required force, the coarser matrices are deposited [5]. In overall, coarser sediment are left behind by the transportation process and found closer to its source; fine ones are found farther away from the source. ...
... Textural parameters can be obtained from percentile deduction and statistical analysis of data. It relates with the velocity of the medium accountable for the transportation and deposition of sediment (or sandshale lithology in this research) [5][6]. Table 1 highlights different interpretation of these parameters with their corresponding values. ...
Assessment of textural parameters was carried out to decide on a better technique suitable for porosity estimates in the area of study. Data were obtained from wells A and B and used to generate suites of logs like gamma ray, density and sonic. Microsoft Excel was used for the analysis. The lithology was identified as sand for gamma ray information less than 75 API (or shale if this value is greater than 75 API). Three major Techniques (such as Techniques one, two and three) as deliberated in the subsection of the discussion were examined. Others are Techniques four and five for both wells A and B. The average result of porosity estimates for the three major Techniques are approximately 33%, 35% and 20% from one, two and three respectively for well A. also, 28%, 31% and 16% from one, two and three respectively for well B. With the result of semi-interquartile range, Technique three is seen with the lowest range of spread of the result (that is, 2.75 for well A and 3.00 for well B) and is strongly recommended as the best approach for porosity estimates. Where only sonic data is available, Technique one show better result and should be preferred over Technique two. The coefficient of variation shows that all the results obtained from these five approaches fall within low variance. The matrices making up the lithology are therefore, very poorly sorted, near symmetrically skewed and platykurtic for well A; extremely poorly sorted, coarse skewed indicating high energy environment and leptokurtic for well B. Moreso, the porosity information deduced for both wells from Technique three, categorised them into the good class.
... The use of statistical parameters for environmental reconstruction helps to discriminate ancient environments [4]. Textural parameter such as skewness is an environmental indicator [5][6][7][8]. Texture refers to the properties of sediment such as particle size, shape, roundness and sorting. A well sorted lithology (matrix) is one in which the grains are all about the same size. ...
... By his second law, this body will move in the direction in which the force acts [9]. A greater energy is needed for a greater force to be applied to move sediment and where this energy cannot supply the minimum required force, the coarser matrices are deposited [5]. In overall, coarser sediment are left behind by the transportation process and found closer to its source; fine ones are found farther away from the source. ...
... Textural parameters can be obtained from percentile deduction and statistical analysis of data. It relates with the velocity of the medium accountable for the transportation and deposition of sediment (or sandshale lithology in this research) [5][6]. Table 1 highlights different interpretation of these parameters with their corresponding values. ...
Assessment of textural parameters was carried out to decide on a better technique suitable for porosity estimates in the area of study. Data were obtained from wells A and B and used to generate suites of logs like gamma ray, density and sonic. Microsoft Excel was used for the analysis. The lithology was identified as sand for gamma ray information less than 75 API (or shale if this value is greater than 75 API). Three major Techniques (such as Techniques one, two and three) as deliberated in the subsection of the discussion were examined. Others are Techniques four and five for both wells A and B. The average result of porosity estimates for the three major Techniques are approximately 33%, 35% and 20% from one, two and three respectively for well A. also, 28%, 31% and 16% from one, two and three respectively for well B. With the result of semi-interquartile range, Technique three is seen with the lowest range of spread of the result (that is, 2.75 for well A and 3.00 for well B) and is strongly recommended as the best approach for porosity estimates. Where only sonic data is available, Technique one show better result and should be preferred over Technique two. The coefficient of variation shows that all the results obtained from these five approaches fall within low variance. The matrices making up the lithology are therefore, very poorly sorted, near symmetrically skewed and platykurtic for well A; extremely poorly sorted, coarse skewed indicating high energy environment and leptokurtic for well B. Moreso, the porosity information deduced for both wells from Technique three, categorised them into the good class.
This research was carried out to determine the formation brittleness and textural attributes of an Axx-Field in the Niger Delta Basin. Data available for this study were obtained from three wells (A11, A22 and A33). They were analyzed using Microsoft Excel after spurious values were removed. Two techniques (John Fuller and Plumb Bradford) were used to determine static young's modulus but only one is reported because after statistical analysis on both, the one reported was adequate for accurate outcomes although both approaches have low variance. The results indicates that the highest values of static and dynamic young's modulus are 2.05 x 1025N/m2 and 1.93 x 1010N/m2 respectively. The lowest values are 1.1 x 1024N/m2 and 8.5 x 109N/m2 respectively for well A11. The average values are 1.024 x 1025N/m2 and 5.25 x 1023N/m2 for this well. For well A22, the highest and lowest values of dynamic young’s modulus are 1.5848 x 1010N/m2 and 1.5726 x 1010N/m2 while those of static are correspondingly 1.47 x 1025N/m2 and 9.01 x 1014N/m2. Their average values are 1.5787 x 1010N/m2 and 7.35 x 1024N/m2 for dynamic and static young's moduli respectively. Also, for well A33, dynamic has the lowest value as 3.28 x 1010 N/m2; static has 8.36 x 1014N/m2 and their highest correspond to 2.04 x 1010N/m2 and 7.08 x 1025N/m2. The average value for this well are 2.66 x 1010N/m2 and 3.54 x 1025 N/m2 for dynamic and static respectively. The static Young's modulus results define the formation as brittle, whose environment is extremely poorly sorted, very fine skewed and very leptokurtic with low energy for well A11, very poorly sorted, fine skewed and platykurtic for well A22, extremely poorly sorted, very fine skewed and mesokurtic for well A33.
Samples of soil were obtained from two pits, about 1.8m from a collapsed location along Iman Street in Uyo, Akwa Ibom State, Nigeria. These soil samples were analysed in a laboratory using different sieve sizes to separate dried grains with the help of a sieve shaker. Microsoft Excel was used for the processing of this information and computations. The paint package of Windows 7.1 software was also used to deduce the necessary parameters for gradation result. The result of the grain size distribution yielded the uniformity coefficient, coefficient of gradation and sorting coefficient as 2.71, 0.91 and 1.64. This information classified the soil as poorly graded and more susceptible to soil liquefaction; most of the particles are approximately of the same size; they are not suitable for construction as they may not be compacted better like well-graded soil. The average percentage of natural water content was achieved as 23.99%. However, from the Atterberg limit investigation, the water content where the liquid limit corresponds to the number of blows as 25 is 45.56%. The plasticity index obtained is 18.58%. For better assessment information, some indices results were necessary which include Activity Index = 0.4494%, Liquid Index =-0.61609, Consistency Index = 1.1609, Flow Index = 1.4390, Toughness Index = 12.9117 and Group Index = 3.7042. The plasticity index is high. This may show a marked reduction in bearing capacity with increase moisture content. The soil (laterite) clay mineral is Kaolinite which has low shrink-swell potential. It can hold large amount of water and still stay in plastic state. It is medium compressible since its liquid limit is within 30% to 50%. It is cohessionless and with the value of the group index obtained, the soil subgrade may be predicted as belonging to a fair class.
