Advances in Geological and Geotechnical Engineering Research | Vol.4, Iss.4 October 2022

Geotechnical Engineering and Architectural Preservation of historic buildings, Conservation Department, faculty of
archaeology, Cairo university., Egypt
Editor-in-Chief
Prof. Sayed Hemeda
Amin Beiranvand Pour
Universiti Malaysia Terengganu, Malaysia
Associate Editor
Editorial Board Members
Reza Jahanshahi, Iran
Salvatore Grasso, Italy
Shenghua Cui, China
Golnaz Jozanikohan, Iran
Mehmet Irfan Yesilnacar, Turkey
Ziliang Liu, China
Abrar Niaz, Pakistan
Sunday Ojochogwu Idakwo, Nigeria
Jianwen Pan, China
Wen-Chieh Cheng, China
Wei Duan, China
Intissar Farid, Tunisia
Bingqi Zhu, China
Zheng Han,China
Vladimir Aleksandrovich Naumov, Russian Federation
Dongdong Wang, China
Jian-Hong Wu, Taiwan
Abdel Majid Messadi, Tunisia
Himadri Bhusan Sahoo, India
Vasiliy Anatol’evich Mironov, Russian Federation
Maysam Abedi, Iran
Anderson José Maraschin, Brazil
Alcides Nobrega Sial, Brazil
Ezzedine Saïdi, Tunisia
Mokhles Kamal Azer, Egypt
Ntieche Benjamin, Cameroon
Jinliang Zhang, China
Kamel Bechir Maalaoui, Tunisia
Shimba Daniel Kwelwa,Tanzania
Antonio Zanutta, Italy
Nabil H. Swedan, United States
Swostik Kumar Adhikari,Nepal
Irfan Baig, Norway
Shaoshuai Shi, China
Sumit Kumar Ghosh, India
Bojan Matoš, Croatia
Massimo Ranaldi, Italy
Zaman Malekzade, Iran
Xiaohan Yang, Australia
Gehan Mohammed, Egypt
Márton Veress, Hungary
Vincenzo Amato, Italy
Sirwan Hama Ahmed, Iraq
Siva Prasad BNV, India
Ahm Radwan, Egypt
Nadeem Ahmad Bhat, India
Mojtaba Rahimi, Iran
Mohamad Syazwan Mohd Sanusi, Malaysia
Sohrab Mirassi, Iran
Gökhan Büyükkahraman, Turkey
Zhouhua Wang, China
Bahman Soleimani,Iran
Luqman Kolawole Abidoye,Nigeria
Tongjun Chen,China
Saeideh Samani,Iran
Khalid Elyas Mohamed E.A.,Saudi Arabia
Mualla Cengiz,Turkey
Hamdalla Abdel-Gawad Wanas,Saudi Arabia
Gang Li,China
Williams Nirorowan Ofuyah,Nigeria Ashok
Sigdel,Nepal
Richmond Uwanemesor Ideozu,Nigeria
Ramesh Man Tuladhar,Nepal
Mirmahdi Seyedrahimi-Niaraq, Iran
Editor-in-Chief
Prof. Sayed Hemead
Prof. Wengang Zhang
Prof. Amin Beiranvand Pour
Associate Editor
Cairo University,Egypt
Chongqing University,China
Geotechnical Engineering and Architectural Preservation of historic buildings, Conservation Department, faculty of
archaeology, Cairo university., Egypt
Editor-in-Chief
Prof. Sayed Hemeda
Amin Beiranvand Pour
Associate Editor
Reza Jahanshahi, Iran
Salvatore Grasso, Italy
Shenghua Cui, China
Golnaz Jozanikohan, Iran
Mehmet Irfan Yesilnacar, Turkey
Ziliang Liu, China
Abrar Niaz, Pakistan
Sunday Ojochogwu Idakwo, Nigeria
Jianwen Pan, China
Wen-Chieh Cheng, China
Wei Duan, China
Intissar Farid, Tunisia
Bingqi Zhu, China
Zheng Han,China
Vladimir Aleksandrovich Naumov, Russian Federation
Dongdong Wang, China
Jian-Hong Wu, Taiwan
Abdel Majid Messadi, Tunisia
Himadri Bhusan Sahoo, India
Vasiliy Anatol’evich Mironov, Russian Federation
Maysam Abedi, Iran
Anderson José Maraschin, Brazil
Alcides Nobrega Sial, Brazil
Ezzedine Saïdi, Tunisia
Mokhles Kamal Azer, Egypt
Ntieche Benjamin, Cameroon
Jinliang Zhang, China
Kamel Bechir Maalaoui, Tunisia
Shimba Daniel Kwelwa,Tanzania
Antonio Zanutta, Italy
Nabil H. Swedan, United States
Swostik Kumar Adhikari,Nepal
Irfan Baig, Norway
Shaoshuai Shi, China
Sumit Kumar Ghosh, India
Bojan Matoš, Croatia
Massimo Ranaldi, Italy
Zaman Malekzade, Iran
Xiaohan Yang, Australia
Gehan Mohammed, Egypt
Márton Veress, Hungary
Vincenzo Amato, Italy
Sirwan Hama Ahmed, Iraq
Siva Prasad BNV, India
Ahm Radwan, Egypt
Nadeem Ahmad Bhat, India
Mojtaba Rahimi, Iran
Mohamad Syazwan Mohd Sanusi, Malaysia
Sohrab Mirassi, Iran
Zhouhua Wang, China
Bahman Soleimani,Iran
Luqman Kolawole Abidoye,Nigeria
Tongjun Chen,China
Saeideh Samani,Iran
Khalid Elyas Mohamed E.A.,Saudi Arabia
Mualla Cengiz,Turkey
Hamdalla Abdel-Gawad Wanas,Saudi Arabia
Gang Li,China
Williams Nirorowan Ofuyah,Nigeria Ashok
Sigdel,Nepal
Richmond Uwanemesor Ideozu,Nigeria
Ramesh Man Tuladhar,Nepal
Mirmahdi Seyedrahimi-Niaraq, Iran
Olukayode Dewumi Akinyemi,Nigeria

Editor-in-Chief
Prof. Sayed Hemead
Prof. Wengang Zhang
Advances in Geological
and Geotechnical
Engineering Research
Volume 4 Issue 4 ·October 2022· ISSN 2810-9384 (Online)

Volume 4 ｜ Issue 4 ｜ October 2022 ｜ Page1-49
Advances in Geological and Geotechnical Engineering Research
Contents
Articles
1 Use of GIS to Estimate Recharge and Identification of Potential Groundwater Recharge Zones in the
Karstic Aquifers, West of Iran
Zeinab Najafi Gholam Hossein Karami
14 Petrography and Geochemical Studies of Basement Rocks around Zango-Daji and Its Environs, North
Central Nigeria
Simon D. Christopher Onimisi A. Jimoh Onimisi A. Martins
22 Aeromagnetic Interpretation of Basement Structures and Geometry in Parts of the Middle Benue
Trough, North Central, Nigeria
Esho Oluwaseyi Olatubosun Osisanya Olajuwon Wasiu Ibitoye Taiwo Abel Ajibade Femi Zephaniah
Tokunbo Sanmi Fagbemigun
41 Assessment of Subsurface Competency Using Geotechnical Method of a Proposed Structure F.C.T Nigeria
Osisanya O. Wasiu Diyanuwa A. Buduwara Korode A. Isaac Ibitoye T. Abel Ajibade F. Zephaniah

1
Advances in Geological and Geotechnical Engineering Research | Volume 04 | Issue 04 | October 2022
https://ojs.bilpublishing.com/index.php/agger
Copyright © 2022 by the author(s). Published by Bilingual Publishing Co. This is an open access article under the Creative Commons
Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License. (https://creativecommons.org/licenses/by-nc/4.0/).
*Corresponding Author:
Gholam Hossein Karami,
Department of Geology, Earth Sciences Faculty, Kharazmi University, Tehran, Iran;
Email: g.karami@khu.ac.ir
DOI: https://doi.org/10.30564/agger.v4i4.5122
ARTICLE
Use of GIS to Estimate Recharge and Identification of Potential
Groundwater Recharge Zones in the Karstic Aquifers, West of Iran
Zeinab Najafi1
Gholam Hossein Karami2*
1. Department of Environmental Geology and Hydrogeology, Earth Sciences Faculty, Shahrood University of Technology,
Semnan, Iran
2. Department of Geology, Earth Sciences Faculty, Kharazmi University, Tehran, Iran
ARTICLE INFO ABSTRACT
Article history
Received: 4 October 2022
Revised: 31 October 2022
Accepted: 5 November 2022
Published Online: 11 November 2022
Estimating and studying groundwater recharge is necessary and important
for the management of water resources. The main aim of this work is to
estimate the value of the annual recharge in some parts of the Kermanshah
and Kurdistan province located in the west of Iran. There are many ap-
proaches available for estimation of the recharge, but RS (remote sensing)
and GIS (geographic information system) have provided and combined a
lot of effective spatial and temporal data of large areas within a short time.
For this purpose, nine information layers including the slope, aspect of
slope, lithology, lineament density, drainage density, precipitation, vege-
tation density, soil cover, and karst features were prepared and imported
to the ArcMap software. After preparing the information layers, they have
to weigh based on their effects on the value of the recharge. In order to be
weighted the different parameters, methods of judgment expert, reciprocal
influences of parameters, and AHP were used. Using GIS, the results ob-
tained from the final map indicated the average value for the recharge based
on the average calculated coefficient of recharge. The annual recharge coef-
ficient in the study area was estimated to be between 30% and 80%.
Keywords:
Karstic aquifers
Remote sensing
GIS
AHP
Aquifer recharge coefficient
Iran
1. Introduction
Groundwater potential is dependent on recharge, its
management and sustainability require the identification
of the recharge coefficient. Recharge has an impact on
the groundwater flow and transport models. It is the most
important parameter for abstraction from a groundwater
reservoir, especially in arid and semiarid regions, like
Iran, because the extraction from groundwater should not
be larger than the average recharge (in the long term).
The aquifer recharge can be determined by different
methods. These methods can be put into several groups
such as studies on surface water, unsaturated zone, saturat-
ed zone, and remote sensing. Each of these methods can
be divided into subgroups. The techniques based on sur-
face water studies have included the surface-water budget,
hydrograph analysis, direct measurement [1-6]
, seepage
meters [7]
, and natural and artificial tracers. The technics

2
based on unsaturated-zone studies contain methods of as-
sessment of the soil moisture balance. Many authors have
worked in this field [8-10]
.
Some methods have been used in both the saturated and
unsaturated-zones such as water balance [11-13]
and Darcy’s
low. The application of this method requires knowing
the hydraulic conductivity and hydraulic head in the un-
saturated zone. Natural tracers [14-17]
and CMB (Chloride
Mass Balance) has been used by many researchers for the
estimation of recharge [18,19]
. Water table fluctuation (WTF)
is based on the measurement of the water level before
and after precipitation, and with this measurement, the re-
charge has been estimated [19]
. Modeling is another method
for evaluation of the recharge [19,20]
. The appropriate tech-
nique used for estimating the recharge in a saturated zone
is the saturated volume fluctuation (SVF).
As is clear, there are many different ways to estimate
the recharge, but because of the expanse of the study area,
using the RS (remote sensing) and GIS (geographic infor-
mation system) is the most beneficial way. Other methods
are usable at the local scale, in which there is a lot of data
and equipment. The RS and GIS are powerful and unique
tools used for managing and evaluating vital groundwa-
ter recharge. These have provided and combined a lot of
effective spatial and temporal data of large areas within a
short time. The RS and GIS technics have been used for
the evaluation and estimation of the recharge zone [21-29,13]
.
The study area covers an area of about 22000 km2
sit-
uated in Kermanshah and Kurdistan province in the West
of Iran (Figure 1). It is located between 46.48° – 47.93°E
longitude and 34.19° – 35.36°N latitude. The study area
has a Mediterranean climate. The temperature ranges
between 6 °C and 21 °C, and the total annual rainfall is
ranging from 350 mm to 750 mm. Based on the average
precipitation and temperature in this area, by using the
interpolation of weather station data in the study area,
there are three different zones of precipitation. Zone A in
the central and west has 385 mm, zone B with 700 mm
in the northwest, and other parts have 515 mm. The pop-
ulation in the study area is more than 2 million people.
Most of this area is covered by the mountainous region.
The most geologic setting of the studied area is limestone
and dolomite, and there is sandstone, shale, conglom-
erate, radiolarite, volcanic rocks consisting of andesite
and gabbro in some parts, and also alluvium deposits of
Quaternary age (this part has an agricultural plain). The
major formations in the mountainous region are carbonate
formations.
Figure 1. Geological map of the study area
Figure 1 shows the geological map of the area. There are several important karstic
aquifers in the study area .Because of the breadth of the study area, using RS and GIS is
the most efficient way. The estimated recharge for the different geological settings is
more complicated because this area has more heterogeneity, so GIS is a very convenient
tool. The objection against GIS is the large number of expert comments it received. This

3
Figure 1 shows the geological map of the area. There
are several important karstic aquifers in the study area.
Because of the breadth of the study area, using RS and
GIS is the most efficient way. The estimated recharge for
the different geological settings is more complicated be-
cause this area has more heterogeneity, so GIS is a very
convenient tool. The objection against GIS is the large
number of expert comments it received. This problem has
partly been solved using the methods applied in this paper.
The fundamental aims of this study are:
1) Estimating the annual recharge in the study area
2) Determining the important potential zone for the re-
charge
3) Introducing and comparing several different meth-
ods weighing in GIS to evaluate the recharge.
2. Materials and Methods
To achieve the above goals, GIS was applied. The mean
coefficient of annual recharge for this area was calculat-
ed using three various methods for weighing, rating and
comparing with others and the real discharge was meas-
ured. First, the layers of information corresponding to the
recharge were introduced into GIS. For this work, ENVI
4.3, Google Earth, global mapper 13, and ArcGIS 10 were
used. Finally, the layers were entered into ArcMap. For
rating and weighing the prepared layers, three methods
were used, which are explained below.
3. Results and Discussion
The estimation and evaluation of the groundwater re-
charge potential zones have been explored by analyzing
the various parameters such as lithology, slope, aspect,
Lineament density, drainage density, precipitation, karst
feature, soil cover, and vegetation cover, it has been done
by using 3 different weighting methods in ArcGIS. The
most important information layers which affect the re-
charge into the study area’s aquifers are as follows:
3.1 Lithology
Lithology and hydrographic network influence the lin-
eaments and drainage as a function of porosity (primary
and secondary), and water percolation. The distribution
of the lithological formation was taken from a geological
map of 1:250000 scale for Kermanshah [30]
, as a base, but
had to be combined with the fieldwork. Satellite images
(Landsat 7 ETM+) and Google Earth were used for more
exact matching. The area mainly has been covered by
carbonate formation and in some part igneous rocks and
shale, sandstone, whereas the surrounding mountains were
covered by alluvial plains, this study aims is assessment
the recharge in Karstic aquifers, so, only carbonate forma-
tions has been considerated (Figure 3a).
3.2 Slope
The slope is an influencing factor for the percolation
resulting in the recharge. The slope information layer was
created using the Digital Elevation Model (DEM) of the
studied area. Then it was classified in ArcGIS by degree
(Figure 3b).
In steep areas, the possibility of the presence of soil
and vegetative on limestone is usually low. However,
sinkholes and other solutional cavities are generally ab-
sent in the steep regions. Therefore, the flat regions with
low slopes, particularly on the top of mountains, play an
important role in karst aquifer recharges.
3.3 Aspect (Slope Direction)
The angle of the sunbeam varies in different slope
directions. Such that the resistance time of snow in the
North and North-East slopes is larger than in South and
South-West directions. Therefore, this factor has a major
impact on the recharge, especially in snowy areas. This
layer was also produced by DEM and then reclassified in
ArcGIS. The North-facing slopes gave more values (Fig-
ure 3c).
3.4 Lineament Density
The term lineament is commonly used for some geo-
logical linear features. The main lineament features are the
fault, rift valleys, axial traces of folds, joints and fractures,
vegetation along, dike, layering of stratification, rivers,
and valleys.
The faults and joints provide the possibility of per-
colation. The dissolution causes a larger space for more
infiltrating water. The most accurate method for providing
a lineament map is the fieldwork but it is more expensive
and has a limited spatial viewpoint. Thus, maps of linea-
ment can be made using remote sensing. For this purpose,
geological maps, satellite images (Panchromatic band of
IRS), and Google earth were used. A satellite image enters
the ENVI software, and by using the appropriate filter, be-
comes an apparent lineament in the studied area. Google
Earth was used to correct it. More values were devoted
to a higher density of lineaments. The final map was pro-
duced by the ArcGIS software (Figure 3d).
3.5 Drainage Density
The stream is the drainage path for the passage of wa-
ter from the highland to lowland regions. So, the drainage
density can influence the recharge because water has more

4
time to penetrate. Because the stream contains a large val-
ue of water for a long time, it becomes the most important
in the top order. This layer was plotted by ArcGIS, and
controlled by Google Earth (Figure 3e).
3.6 Precipitation
More amount of precipitation results in a greater value
of recharged water (in the warm months which is the high
evaporation, precipitation is very low or does not occur).
This layer was prepared by the data available for 12
stations in the study area that had long-time data (a thir-
ty-year period).
Generally, the precipitation occurs concentrated rela-
tively, during the period in which evaporation is low, for
example, according to the data of Kermanshah station, the
total precipitation for the water year of 2015-2016 was
654 mm, 524 mm of this amount, has been recorded in 20
days (from the end of November until March and 1 day in
May). This period had 67 days of frost days. Due to the
presence of large development karst areas with wide karst
features in the area, the rate of recharge in mountainous
areas is relatively high (Figure 3f).
3.7 Karst Feature
Other parameters that can influence the value of re-
charging water are Karst features. In the areas with low
slopes and low evaporation (due to high elevation), up to
90% of precipitation can be infiltrated [31]
. In the carbonate
formation, at several points in the study area, sinkholes
were observed. Such karst features can be the most effec-
tive factor for recharge (Figure 3g).
3.8 Soil Cover
Most part of this area does not have the run-off, pre-
cipitation infiltrated, or volatilize. In the karstic part of the
study area, cracks are, observed in the soil. The area ob-
served related to subsurface drainage (cracks in epikarst,
according to the studied area) (Figure 2).
The soil cover layer was extracted from the satellite
images, and then it was inputted into the ArcGIS software
(Figure 3h).
3.9 Vegetation Density
The last layer of information produced was the vege-
tation density layer. To achieve the above aim, satellite
images of ETM+
and the NDVI software were used. This
index was created by subtracting bands 3 and 4 and divid-
ing by the total of them. Its range was between –1 and +1.
Devoid of vegetation gave-1 and increased with the vege-
tation cover (Figure 3i).
The effect of each factor on recharge relative to the
others is different. In the next step and before overlapping
layers, it is necessary to determine the relative importance
of each layer to the other layers. The expert judgment is
very impressive when weighing ArcGIS. In this work,
three methods were used for weighing, and the effect of
the expert judgment in them is getting less. Finally, the
outputs of the different methods were compared.
3.10 Expert Judgment
In this approach, determining the estimated weighed
is based on the expert opinion that is at a specific scale,
for example on a scale of 1 to 100. The 100 points were
divided among the various criteria. A score of zero will
be allocated to one parameter, and this parameter will be
ignored. In the event that just one parameter takes 100
points, this has been considered only. The coefficients and
weights for this method are presented in Table 1.
a b
Figure 2. a) Cracks observed in the soil at the south of Ravansar, of the area, b) Epikarst observed on a mountain at
north of Kermanshah

5
3.11 Reciprocal Influences of Parameters Method
This method has been used by Shaban (2003) for the
first time [21]
. For this approach, the expert idea is effec-
tive only in the early stage (in the rating categories). Then
each of the criteria had been evaluated and then divided
into the effective major and minor parameters. One (1)
point is allocated to the major effect and the minor effect
gets half of the point. The sum of all points for each crite-
rion produces its coefficient. The measured weight of each
coefficient is multiplied by its initial coefficient. The final
weight was obtained by summing the weights. Figure 4
shows the effect of parameters on each other.
The calculated effect for each influencing factor is ex-
pressed as follows:
Lithology: 2 minor + 4 majors = 0.5 × 2 + 1 × 4 = 5
Lineament density: 1 minor + 2 major = 0.5 × 1 + 1 × 2 = 2.5
Drainage density: 1 minor + 2 major = 0.5 × 1 + 1 × 2 = 2.5
Karst feature: 2 minor + 2 majors = 0.5 × 2 + 1 × 2 = 3
Precipitation: 4 majors = 1 × 4 = 4
Soil cover: 1 minor + 3 major = 0.5 × 1 + 1 × 3 = 3.5
Aspect: 3 minor + 1 majors = 0.5 × 3+ 1 × 1 = 2.5
Slope: 1 minor + 3 major = 0.5 × 1 + 1 × 3 = 3.5
Vegetation: 1 minor + 3 major = 0.5 × 1 + 1 × 3 = 3.5
To obtain the weight of each factor, the calculated ef-
fect and coefficient must be multiplied (Table 2). Finally,
a)Geology of the study area b) Slope map of the study area c) Aspect map of the study area
d) Lineament density map of the study
area
e) Drainage density map of the study
area
f) Precipitation map of the study area
g) Karst feature map of the study area h) Soil cover in the study area i)Vegetation map of the study area
Figure 3. Prepared effective layers on aquifer recharge
The effect of each factor on recharge relative to the others is different. In the next
step and before overlapping layers, it is necessary to determine the relative importance of
each layer to the other layers. The expert judgment is very impressive when weighing
ArcGIS. In this work, three methods were used for weighing, and the effect of the expert
judgment in them is getting less. Finally, the outputs of the different methods were
compared.
3.10 Expert Judgment
Figure 3. Prepared effective layers on aquifer recharge

6
Table 1. Categorization and weights of factors influencing recharge based on expert judgment
Weight
Rate
Classify
Affecting factor
Weight
Rate
Classify
Affecting factor
14%
2
4
5
6
7
8
0-20%
20-30%
30-40%
40-55%
55-70%
>70%
Drainage density
10%
3
4
6
8
9
<30%
30-50%
50-65%
65-80%
>80%
Lineament density
15%
9
7
6
4
3
2
Very high
High
Moderate
Low
Very low
Without karst feature
Karst feature
14%
–5
–3
–1
6
9
Very high
High
Moderate
Low
Without soil cover
Soil cover
10%
8
6
4
5
3
N-NE
SE-E
NW-W
SW-S
----
Aspect
10%
7
6
4
2
1
High
Moderate
Low
Very low
Without vegetation
Vegetation
14%
9
7
6
5
4
3
2
0-1
1-5
5-7.5
7.5-12.5
12.5-22
22-33
>33
Slope
8%
1
2
3
4
5
6
7
8
9
360-400
400-450
450—500
500-550
550-600
600-640
640-680
680-740
740-780
precipitation(mm)
5%
7
0
0
5
0
0
Karst
Alluvial
Conglomerate
Impure karst
Shale – Sandstone
Volcanic rocks
Lithology
Figure 4. Schematic sketch showing affective parameters concerning aquifer recharge

7
Table 2. Categorization and weight of selection factors influencing recharge, based on reciprocal influences of parame-
ters method
Total weight
Sum
Weight |a*b|
The calculated effect (b)
Rate (a)
Classify
Factor
12
85
10
5
15
35
20
5
0
0
0
5
0
Shale – Sandstone
Volcanic rocks
shale-sandstone
Karst-Impure karst
Conglomerate
alluvial
Lithology
11
77.5
10
12.5
15
17.5
22.5
2.5
4
5
6
7
9
0-20%
20-30%
30-45%
45-70%
>70%
Lineament density
10
67.5
5
10
15
17.5
20
2.5
2
4
5
6
7
8
0-20%
20-30%
30-40%
40-55%
55-70%
>70%
Drainage density
9
66.5
17.5
10.5
3.5
7
28
3.5
5
3
1
2
8
Very high
High
Moderate
Low
Without soil cover
Soil cover
11
80.5
28
21
14
10.5
7
3.5
8
6
4
3
2
>5
5-12.5
12-22
22-33
>33
Slope
9
65
20
15
10
12.5
7.5
2.5
8
6
4
5
3
N-NE
SE-E
NW-W
SW-S
-
Aspect
12
84
27
21
18
12
6
3
9
7
6
4
2
Very high
High
Moderate
Low
Very low and Without karst
feature
Karst feature
10
70
24.5
21
14
7
3.5
3.5
7
6
4
2
1
High
Moderate
Low
Very low
Without vegetation
Vegetation
16
112
8
16
20
32
36
4
2
4
5
8
9
360-450
450-550
550-640
640-740
>740
precipitation(mm)

8
the weight of each factor must be integrated. The sum of
the weights, in this case, was equal to:
85+84+80.5+65+77.5+112+67.5+70+66.5=708
The percentages of the factor affecting the recharge
were as follow:
Lithology: (85/708) × 100 ≈ 12
Lineament density: (77.5/708) × 100 ≈ 11
Drainage density: (67.5/708) × 100 ≈ 10
Karst feature: (84/708) × 100 ≈ 12
Precipitation: (112/708) × 100 ≈ 16
Soil cover: (66.5/708) × 100 ≈ 9
Aspect: (65/708) × 100 ≈ 9
Slope: (80.5/708) × 100 ≈ 11
Vegetation: (70/708) × 100 ≈ 10
3.12 AHP Method
The Analytical Hierarchy Process (AHP) has been de-
signed to solve multivariate problems by Saaty (1986) [32]
.
The values in this method are assigned from one to nine
(Table 3). The AHP variable elements in each level are
compared with the higher-level elements. The weights are
called the relative weight and by using the integration of
relative weights, the absolute weight is calculated (Figure
5). Using this method, the calculated weight for each fac-
tor varies between zero and one. The closer to one is the
more important factor, and vice versa. Using the expertise
and the extension of AHP in ArcMap software, the rela-
tive and absolute weights are calculated for each criterion
(Table 4).
Using these methods, the coefficient of annual recharge
is then calculated.
The maps obtained for each selection factor (in each
method) were produced as layers. The ArcGIS software
was applied to the overlaying of these layers with de-
termined weights together. The resulting maps for each
method are shown in Figure 6.
Table 3. Scale of relative importance for the AHP method
(according to [32]
)
Intensity of importance Definition
1 Equal importance
2 Weak
3 Moderate importance
4 Moderate plus
5 Strong importance
6 Strong plus
7 Very strong importance
8 Very very strong importance
9 Extreme importance
Figure 5. The matrix of the AHP method
Table 4. Categorization and weight of factors influencing recharge based on the AHP method
Calculated weight by
software
Rate
Categorize
Factor
Calculated weight
by software
Rate
Categorize
Factor
0.181
7
>5
5-12.5
12-22
22-33
>33
Slope
0.094
6
Shale – Sandstone
Volcanic rocks
shale-sandstone
Karst-Impure karst
Conglomerate
alluvial
Lithology

9
Calculated weight by
software
Rate
Categorize
Factor
Calculated weight
by software
Rate
Categorize
Factor
0.016
4
N-NE
SE-E
NW-W
SW-S
-
Aspect
0.125
7
0-20%
20-30%
30-45%
45-70%
>70%
Lineament density
0.397
9
Very high
High
Moderate
Low
Very low and
Without karst
feature
Karst
feature
0.022
5
0-20%
20-30%
30-40%
40-55%
55-70%
>70%
Drainage density
0.037
6
Very high
High
Moderate
Low
Without soil cover
Soil cover
0.027
5
High
Moderate
Low
Very low
Without vegetation
Vegetation
0.101
7
360-450
450-550
550-640
640-740
>740
precipita-
tion(mm)
a) Expert judgment method
b) reciprocal influences of parameters method
Table 4 continued

10
4. Conclusions
In order to achieve the goal of this paper (to estimate
the recharge in the study area), GIS was used. Although
GIS was applied, the main disadvantage was the personal
view intervention. To overcome this problem, the data ob-
tained from 3 different methods were used. Although the
degree of personal view intervention in them is different,
the results are almost the same. The percent of their area
was classified into five classes (Table 5). The recharge
coefficient varied from about 30% to 80%. The maximum
area and the mean recharge coefficient are shown in Table
5. In order to estimate the annual recharge, the first coeffi-
cient of recharge was calculated using Equation (1), then
the value of the annual infiltration was estimated. Given
that the average annual precipitation is around 473 mm,
the values for the recharged water (W) for these methods
according to equation 1 are as follows:
Expert judgment: the average coefficient of recharge
was 0.48, and the value for the infiltrate was about 2279
MCM.
Affecting parameters: the average coefficient recharge
was 0.54, and the value for the infiltrate was about 2470
MCM.
AHP: the average coefficient recharge was 0.44, and
the value for the infiltrate was about 2130 MCM.
value of the annual infiltration was estimated. Given that the average annual precipitation
is around 473 mm, the values for the recharged water (W) for these methods according to
equation 1 are as follows:
Expert judgment: the average coefficient of recharge was 0.48, and the value for
the infiltrate was about 2279 MCM.
Affecting parameters: the average coefficient recharge was 0.54, and the value for
AHP: the average coefficient recharge was 0.44, and the value for the infiltrate
was about 2130 MCM.
 = 1

11 + 22 + …

 = 
(1)
where A is the area, R is the recharge coefficient, P is the precipitation, and W is the
volume of water recharged into the aquifer.
Table 5. Results obtained for the different methods
value of the annual infiltration was estimated. Given that the average annual precipitation
is around 473 mm, the values for the recharged water (W) for these methods according to
equation 1 are as follows:
Expert judgment: the average coefficient of recharge was 0.48, and the value for
Affecting parameters: the average coefficient recharge was 0.54, and the value for
AHP: the average coefficient recharge was 0.44, and the value for the infiltrate
was about 2130 MCM.
 = 1

11 + 22 + …

 = 
(1)
where A is the area, R is the recharge coefficient, P is the precipitation, and W is the
volume of water recharged into the aquifer.
W (MCM)
Average
recharge
Area (km2
)
Recharge
Method
(1)
where A is the area, R is the recharge coefficient, P is the
precipitation, and W is the volume of water recharged into
the aquifer.
W
(MCM)
Average recharge
coefficient
Area (km2
)
Recharge
percent
Method
2279
0.48
69.72
4732.10
4390.66
198.57
0.026
30%
30-50%
50-65%
65-80%
80%
Judgment
expert
2470
0.54
8.33
2792.74
5444.42
1125.45
21.03
30%
30-50%
50-65%
65-80%
80%
Affecting
parameters
2130
0.44
5.25
7415.18
1760.55
201.53
10.37
30%
30-50%
50-65%
65-80%
80%
AHP
The final infiltrate map was derived by combining the
obtained maps from the methods (Figure 7).
Based on Figure 7, the average water infiltrate was ob-
tained to be 2249 MCM, and the recharge coefficient was
calculated to be 0.50. The maximum coefficient is related
to the areas with many karst features, which is consistent
with the fieldwork evidence (Figure 7).
a) Expert judgment method
b) reciprocal influences of parameters method
c) AHP method
Figure 6. Final recharge map of the study area, obtained by a) Expert judgment method b) reciprocal
influences of parameters method, c) AHP method
4. Conclusions
In order to achieve the goal of this paper (to estimate the recharge in the study
area), GIS was used. Although GIS was applied, the main disadvantage was the personal
view intervention. To overcome this problem, the data obtained from 3 different methods
were used. Although the degree of personal view intervention in them is different, the
results are almost the same. The percent of their area was classified into five classes
(Table 5). The recharge coefficient varied from about 30% to 80%. The maximum area
and the mean recharge coefficient are shown in Table 5. In order to estimate the annual
recharge, the first coefficient of recharge was calculated using Equation (1), then the
Figure 6. Final recharge map of the study area, obtained by a) Expert judgment method b) reciprocal influences of parame-
ters method, c) AHP method

11
b)
c )
Figure 7. Final recharge map for karstic aquifer in west of Iran, obtained by the combination of different
methods, photographs of the high potential groundwater recharge zones area
Based on Figure 7, the average water infiltrate was obtained to be 2249 MCM,
and the recharge coefficient was calculated to be 0.50. The maximum coefficient is
Doline
Sinkholes
Collapse Sinkholes
Doline after by [33]
Collapse Sinkhole
Figure 7. Final recharge map for karstic aquifer in west of Iran, obtained by the combination of different methods, pho-
tographs of the high potential groundwater recharge zones area

12
Author Contributions
Z. Najafi conceived the presented idea and investigated
the analytical methods. G.H. Karami developed the theory
and performed the computations and supervised the find-
ings of this work. Both authors discussed the results and
contributed to the final manuscript.
Conflict of Interest
The authors declare that they have no conflict of inter-
est in the publication of this article.
Funding
This research received no external funding.
Acknowledgments
This research has been supported by prof. Michel Sch-
neider, we wish to express our sincere gratitude and ap-
preciation to him for his enormous help and support.
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14
Onimisi A. Jimoh,
Department of Geology, Federal University Lokoja, Nigeria;
Email: onimisi.jimoh@fulokoja.edu.ng
ARTICLE
Petrography and Geochemical Studies of Basement Rocks around
Zango-Daji and Its Environs, North Central Nigeria
Simon D. Christopher Onimisi A. Jimoh*
Onimisi A. Martins
Department of Geology, Federal University Lokoja, Nigeria
Article history
Received: 1 September 2022
Revised: 16 November 2022
The geology of the Zango-Daji area was investigated petrographically and
geochemically to determine the study area’s rock types and mineralization
potential. The study area is underlain by rocks of the basement complex
characterized by hilly and undulating rocks, which include granitic gneiss,
migmatite gneiss, biotite hornblende granite gneiss, and pegmatites. Obser-
vation from the field shows that the study area is dominantly underlain by
granitic gneiss. The granitic gneiss is dark grey, medium-coarse-grained,
and characterized by weak foliation defined by the alignment of a streak of
light and dark coloured minerals. They are widespread in the area consti-
tuting about 70% of rock types found in the study area. The average modal
percentage of minerals in the rocks from petrographic studies shows that
granitic gneiss had quartz 45%, plagioclase 10%, microcline 20%, horn-
blende 2%, biotite 10%, muscovite 5%, kyanite 8% and other minerals 5%.
Also, the pegmatite of the study area has no evidence of mineralization; it
contains minerals like quartz, feldspars (microcline and orthoclase), and
micas (mostly muscovite). Geochemical analysis of the granitic gneiss of
the study area shows that silica is by far the most abundant with a value
of 53.5%, Na2
O value of 32.5%, Al2
O3, and k2
O of 6.1% and 4.0%, re-
spectively. CaO value of 2.630% accounts for plagioclase feldspar in the
granitic gneiss. The QAP diagram was used to determine the petrogenesis
of the granitic gneiss. The plot shows the parent rock was a monzogranite
with a low percentage of plagioclase in a thin section with a high percent-
age of quartz and alkali feldspar. The pegmatites of the study area are bar-
ren, as confirmed by the XRD result.
Keywords:
Gneiss
Hornblende
Migmatites
Pegmatite
Petrographic
1. Introduction
The Precambrian basement complex of Nigeria lies
within the Pan-African mobile belt, east of the West Af-
rican craton and northwest of the Congo-Gabon craton
(Figure 1). Evidence from the eastern and northern mar-
gins of the West African craton indicates that the Pan-Af-

15
rican belt evolved by plate tectonic processes which
involved the collision of the passive continental margin of
the West-African craton and the active margin of the Phar-
usian belt (Tuareg shield), about 600 Ma [1,2]
. It lies within
the reactivated part of the belt [3]
. The geology of Zango
Daji has been studied to various degrees by some authors,
including [4]
. These authors indicate the major rock groups,
distribution, and structural relationships.
The major rock types in this area are migmatites, granite
gneiss, and biotite gneiss, while there are minor occurrences
of rock types like pegmatites and quartzo-feldspathic veins.
Migmatites are the most comprehensive spread rock type in
the area and form the country rock in which all other rocks
occur. The Nigerian basement complex rock is classified
into four major groups [5]
. His classification was based on
petrological evidence viz; the migmatite complex, the meta-
sedimentary series, the older granites, and the miscellaneous
rock types, including Bauchites and Diorite. Furthermore, the
area comprises basement complex rocks, highly migmatised
gneisses, and granodiorite (biotite hornblende granodiorite,
biotite hornblende granite, porphyritic biotite granite, and
muscovite biotite granite) intruded by the NE-SW trending
pegmatite dykes and covered by medium-grained alluvium
sediments [6]
.
The study area is a basement terrain; the metamorphic
rock unit includes granitic gneiss, migmatite gneiss, bi-
otite hornblende granite gneiss, and pegmatite intrusions
(Figure 2).
Figure 1. Geological map of Nigeria highlighting the
basement complex [7]
2. Geological Settings
The migmatite gneisses are highly foliated and generally
occur as low-lying outcrops, impregnated by quarto-felds-
pathic and pegmatitic veins; the granitic gneisses in the area
occur as massive plutons affected by intense weathering with
a relict product as quartz ridge, their gneissic textures are
more exposed around river channels and are largely intrud-
ed by pegmatites. Around the western part are pegmatites
intruding massively towards the south and traceable to the
northern region (Figure 2). Some of the rocks exhibit jointing
and show strong mineral lineation and foliation. The megas-
copic minerals observed on these outcrops include feldspar,
quartz, and biotite.
3. Methodology
The major research methods used in carrying out this
research are geological field mapping, petrographic and
geochemical analysis.
The first objective was to carry out a detailed geological
mapping of the study area. This was done using the Global
Positioning System (GPS) to locate outcrops and other ge-
ological features and subsequently followed closely spaced
traverses across rivers and footpaths. Field mapping was
carried out using a topographic map on a scale of 1:25,000.
Representative rock samples of the rock types encountered in
the study area were obtained using a sledgehammer.
Analyses of rock samples of the study area were done
to determine the mineral types and their comparative pro-
portions in the various rocks through thin sections. Major
element geochemical analyses of the samples were carried
out using XRF.

16
3.1 Thin Section Analysis
In the preparation of the thin section, the samples were
cut into slabs of thin thickness, and the surfaces were
smoothened with carborundum 70 and 90 grit to remove
saw marks from the surfaces after thorough washing.
Then, mounted on glass slides with thin araldite and
pressed together (using forceps) to remove air bubbles.
Furthermore, the mounted samples were allowed to cool
to room temperature before reduction to about 3 mm ~ 4
mm on a lapping wheel. The final reduction and thinning
of the samples involved grinding and smoothening with
carborundum 600 and 800 grits, respectively. The thinning
was done with occasional observation under the petro-
logical microscope, and the covering of the glass slides
followed this stage with a thin glass slip to preserve the
surface. Excess araldite on the glass slides was washed
off with acetone and soap solution. The slides were then
rinsed with distilled water using a camel hair brush. The
slides were labelled accordingly and ready for detailed
study under a petrographic microscope.
3.2 X-ray Fluorescence (XRF) Analysis
Selected rock samples from the study area were sub-
jected to X-ray fluorescence spectroscopy using a Rigaku
RIX 3000. Qualitative and quantitative analyses were con-
ducted to determine powdered samples’ major and minor
oxides. The powdered samples were used to make fusion
beads to analyze most major and minor elements.
In preparation for the geochemical analysis, the sam-
ples were first pulverized (ground to a fine powder) using
the Agate mortar. The ground samples were ensured to
pass through the 150 micro mesh sieves to ensure homo-
geneity. Afterward, 5 gm of each pulverized sample was
weighed into a beaker for palletisation. 1 gm of binding
acid was then added to the solution.
The mixture was thoroughly mixed to ensure homo-
geneity and was pressed under high pressure to produce
pellets. This was then labelled and packaged for analysis.
For the X-Ray fluorescence (XRF) analysis, the pellet was
carefully placed in the representative measuring position
on the sample changer of the machine. A given periodic
table guided the selection of filters for elemental analysis.
4.1 Field Description and Petrographic Studies of
the Rocks
The area under study is generally underlain by gran-
ite gneiss, migmatite gneiss, biotite hornblende granite
gneiss, and pegmatites predominant in the Zango area.
Thin sections made from the rock representative sam-
ples were carefully studied using the polarizing micro-
scope. The minerals in the thin section were identified
under both crossed and plane-polarized light. Optical
properties such as colour, twinning, birefringence, pleo-
chroism, and extinction angle were helpful in the identifi-
cation of some minerals.
4.1.1 Migmatite Gneiss
This occurs in the central part extending to the north-
western portion of the study area. Migmatite rocks en-
countered in the study area were grey in colour with large
crystals of orthoclase feldspar, which are pinkish. The
pink masses sandwiched by small dark bands rich in bio-
tite mica consist of alternating pink and dark grey bands.
Foliations are pronounced in most outcrops of the study
area.
Location N 007° 47’ 56’’and E 006° 37’ 28’’ occurs as
low-lying outcrops intruded by quarto-feldspathic veins.
The outcrops are weathered in some areas with evidence
of structural elements such as joints and fractures. Line-
ations and foliations are well developed and preserved in
the rocks of the study area (Figure 3).
Figure 3. Migmatite gneiss outcrop
Feldspar is subhedral and consists of about 39% of the
mineral composition. The hornblende is subhedral and an-
hedral in shape and consists of about 30% of the mineral
composition, while the microcline is subhedral with cross-
hatching and is 20% of the mineral composition. Quartz
consists of 10% of the mineral composition (Figure 4).
4.1.2 Granitic Gneiss
This occurs in the entire southern part of the study area
(Figure 2). The study area is underlain predominantly by
granitic gneiss that is fine to medium-grained, poorly fo-

17
liated with bands of light and dark coloured minerals as
evident in (Figure 5) location N 007° 47’ 56’’ and E 006°
37’ 28’’. Quartz and quartzo-feldspathic veins are running
concordantly and crosscutting the rocks. The minerals in
the thin section generally show low relief; visible minerals
are quartz, feldspars, biotite, kyanite, muscovite, horn-
blende, and other accessory minerals. Most of the crystals
show a subhedral-anhedral form. The quartz mineral is
colourless under plane polarised light and shows no pleo-
chroism; it has a first-order birefringence with extinction
angles at 30° and 80°. Biotite shows brown colouration
with subhedral–anhedral form. The crystals of feldspar
appear colourless in plane polarised light (PPL) and grey
under cross polarised light (XPL); they include plagi-
oclase and microcline, tartan and lamella twinning was
a diagnostic feature used in differentiating the feldspars,
with the microcline displaying a cross-hatch twinning,
and the plagioclase lamellar twinning. Quartz and feld-
spar constitute about 70% of the thin section. Quartz is
the most abundant mineral in the slide, indicating that the
rock is a product of acidic magma crystallization.
The minerals in the thin section generally show low
relief with visible minerals like quartz, feldspars, bio-
tite, kyanite, muscovite, hornblende, and other accessory
minerals. Most of the crystals show a subhedral-anhedral
form. Quartz mineral is colourless under plane polarised
light and shows no pleochroism; it has a first-order bire-
fringence with extinction angles at 30° and 80°. Biotite
shows brown colouration with subhedral – anhedral form.
The crystals of feldspar appear colourless in PPL and
grey under XPL (Figures 6 and 7), including Plagioclase,
Microcline, and tartan, and lamella twinning was a diag-
nostic feature used in differentiating the feldspars, with
the microcline displaying a cross-hatch twinning and the
plagioclase lamellar twinning. Quartz and feldspar consti-
tute about 70% of the thin section; Quartz is the dominant
mineral in the slide and indicates that the rock is a product
of acidic magma crystallization
Figure 5. Granitic gneiss outcrop
Figure 6. Photomicrograph of granitic gneiss (viewed
under plane polarised light)
H= Hornblende, B= Biotite, K=Kyanite, QTZ= Quartz, MI=
Microcline
Figure 4. Photomicrograph of migmatite gneiss under
XPL Magnification= X10
H=Hornblende, F= Feldspar, MI= Microcline and Qtz= Quartz.

18
Figure 7. Photomicrograph of granitic gneiss (viewed
under crossed polarised light).
Qtz= quartz, Pl= Plagioclase, MI= Microcline, B= Biotite
4.1.3 Biotite Hornblende Granite Gneiss
This rock type occurs in the study area’s extreme north-
east (Figure 2). They form a minor part of the granitic
gneiss terrain at location N 007° 48’ 27.7’’ and E 006° 38’
35.3’’. They occur as grey-black, medium–grained batho-
liths intimately mixed with the granitic gneiss. The pe-
trography of the gneiss is variable, particularly concerning
quantities of individual minerals, but the rock is character-
ized by biotite and hornblende (Figure 8).
Figure 8. Biotite hornblende granite gneiss outcrop
They are primarily mesocratic, coarse-grained, and
equigranular, exhibiting interlocking textures, and sub-
hedral to anhedral grains. Oligoclase is colourless and
cloudy; hornblende is green and pleochroic from light
green to dark green. From the thin section, biotite, horn-
blende, quartz, plagioclase, and feldspar are present, as
shown in Figure 9. The plagioclase is subhedral to an
anhedral shape, while the quartz appears to be anhedral in
form. Also, the biotite is anhedral, and hornblende is sub-
hedral to anhedral in shape.
Pl
F
Qtz
B
H
Figure 9. Photomicrograph of biotite hornblende granite
gneiss under XP L. Magnification X10
H=Hornblende, B= Biotite, Pl= Plagioclase, F= feldspar and
Qtz= Quartz
4.1.4 Pegmatites
Pegmatites were found throughout the study area, most
intruding on the surrounding rocks. They are more con-
centrated at the base of the granitic gneisses and are foliat-
ed in the migmatite gneiss (Figure 10). Field observations
show that pegmatite is associated with other rock types,
such as migmatite gneiss and granite gneiss. It is evident
from the geological map (Figure 2) that pegmatite, which
intrudes into the gneiss, is the predominant rock type in
the study area. Pegmatite in the study area trends in the
NE-SW direction and are found in different forms as vein,
irregular bodies, and sometimes as a cross-cutting dis-
cordant dyke. Pegmatite existing as vein and dyke is very
common in other rock units such as granite gneiss and
migmatite gneiss of the study area.
The relationships of pegmatite with the host rock are
cross-cutting, oblique, and sometimes concordant to fo-
liations of the general trend. These pegmatite dykes and
veins range from a few centimetres to tens of metres. In
some cases, there are abrupt terminations. The pegmatites
within the mining site at Zango have been extensively
and deeply weathered, revealing the high resistance of
fractured quarzitic bodies. The quartzites are of different

19
varieties, with common ones that include transparent rock
crystals, milky white quartz, and smoky quartz, which
could be irregular in shape, while some assume hexagonal
crystal shapes. The topography of the pegmatite mining
site is composed of the sloppy highland, deeply weathered
pegmatite, and flat-lying unweathered pegmatite outcrop-
ping discontinuously within the mining site.
Figure 10. Pegmatite in migmatite gneiss (N 07° 47’
43.8’’ and E 06° 37’ 25.5’’)
The petrographic study of pegmatite in thin sections
(Figure 11) shows the predominant minerals are quartz,
microcline, muscovite, plagioclase, biotite, and accessory
minerals in varying compositions, with sizes ranging from
veinlet of about a few kilometres to a few millimetres in
width. Muscovite is the more abundant mica occurring in
the pegmatite of the study area, while biotite is very few.
Minerals in pegmatite have large crystals identifiable and
recognizable in hand specimens.
F
Qtz
M
Figure 11. Photomicrograph of pegmatite under XPL.
Magnification x10
F= feldspar, Qtz= Quartz and M= Muscovite
The pegmatite observed in the study area is barren,
confirmed by the XRD result presented in Figure 12. Bar-
ren pegmatite has no evidence of mineralization; it con-
tains minerals such as quartz, feldspars (microcline and
orthoclase), and micas (mostly muscovite). The Muscovite
present here is compacted into dark colouration.
X-Ray Diffractogram (XRD) analysis (Figure 13) of
the pegmatite reveals an average mineralogical compo-
sition of quartz (36%), albite (18%), orthoclase (29%),
chlorite (3.6%), illite (8%) and garnet (4.7 %). Quartz,
Figure 12. XRD of Zango pegmatite

20
orthoclase, and albite are the dominant minerals in the
pegmatite of the study area. This result shows that the
pegmatites in this area are simple (barren) pegmatites.
4.2 Major Element Geochemistry
Results of geochemical analysis of the granitic gneiss
from the study area, as presented in Table 1, show that
silica is by far the most abundant with values in mole
percentage of 53.5%, Na2O values of 32.5%, Al2O3 and
k2O of 6.1% and 4.0% respectively. CaO value of 2.630,
which accounts for the plagioclase feldspar in the granitic
gneiss, FeO3 and MnO values are generally low with val-
ues of 0.806% and 0.045%, respectively, while TiO2 has a
value of 0.225%.
Table 1. XRF result of major oxides
S/N OXIDES MOL%
1 SiO2 53.473
2 F2O3 0.806
3 Na2O 32.525
4 Al2O3 6.111
5 P2O5 0.000
6 MnO 0.045
7 K2O 4.092
8 MgO 0.000
9 CaO 2.630
10 TiO2 0.225
Figure 13. Mineralogical Composition of Zango pegmatite determined from Identified Peaks of the X-Ray Diffractogram
Figure 14. QAP diagram classifying the granitic gneiss of the study area.

21
4.3 Protholith and Petrogenesis
The protolith and petrogenesis of the granitic gneiss
in the study area were derived using the QAP diagram;
the QAP plot is particularly useful in classifying intrusive
rocks. The granitic gneiss plotted in the Monzogranite
field (Figure 11) indicates that the parent rock is Mon-
zogranite.
5. Conclusions
This research discussed the petrography and geochem-
istry of basement rocks of Zango-Daji and its environs,
a typical example of the Precambrian basement com-
plex of Nigeria. It is underlain by the basement complex
rocks characterized by hilly and undulating rocks such
as granitic gneiss, migmatite gneiss, biotite hornblende
granite gneiss, and pegmatite. The migmatite gneiss is
highly foliated and occurs as low-lying outcrops; granitic
gneisses are widespread in the study area and occur as
massive, rugged hills heavily impregnated with pegmatite
intrusions. Pegmatite occurs as outcrops and intrusions in
the area with minerals such as quartz, feldspar, micas, and
chlorite.
The granitic gneiss is a metamorphosed granite rock
that displays light and dark minerals banding. It is com-
posed of mafic minerals such as biotite and hornblende;
felsic minerals include quartz feldspars (microcline) and
muscovite mica. The average modal percentage of min-
erals in the rocks from petrographic studies shows that
granitic gneiss contains; quartz 45%, plagioclase 10%,
microcline 20%, hornblende 2%, biotite 10% muscovite
5%, kyanite 8%, and other minerals 5%. Pegmatite of the
study area has no evidence of mineralization; it contains
quartz, feldspars (microcline and orthoclase), and micas
(mostly muscovite).
The QAP diagram was used to plot the modal percent-
ages of minerals in the granitic gneiss rock sample. The
plot shows the possible parent rock was a monzogranite,
with a low percentage of plagioclase in the thin section
and a high percentage of quartz and alkali feldspar. This
research seeks to contribute knowledge on the geology of
the area by geochemical and mineralogical observation of
rocks around the area.
Acknowledgment
The authors are grateful to the Geology department,
Federal University Lokoja, for providing a petrograph-
ic microscope to analyze the samples to determine the
mineral types and their relative proportions in the rocks
through the thin section.
Conflict of Interest
There is no conflict of interest.
References
[1] Black, R., Caby, R., Moussine-Pouchkine, A., et al.,
1979. Evidence for late Precambrian Plate tectonics
in West Africa. Nature. 278(5701), 223-227.
[2] Burkey, K.C., Dewey, J.F., 1972. Orogeny in Africa.
Dessavagie, T.F.J. and Whiteman, A.J. (Eds) African
Geology, Ibadan: Ibadan University Press. 583-608.
[3] Rahaman, M.A., 1976. Review of the Basement Ge-
ology of South Western Nigeria. Kogbe, C. A. (ed)
Geology of Nigeria. Elizabethan Pub. Co. Lagos. 41-
58.
[4] Imasuen, O.I., Onyeobi, T.U.S., 2013. Chemical
Compositions of Soils in Parts of Edo State, South-
west Nigeria and their Relationship to Soil Produc-
tivity. Journal of Applied Sciences and Environmen-
tal. 17(3), 379-386.
[5] Oyawoye, M.O., 1964. The geology of the Nigerian
Basement Complex – A survey of our present Knowl-
edge of them. Journal of Nigeria Mining, Geology
and Metallurgical Society. 1, 87-103.
[6] Ozulu, G.U., Okoro, A.U., Ndubueze, V.O., 2019.
Petrographic and Geochemical interpretation of
source provenance and tectonic setting of the Lokoja
Sandstone, Bida Basin, Nigeria.
[7] Obaje, N.G., 2009. Geology and Mineral Resources
of Nigeria. Keffi, Nigeria: Springer.

22
Osisanya Olajuwon Wasiu,
Department of Physics, University of Benin, Benin, Nigeria;
Email: wasiu.osisanya@uniben.edu
ARTICLE
Aeromagnetic Interpretation of Basement Structures and Geometry
in Parts of the Middle Benue Trough, North Central, Nigeria
Esho Oluwaseyi Olatubosun1
Osisanya Olajuwon Wasiu2*
Ibitoye Taiwo Abel3
Ajibade
Femi Zephaniah3
Tokunbo Sanmi Fagbemigun4
1. Department of Geology, University of Ibadan, Ibadan, Nigeria
2. Department of Physics, University of Benin, Benin, Nigeria
3. Department of Petroleum Engineering and Geosciences, Petroleum Training Institute, Effurun, Nigeria
4. Department of Geophysics, Federal University, Oye-Ekiti, Nigeria
Article history
Received: 8 October 2022
Revised: 9 November 2022
The research of an analysis of aeromagnetic data collected in the middle
Benue Trough in north-central Nigeria is presented. A detailed analysis
of basement structures is conducted in order to identify regions with high
hydrocarbon potential that is different from those discovered by earlier
researchers. Aeromagnetic data were filtered by using the Butterworth
and Gaussian filters, transformed by engaging the reduction to the equator
technique, and subsequently enhanced. To estimate magnetic basement
depths at various places throughout the basin, the Euler deconvolution
depth weighting approach was used. Eleven (11) sub-basins with depths
ranging from –2000 m to –8000 m were also identified by Euler’s findings.
The sub-basins trend in the NE-SW direction while the average sediment
thickness is found to be more than 3 km. The extracted structural features
indicate areas like Kadi Blam and Kado areas in the southeastern part and
Ogoja and Obudu in the southern part of the study area as regions with high
structural densities. These areas coincide with the areas delineated as the
sub-basins. The cross-sections generated reveal depressions caused by the
action of some tectonic activities in the area. This study identified undulat-
ing basement topography believed to be due to tectonic activities as well as
five areas that are possible targets for hydrocarbon exploration.
Keywords:
Aeromagnetic data
Euler deconvolution
Basement structures
Hydrocarbon potentials
Middle Benue Trough
1. Introduction
Petroleum (oil and gas) has been discovered to be a ma-
jor source of the nation’s revenue generation since its dis-
covery in large quantities in 1956. Abubakar [1]
discovered
that at least two potential petroleum system may be found
in Nigeria Benue trough; namely (i) the lower cretaceous
petroleum system which is capable of producing oil and

23
gas (ii) the upper cretaceous petroleum system which was
inferred to generate mainly gas which made them very
reliable in conducting future exploration research in the
basin. The Benue Trough is an inland coal-bearing region
with extensive hydrocarbon exploration. Despite geosci-
entists’ tireless work over the last two decades to maxi-
mize the middle Benue Trough’s hydrocarbon potential,
there is still more to be found. This study intends to carry
out a detailed interpretation of basement structures in
parts of the middle Benue Trough in an attempt to discov-
er areas of high hydrocarbon potentials different from the
ones that have been discovered by earlier researchers. The
aim is to assess the possible occurrence of fundamental
hydrocarbon potential parameters (such as reservoir thick-
ness) that could serve as a guide to further research and
subsequent exploratory work in the basin. The aeromag-
netic survey is an effective method for determining the
regional geology (lithology and structure) of the buried
basement area. When the geology of the examined area is
well understood, the precise aeromagnetic map has proven
to be quite useful [2]
. Moreover, aeromagnetic surveys help
to investigate the depth of magnetic basement rocks in the
sedimentary basin. Major basement features are identified
using an aeromagnetic survey, revealing promising explo-
ration areas that can be further investigated using the more
expensive but more concise and specific seismic approach
of geophysical investigation. Intensive geophysical re-
search has been conducted in various regions of the Benue
Trough for quite some time. Falconer [3]
was the first to
report on work done in Nigeria’s middle Benue Trough
and also recently by Kasidi and Ndatuwong [4]
. He wrote
on the geology of the basement and the Chad basin near-
by. According to him, the Asu River Group contains the
middle Benue’s earliest sediments. He later coined the term
“Lower Shale” to describe this group. Cratchley and Jones [5]
were the first to conduct a major geophysical survey in
the Benue depression and also reported by Abubakar [1]
.
The majority of the articles published on the subject were
regional. Many geologists, however, have mapped the
area [6]
. Ehinola [7]
examined the middle Cretaceous black
shales for hydrocarbon source potential, thermal maturity,
and depositional environment using organic facies fea-
tures. He also conducted detailed geological mapping and
geochemical analyses of the Abakaliki anticlinorium’s oil
shale deposit to establish its extent, resource estimation,
recovery strategies, and potential environmental conse-
quences. Obi et al. [8]
investigated the effects of subsurface
intrusive on hydrocarbon appraisal in the Lower Benue
Trough using aeromagnetic modeling. In the locations
near Nkalagu, Abakaliki, IkotEkpene, and Uwet, they
discovered 12 intrusive bodies with sediment thicknesses
ranging from 1.0 km to 4.0 km. He concluded that these
intrusives have adequate sediment thickness (more than 2
km) to generate hydrocarbons. To identify probable petro-
leum systems in the Nigerian Benue Trough and Anambra
Basin, Abubakar [1]
conducted an assessment of the geol-
ogy and petroleum potentials of the basins. He discovered
that the basins could have at least two potential petroleum
systems: the Lower Cretaceous petroleum system, which
could generate both oil and gas, and the Upper Cretaceous
petroleum system, which could generate mostly gas. He
observed that the systems are similar in temporal disposi-
tion, architecture, sources, and reservoir rocks, and maybe
generation mechanism to those found in Sudan’s Muglad
Basin and Niger and Chad Republics’ Termit Basin. Lik-
kason et al. [9]
conducted a study on the Nigerian middle
Benue Trough based on geological applications and anal-
ysis of the aeromagnetic data spectrum. The radial spec-
trum and the field’s matched filtered output are compared,
and the results of the plot of the log radial spectrum ver-
sus the frequency numbers revealed five discernible linear
segments with depths corresponding to magnetic layers
ranging from 20.62 km (highest) to 0.26 km (lowest). Pat-
rick et al. [10]
compiled a stratigraphic report for the mid-
dle Benue Trough in Nigeria, based on petrographic and
structural analysis of the Abuni and environs formations,
which are part of the late Albian–Cenomanian Awe and
Keana formations. Bedding, lamination, huge bedding,
graded bedding, mud cracks, cross-bedding, folds, and
joints were among the formations they discovered on the
field. The principal structural tendencies are directed in
the following directions: NE-SW, NNW-SSE, WNW-ESE,
and NW-SE. Hornblende, plagioclase feldspar, olivine,
and accessory minerals, which include opaque minerals
and are thought to be iron oxides due to the high concen-
tration of iron in almost all of the samples, are among the
mineral suites identified from the thin slice of the volcanic.
1.1 Location of the Study Area
The study region is located in Nigeria’s middle Benue
Trough, between latitudes 07°00’N and 09°00’N and lon-
gitudes 08°00’E and 10°30’E covering about 39027 km2
. It
covers Markurdi, Tanka, Logo and Gboko towns in Benue
State. The study area also includes the Wakuri and Donga
areas of Taraba State. The study area also includes Nassar-
awa State’s Lafia, Doma, and Awe regions (Figure 1). Bali
is to the east, Apa is to the west, Bokkos is to the north, and
Vandeky is to the south. The Gboko road in the southern sec-
tion provides access to the research area. Other minor roads
link the smaller interior villages from the major road. The
major roads are tarred while the minor roads are best graded
and may not be accessible at the peak of the rainy season.

24
1.2 Drainage
It is traversed by the Benue River, which is the largest
in the study area (Figure 1). It comes from the Adamawa
Plateau in northern Cameroon. It enters Nigeria from the
south, passing through Garoua and the Lagdo Reservoir
on its way to the Mandara Mountains. It then travels
through Jimeta, Ibi, and Markurdi before arriving in
Makurdi on the Niger. The NE-SW lineaments appear to
direct its path. These rivers have a significant flow differ-
ence between peak flow (usually at the end of the rainy
season) and ebb flow (at the end of the dry season), when
they are reduced to a trickle. Their banks provide in the
dry season, very good exposures of the shale units other-
wise hidden in other locations. Other smaller streams are
also controlled by the ridge and swale topography giving
a rough trellis drainage pattern.
1.3 Climate and Vegetation
The study area has a warm tropical climate with rela-
tively high temperatures (27 °C on average) all year and
two seasons: the rainy or wet season, which runs from
March to November in the south and May to October
in the north, and the dry season, which runs the rest of
the year. The rainy season is divided into two periods of
high rainfall, separated by a brief time in August that is
comparatively dry (the August break). The study area has
evolved Guinea savannah with a relict forest as its vege-
tation. Originally, this region of the high woodland was
the drier part. Most of the high forest trees were destroyed
due to bush burning and overgrazing, cultivation, and
hunting activities in the area over a lengthy period, and
the forest was replaced with a mixture of grasses and dis-
persed trees.
1.4 Geologic and Tectonic Evolution of the Benue
Trough
The Benue Trough is one of Africa’s most significant
rift structures, and it is thought to have been produced
during the Cretaceous by rifting of the central West Afri-
can basement. The circumstances that led to the develop-
ment of the Benue Trough and its constituent subdivisions
have been thoroughly chronicled [11-17]
. The Nigerian
Benue Trough (Figure 1) is an intracontinental basin that
runs from north to south in Central Africa.
It has a length of over 1000 kilometers and a breadth
of over 150 kilometers Its southern outcropping limit is
the northern boundary of the Niger Delta Basin, while it’s
northern outcropping limit is the southern boundary of the
Chad Basin, which is separated from the Benue Trough
by the “Dumbulwa-Bage High”, an anticlinal structure [18]
.
The Benue Trough has up to 6 kilometers of Cretaceous
deposits, some of which are volcanic. It’s part of the West
and Central Africa Rift System, a mega-rift system that
spans the continent (WCARS). The WCARS includes
Niger’s Termit Basin and western Chad, southern Chad’s
Bongor, Doba, and Doseo Basins, the Central African Re-
public’s Salamat Basin, and Sudan’s Muglad Basin.
Figure 1. Geology map of Benue Trough

25
1.5 The Middle Benue Trough
The Middle Benue Trough stretches northeastward as
far as the Bashar-MutumBiyu border.
The Gombe and Keri-Keri Formations reach their
southern limit at this point, while the Upper Benue
Trough’s earlier sediments undergo lateral facies shift.
1.5.1 The Structure of the Middle Benue Trough
The middle Benue Trough’s axial basement high [19]
,
(Keana ridge) coincides with the Keana anticline, which
runs NE-SW. Benkhelil [13,20]
used gravity and aeromag-
netic data to locate the high sedimentary sub-basin on ei-
ther side of the basement. A minor “Shendam Basin” and
a more important “Kadarko Basin” with sediment thick-
nesses of 2.4 km to 5.3 km on the south-eastern flank, and
a “Wukari” and a MutumBiyu Basin with inferred sedi-
ment thicknesses of 1.9 km to 3.8 km on the north-west-
ern flank. The Cretaceous era produced the middle Benue
Trough (Figure 1).
1.5.2 The Stratigraphy of the Middle Benue Trough
The middle Benue Trough encompasses the research
area. Several scholars have written about the geologic suc-
cession in the middle Benue Trough [5,6,21-23]
. The middle
Benue Trough is split into six (6) formations, according to
these scholars.
1.5.3 Hydrocarbon Potential of the Middle Benue
Trough
The Central Benue Trough is a valley in central Benue.
The shales and limestones of the marine AlbianAsu River
Group (Gboko, Uomba, and Arufu Formations) could be
potential source rocks, the sandstones of the Cenomanian
Keana and Awe Formations could be potential reservoirs,
and the shales of the basal Ezeaku Formation could act
as a regional seal in the possible Lower Cretaceous Pe-
troleum System in the Central Benue Trough [24]
. Because
organic geochemical data on the potential petroleum
source rock (Asu River Group) for this system is scarce
to non-existent, the author was unable to obtain any raw
data on organic matter quantity or quality. Obaje et al. [24]
suggested that values over 1.25 percent to indicate late
gas window stage to over maturity on maturity. The Asu
River Group could be up to 1800 meters thick on average [24]
.
Flaggy medium coarse-grained calcareous sandstones and
fluvial-deltaic cross-bedded coarse-grained feldspathic
sandstones, respectively, are prospective reservoir rocks in
the Awe and Keana Formations. In certain locations, the
Awe Formation can be 100 meters thick. These formations
are particularly important water aquifers surrounding Kea-
na and Awe, despite the lack of reservoir quality data.
2. Materials and Methods
2.1 Data Acquisition and Instrumentation
Fugro Airborne Surveys Limited gathered aeromagnetic
data on behalf of the Nigerian Geological Survey Agency
as part of a nationwide high-resolution airborne geophys-
ical survey aimed at supporting and promoting mineral
exploitation in Nigeria (NGSA). The data were gathered
methodically by dividing the country into segments
(blocks) with different measurement parameters for each
block, with the result being the creation of an aeromagnet-
ic map of the entire country. Three Scintrex CS-3 Cesium
Vapour Magnetometers were utilized to collect data dur-
ing the survey. The data were obtained at a nominal flying
altitude of 152.4 meters along two N-S flight lines that
were about 2 kilometers apart. The magnetic data were
compiled into 12-degree aeromagnetic maps with a scale
of 1:100,000. For simple reference and identification, the
maps were numbered, and place names and coordinates
(longitude and latitude) were written. Before plotting the
contour map, the real magnetic data were scaled down by
25,000 gamma. All of the maps have an epoch date of Jan-
uary 1, 1974, and a correction based on the International
Geomagnetic Reference Field (IGRF). The National Geo-
graphic Society ofAmerica (NGSA) published a report in
1974 on the subject of Fixed-wing (Cessna) aircraft also
took part in the survey, which covered a total of 235,000
line kilometers with a flight spacing of 200 meters and
terrain clearance of 80 meters. The flight was headed NW-
SE, with a 200-meter tie-line spacing and a NE-SW tie-
line orientation. The flight line and tie-line trends were
135° and 45°, respectively, and the magnetic data record-
ing interval was 0.1 seconds. Within UTM Zone 36S and
using the Clark 1880/Arc 1960 coordinate system, a grid
mesh of 50 meters was used in the World Geodetic Sys-
tem of 1984 (WGS84). The followings are the sources of
information used in this study:
i) Aeromagnetic grid covering sheets 209-213, 230-
234, 250-254, and 270-273 making a total of 19 sheets.
(ii) Oasis Montaj Software;
(iii) Surfer 13 Software;
(iv) Geological map of Nigeria (soft copy) (iv) Linea-
ment map of Nigeria (soft copy).
2.2 Data Processing
In a defined coordinate system, digitized data were
gridded into an evenly spaced lattice. Interpolating data
taken along with parallel profiles but at random places

26
along the profiles was done using the minimal curvature
gridding approach. Using Briggs’ method, this method fits
minimal curvature curves to the data point (which is the
smoothest possible surface that would suit the supplied
data values) [25]
. Because the data collected in the field are
a mix of signal and noise, it must be processed to remove
the undesirable information that could lead to erroneous
subsurface interpretation. Data processing’s overall goal
is to reduce noise and improve the signal-to-noise ratio.
To achieve this purpose and obtain a refined dataset, the
following filters were used:
2.2.1 Combined Gaussian and Butterworth
These two filters were used to reduce the impacts of
the regional anomaly on magnetic bodies (a model of the
earth’s core field based on the I.G.R.F. epoch date of data
acquisition: 1st January 1974) [26]
. Following the I.G.R.F
reduction technique, the Butterworth filter, which is a low-
pass filter, was used. This was used to remove regional
impacts from total magnetic intensity data by removing all
frequencies over the cut-off frequency and leaving those
below unaltered. The fitting method generates a surface
(called the regional field) that has the best fit to the mag-
netic field.
2.2.2 Data Transform
Because the study area is at a low magnetic latitude,
and the Earth’s field intensity decreases from the poles to
the equator, peaks of magnetic anomalies are likely to be
incorrectly or improperly positioned over their sources, as
well as skewed along a particular direction, usually visible
as abnormal elongation of anomalies along the E-W direc-
tion, this filter was used in the Fourier domain to migrate
the observed field from the observed magnetic inclination
and the observed magnetic inclination. The study site has
a low magnetic equator of about 150, which can make
anomaly interpretation difficult if not corrected. The peaks
of magnetic anomalies are centered over their sources in
this operation.
Any asymmetry in the reduced-to-equator field can
then be attributed to source geometry and/or magnetic
properties, which helps with interpretation. With a dec-
lination of –3.50, an inclination of –14.70, and a field
strength of 31533.7 nT, the data were reduced to the mag-
netic equator. Reduction to the magnetic equator filter was
applied to the magnetic intensity data to center the peaks
of the magnetic anomalies over their geologic sources by
recalculating the total magnetic intensity data as if the
inducing magnetic field had a 900 inclination and trans-
forms dipolar magnetic anomalies to monopolar anoma-
lies centered over their causative bodies, simplifying the
resultant aeromagnetic map, which bears a simple and
direct relationship with t [27]
.
2.2.3 Data Enhancement
Horizontal Derivative: To refine the edges of mag-
netic anomalies and better determine their positions,
derivative filtering techniques are applied [28]
. This filter
was employed to increase deep-seated abnormalities to
see a sharper picture of them, thereby isolating magnetic
anomalies near the surface. This filter was used to con-
struct two maps, one in the x-direction and the other in
the y-direction. As a result, high-frequency variations in
potential field data are amplified. Faults and/or geological
unit borders could be the source of such fluctuations [29]
.
This technique employed an order of differentiation of 1
because higher values would result in noise amplification.
This filter also decreases the intricacy of anomalies, al-
lowing for better imaging of the causal structures. The up-
ward continuation filter was employed to investigate the
regional propagation of the inherent anomalies. At 2 km
altitudes, the anomalies’ trending trends were compared.
The analytic signal was gathered to create an analytic sig-
nal map of the study area, which is normally determined
by a combination of horizontal and vertical gradients of
the magnetic anomaly [30]
. The resulting map delineated
the forms and boundaries of the geologic sources that
caused the anomalies.
As a result, the magnetization map was utilized to pin-
point the magnetic bodies (as well as their boundaries)
that were responsible for the observed magnetic anoma-
lies as shown on the magnetic intensity map [30]
. To map
the shallow basement structures and mineral exploration
sites, the tilt derivative was applied [31]
. The tilt angle’s
amplitude is positive over magnetic sources, crosses
through zero at the source’s near edge, and is negative
outside of the source. The signatures of the linear struc-
tures were used to identify them. The structures that were
continuous and those were not were identified by a com-
prehensive examination of the structures. The orientations
of the mapped lineaments were also detected by this filter.
Where VDR and THDR are the first vertical and total
horizontal derivatives of the total magnetic intensity T,
respectively. In the initial stage of this process, the stand-
ard deviation approach was applied. The method creates a
smoother representation of the degree of unpredictability,
which eliminates the data’s intrinsic noise.
1
TDR tan
VDR
THDR
−
= (1)
as proposed by Verduzco et al. [31]
, where VDR and THDR
are first vertical and total horizontal derivatives, respec-

27
tively, of the total magnetic intensity T.
The standard deviation method was used in the first
phase of this process. The method provides a smoother
representation of the degree of randomness that over-
comes the inherent noise in the data.
(2)
where σ is the standard deviation.
The smallest wavelength filter employed was 5, the ro-
bustness was 3, and the orientation was all-encompassing,
with a total of five filter scales. The resulting map depicts
the lineaments’ orientation. The structural complexity map
was created to help discover locations with a high density
of junctions. It also uses the standard deviation principle
to create a database with straight line segments. The local-
ized junctions were chosen using a contact voting influ-
ence of 3 and the orientation entropy was then calculated
for all directions.
The vectorized standard deviation grid was used as the
input grid to create this map. The proximity was set to be
within 5 cells, the angle of deviation was set to be greater
than 300, the contact voting influence radius was reduced
to 3 (to pick localized junctions), the window size was
20 cells, and the entropy bins were set to 6. The obtained
map from the Euler deconvolution plot was used to identi-
fy the sub-basins in the study area.
Total magnetic field measured at a point (x, y, and z)
due to a point/line source located at x0, y0, z0 can be ex-
pressed as
(x −x0)dF/dx +(y −y0)dF/dy+(z −z0)dF/dz= N(B –F) （3)
as proposed by Milligan et al. [32]
, N is Euler’s structural
index, and B is the whole field’s regional value. It’s an
exponential factor that represents the pace at which a
source’s field diminishes with distance for a certain geom-
etry. N is the source geometry’s prior information input.
For geologic contact, N = 0.0, 1.0 for dike, 2.0 for hori-
zontal or vertical cylinder, and 3.0 for magnetic sphere.
The Euler deconvolution algorithm was used in this study
to determine the location and depth of causative anoma-
lous bodies from gridded aeromagnetic data using Oasis
Montaj TM. The resulting map depicts the locations of
geologic sources as well as their estimated depths.
In addition, profiles were used to determine the depth
to the magnetic basement using Extended Euler deconvo-
lution. This is consistent with Reid et al.’s findings [33]
. The
Windowing Technique was used to refine the resulting
answers and decrease uncertainty to the bare minimum.
This was achieved by constraining the obtained Euler
deconvolution solutions to accept a maximum depth lim-
it of 10000 m, maximum % depth of tolerance of 10%,
thus depth uncertainty (dz in %) greater than 10% were
rejected. Similarly, horizontal uncertainty (dx in %) was
set to 20%. This assigns masks to solutions with outcomes
outside the chosen window [34]
. To identify the depth of
the magnetic source, processed aeromagnetic data was
applied to Euler’s deconvolution algorithm [33,35-37]
. Decon-
volution was done using both conventional and Located
Euler techniques. The traditional technique looked at
every grid position and kept only those with good solu-
tions, whereas the localized method calculates the analytic
signal, discovers peaks in the analytic signals, and then
uses the determined locations for Euler deconvolution.
The latter offers a benefit over the usual method in that
solutions are only approximated over anomalies that have
been identified, resulting in more accurate results [30]
.
Because the Euler plot depicts the spatial distribution of
depth to magnetic sources across the entire area, regions
with significant depths were isolated for further investi-
gation. Furthermore, the depths of these isolated regions
ranged from 3 km to 8 km, which aided in the selection
process. This method is preferred over others because of
its unique ability to produce credible results even when
the geological model is incorrectly/inappropriately repre-
sented. It can also generate solutions in areas where there
are no anomalies or at their edges.
2.3 Generation of 2-D and 3-D Models
The final step in properly visualizing the sub-basins
shown by the Euler deconvolution graphic was the crea-
tion of the 3-D model. This was accomplished using the
Surfer 13 program. The Euler deconvolution method gen-
erated over 120,000 solution sets, which were then export-
ed into the Surfer worksheet. Each solution set’s longitude
(easting), latitude (northing), and depth to the top of the
magnetic source (Z) were recorded in the three-column
set utilized in the Surfer worksheet. The data was first
gridded, and then the model was built. Smoothing and
de-peaking techniques were used to improve the model
further A contour map was created using Surfer 13 soft-
ware and the above-mentioned gridded data collection.
A 2-D profile was generated for each of the suspected
sub-basins by drawing four profile lines across them. The
geography of the magnetic basement was represented
considerably more clearly in each 2-D model, which was
exhibited as a cross-section (Figure 2).

28
The residual map of the study area is shown in Figure
3. The study area’s magnetic intensity distribution has val-
ues ranging from –68.597 to 132.362 nT. On the residual
magnetic intensity map, prominent high amplitude mag-
netic intensities can be seen, which mostly trend in the
NE-SW and NNE-SSW directions. The high amplitude
magnetic strengths can be seen in the Benue State areas of
Katsina-Ala and Zoki Bam, Taraba State’s Donga, Ibi, and
Banlaji, Nassarawa State’s Obi, and Cross River State’s
Ogoja. The dominant trends that have been observed in
these areas are NE-SW. Magnetic anomalies with a high
positive amplitude correspond to locations with a high
magnetic mineral composition, such as magnetite. Since
the area under study is in a basin, volcanic intrusions
could give a high response of magnetic intensity. High
negative amplitude magnetic anomalies also character-
ized Tokum and Wukari areas of Taraba State, BojuEga,
Bokem, and Alade areas of Benue State as well as some
parts around the western part of the study area.
The RTE map of the study area is shown in Figure 4.
The graphic depicts the elongation effects imposed on
anomalies near the equator, as well as the correction in
the distribution of the anomalies to locate them over their
sources and lessen the elongation effects imposed on
anomalies near the equator. The asymmetry in the anom-
alies has been removed, indicating that they have been
appropriately aligned over their causal bodies, as seen by
a comparison of the total magnetic intensity map and the
RTE map. The low-frequency anomaly around the Ogoja
area of Cross River state in Figure 4 became more pro-
nounced after the data had been reduced to the equator in
Figure 5. Also, there is a high demarcation of individual
high amplitude anomalies around the northern part of the
study area which initially looked like a massive linear fea-
ture before this process. Figure 5 shows that the regions
around Oboko and Kado have magnetic intensities in the
range of 125 to 190 nT. The values of these regions when
the data had not been reduced to the magnetic equators
were in the range of –38.3 to 7.4 nT. The anomaly has
been aligned over the causal body thanks to the RTE filter.
Figure 2. Simplified flow diagram of aeromagnetic data processing (Modified after Osinowo, 2013)

29
The magnetic intensity ranges of this region before and
after the application of the RTE filter are –68.6 to 52.0 nT
and 57.0 to 115.7 nT, respectively, at Mahanga, the central
part of the study area. In the two aforementioned cases,
the color changed from blue (low value of magnetic inten-
sity) to pink (high value of magnetic intensity) as can be
seen on the maps (Figures 3 and 4).
A structural analysis must be taken into account for
a full understanding of the basement complex [38]
. As a
result, the study’s enhanced Residual Magnetic Intensity
map will need to be processed further.
The RTE map was extended upwards to 2 km (Figure
5) to emphasize the response of the basement rocks. The
most essential consequence of this filter on the map is that
it smoothies it out and makes it more regional, allowing
regional basement abnormalities to be seen. Furthermore,
shallow-seated abnormalities are muted, allowing the
deep-seated ones to shine. The short-wavelength anoma-
lies have begun to fade at the 2 km continuation (Figure
6), allowing longer wavelength anomalies to consolidate;
anomaly units are increasing. Concrete linkages between
anomaly types are obvious and distinct, enhancing signals
from deeper sources and showing a rise in the amplitude
and spread of the magnetic sources responsible for the
high magnetic intensity signals that spread throughout key
portions of the research area.
Figures 6 and 7 present the results of horizontal de-
rivative maps of the study area in the x and y direction
respectively. The magnetic values are very low and range
from about –0.0710 to 0.0752 nT. Eight regions of pos-
itive anomalies were identified in Figure 7 (x-direction)
with the highest value of 0.0454 nT while nine regions of
such were detected in Figure 8 (y-direction), the highest
value being 0.0752 nT. Although with an uneven distri-
bution of anomalous bodies across the study area, there is
a concentration of such around the northeastern part. The
region with the highest positive anomaly in Figures 7 and
8 is around Ogoja, the central part of the study area. The
maps further helped, as indicated with different colors, to
delineate the edges of the deep-seated anomalies that have
been enhanced by the horizontal derivative filter [16]
. The
regions of high anomalous behavior (pink and red color)
Figure 3. Residual aeromagnetic map of the study area (Source NGSA)

30
Figure 4. Reduced to the equator map of the study area
Figure 5. Upward continuation map at 2 km

31
Figure 6. Horizontal derivative map (x-direction)
are identified as the sub-basins and this agrees with the work
of Abubakar [1]
. A close look at Figures 7 and 8 shows a NE-
SW trend of the identified anomalous zones [16]
.
The delineated linear characteristics in the research
area were visible on the tilt angle derivative map (Figure
9). A careful observation of the trends of the lineaments
shows that most of the faults present in the study area
align NE-SW direction but except for some trending in the
NNW-SSE, NNE-SSW, and NW- SE directions [39]
. The
tilt derivative map’s dominant fault trend corresponds to
the whole Benue trough, Benkhelil’s axial NE-SW trend-
ing (1988, 1989). Figure 9 shows the research area’s line-
ament map, which was derived from a Nigerian lineament
map. A large degree of similarity was obtained when com-
paring the faults/trends obtained from the tilt derivative
map and that of the lineament map of Nigeria. From these
maps, it could be seen that a major fault runs from around
Oturkpo in Benue state across Bantaji in Taraba state.
Another conspicuous major fault could be seen running
from the same Oturkpo to Dep in Nassarawa state. A good
number of minor faults dominate the study area giving the
impression that the area is structurally controlled. Some of
the faults intercepted the suspected sub-basins while some
aligned with the edge of some of the sub-basins.
In the graph, the estimated average power spectrum is
shown on a semi-log graph of amplitude against spatial
wave number (Figure 10). It distinguishes between two
separate sources, one with a low amplitude but a higher
wavenumber and the other with a low amplitude but a
higher wavenumber. The negative slope of a straight line
fitted to the two radial power amplitudes was calculated
to be twice the depth to the centre of mass of the bodies
creating the magnetic anomalies [26]
. Figure 10 further
shows that the deepest area is 5.2 kilometers deep, where-
as the shallower parts are 0.5 kilometers deep on average.
According to the plot of average radial power, deep struc-
tures have a longer wavelength than shallow sources. The
spectral solution also offers information about the aver-
age anomaly size (wavelength and amplitude), predicted
depths (maximum and minimum), and window size, all of
which are required to constrain Euler deconvolution [33]
.
The analytic signal derivative map (Figure 11) was created

32
Figure 7. Horizontal derivative map (Y- direction)
Figure 8. Tilt angle derivative map of the study area

33
Figure 9. lineament map of the study area (Extracted from Lineament map of Nigeria: Source, NGSA)
Figure 10. Radially averaged power spectrum with depth estimate

Advances in Geological and Geotechnical Engineering Research | Vol.4, Iss.4 October 2022

Advances in Geological and Geotechnical Engineering Research | Vol.4, Iss.4 October 2022

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