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Structural Characterization of High Resolution Aeromagnetic Data
for Potentially Mineralized Zones Identification Within the North-
Central Basement Complex of Nigeria
*Bwamba Jonah Ayuba, Abu Mallam and Abel Osagie
Physics Department, University of Abuja, Abuja
*Corresponding Author
DOI: https://doi.org/10.51583/IJLTEMAS.2024.130703
Received: 04 June 2024; Revised: 02 July 2024; Accepted: 10 July 2024; Published: 26 July 2024
Abstract: The aim of this study is to identify mineralization zones within the North-Central Basement Complex of Nigeria. The
goal was to locate important geological formations and assess the region's suitability for mining exploitation. Several filters were
used to enhance the short wavelength anomalies which could give preliminary information about the magnetic minerals present in
the study area which spans latitudes 8°00'N to 9°30'N and longitudes 6°30'E to 7°30'E. The total magnetic intensity map revealed
overall field strengths ranging from -99.63 nT to 109.33 nT. The filters used are fisrt horizontal and vertical derivatives, analytic
signal and 3-D Euler Deconvolution. The first horizontal and first vertical derivatives show structures like lineament that could
host to minerals present in the study. The Analytic Signal processing highlighted three distinct magnetic anomaly zones: a low
zone (0.004 nT/m to 0.013 nT/m), an intermediate zone (0.016 nT/m to 0.048 nT/m), and a high zone (0.057 nT/m to 0.282
nT/m). The horizontal derivative map displayed both positive and negative anomalies, with values ranging from -0.061 to 0.061
nT/m. The Euler depth analysis suggested the magnetic sources are located at depths greater than 2000 m, between 1000-2000 m,
500-1000 m, and less than 500 m. The lineament map revealed a dominant NE-SW trend, with a less dominant E-W and NW-SE
trend within the study area. The high lineament density areas of Kwali, Gwagwalada, Shanzhi, Dadabiri, Checheyi, Pangu, and
Suleja correspond to the various mineralization zones identified in the region.
Keywords: Mineral, Characterization, Basement, Complex, Aeromagnetic
I. Introduction
The analysis of aeromagnetic maps entails the interpretation of underlying rock formations and a thorough investigation of
structural and lithological changes within the subsurface. The magnetic basement refers to a collection of rocks that lie beneath
sedimentary basins and may occasionally be exposed at certain locations (Onyedim et al, 2007). Surface linear features can often
be observed on aeromagnetic maps, indicating the presence of magnetic anomalies in numerous sedimentary basins. These
anomalies result from the secondary mineralization occurring along fault planes.
The exploration of solid minerals is a worldwide occurrence, and numerous scientists have employed different techniques and
methods in various places to accomplish their objectives. The choice of method to be utilized is often influenced by the nature of
the deposits. For example, Guo et al. found that in two separate locations within China's Gansu Province, the ground magnetic
method effectively detected the presence of gold minerals connected to sedimentary layers and sulfides, including pyrrhotite.
However, the magnetic technique proved unsuccessful in identifying mineralization at the third site within the same region. This
was due to the magnetic signal being obstructed by the response of the igneous host rock, thereby preventing the detection of
mineralization. Hence, it is essential for a researcher to have a thorough understanding of the geological characteristics of the
study area in order to determine the most appropriate geophysical technique to utilize. Mohamed et al. (2017) used a combination
of these processing techniques to analyze aeromagnetic data and extract valuable information about the subsurface geology. Each
technique has its own advantages and limitations, and their integration allows for a more comprehensive understanding of the
magnetic anomalies and geological features present in the study area.
Other researchers such as Priscillia, et al., (2021), Arifin et al. (2019), Andrew et al. (2018), Adetona et al. (2013), Abdulsalam et
al. (2011), Arinze et al. (2018), Ejepu et al. (2020) and Oguche et al., (2021) have applied different aeromagnetic analytical
techniques to delineate mineralized zones in various locations across Nigeria. These researchers were able to discover numerous
new occurrences, including lineaments that could serve as hosts for minerals.
Despite being endowed with a variety of mineral resources tucked away beneath the earth's surface, Nigeria is entirely dependent
on crude oil (Obaje, 2009). Solid minerals are not effectively used for the benefit of the nation's economy. The volatile price of
crude oil greatly impacts the economy of Nigeria, and given that the hydrocarbon reservoir of the abundant Niger Delta is being
drained or may soon run out as a result of ongoing exploitation, the focus needs to be directed into other areas. Investigating our
readily available natural resources is a logical approach toward diversifying Nigeria's economy. Local miners have widely
exploited natural resources illegally and in an unprofessional manner in several locations of Nigeria. However, to accurately
determine the quantity, types, and depth of these mineral deposits, the government has not done far much in carrying out the
geophysical investigation that is required.
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This puts mining operations in the hands of unlicensed miners who are ignorant of the resources found underneath the surface of
the earth. The economy, safety, health, and ecology of the nation are all impacted by the operations of these local miners.
Geophysical surveys face challenges due to mineralization due to complicated geology and structure, which prevents some of the
mineralization from providing a contrast with the host geology that can be identified by any of the geophysical parameters
(Hodges and Amine, 2010).
The research was aimed at delineating and characterizing subsurface geologic structures that could host possible minerals in the
study area using different filter applications.
Location and Geology of Study Area
The study area is part of Nigeria’s North-Central Basement Complex and covers longitude 6° 30' to 7° 30'E and latitude 8° 00′ to
9°30′N with an estimated area of 18000 km
2
. and falls within the area under stud According to Abaa (1983), the Basement
Complex of Nigeria can be regionally categorised into four (4) major lithological units namely; the Migmatites-Gneiss Complex,
Schist belt (Metasedimentary and Metavolcanics rocks), Older Granites (Pan African Granitoids) and Undeformed Acid and
Basic Dykes (Fig.1).
Fig. 1 Geological map of Nigeria showing the study area modified after NGSA (2010).
Fig. 2: Map showing the geological distribution of the site, modified from NGSA (NGSA, 2006)
According to reports, a diverse range of mineral resources, such as iron ores and gold have been discovered in the region, Burke
et al. (1976). The study area is characterized by the presence of migmatitic Precambrian basement rocks, Proterozoic
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metasedimentary belts (schist), granitoids, and tertiary sediments (fig. 2). The site contains a variety of rocks in its Precambrian
basement, including sheared rocks, migmatite, migmatitic gneiss, banded gneiss, and granite gneiss. Additionally, the Proterozoic
metasedimentary belts consist of schist (undifferentiated), phyllite, and slate. The Basement Complex and the sedimentary rocks
found in the Middle Benue and Northern Anambra Basins are the two main components of the geological makeup of the research
area, as described by Obaje (2009). Low-grade metasediments predominate in the N-S trending belts that make up the Schist
Belts, which are primarily located in the western part of Nigeria, according to Dada (2006). The main constituents of these belts
are supracrustal rocks from the Upper Proterozoic that have been folded inside the migmatite-gneiss complex. The schist belts
exhibit a variety of lithological features, such as amphibolites, pelitic schists, phyllites, banded iron formations, carbonate rocks
including marbles and dolomitic marbles, and clastics with different grain sizes. In the study area, the Zungeru, Igara and Muro
Hills Schist Belts are most prominent in Niger, Kogi and Nasarawa States respectively
The schist belts located in northcentral Nigeria that were captured within the study area constitute the principal ones. This is
mostly found around north and east of Abaji, extending northward towards the southern part of Yenche and east of Shanzhi.
II. Materials and Methodology
The methods employed in this research involved knitting appropriately six (6) aeromagnetic data sheets (185, 186, 206, 207, 227
and 228) obtained from Nigerian Geological Survey Agency (NGSA) which cover a total area of about 18,000 km
2
to form a
single database for the study which was used to produce the Total Magnetic intensity (Fig. 3). The magnetic inclination (− 0.36)
and declination (− 1.83) were obtained based on the International Geomagnetic Reference Field (IGRF) 2005 on the magnetic
calculator to generate the reduction to the magnetic equator (RTE) map (Fig. 3) by employing the use of Oasis Montaj software.
The RTE transformation focuses on aligning the peak of the magnetic anomalies with their underlying magnetic sources, thereby
removing any unevenness associated with magnetic anomalies at low latitudes and the influence of magnetic inclination (Oyeniyi
et al., 2016; Glbert and Gideano, 1985).
The data were captured for NGSA from 2005 to 2010 by Fugro Airborne Surveys as part of nationwide airborne geophysical
surveys and were acquired along a series of NE–SW profiles with a flight line spacing of 500 m and terrain clearance of 80 m.
This was followed by application of various enhancement filters on the TMI-RTE grid such as Analytic Signal to detect the edges
the bodies present and depth, Euler Deconvolution technique to ascertain the location and depth of the structure, Center for
Exploration Targeting grid analysis to reveal the lineaments which could serve as potential host for mineralized deposit in the
study area. Other enhancement filters like the vertical, horizontal and tilt derivatives have similarly been used in delineating
shallow basement structures or geological boundaries such as lineaments, cracks, fractures, faults, etc.
Total Magnetic Intensity Reduction to the Equator (RTE)
The inclination and declination angles of the geomagnetic field determine the form of magnetic anomalies caused by vertical
bodies in the geomagnetic procedures. The main field plunges vertically in the north and south magnetic poles, and symmetric
magnetic anomalies are shaped with the maximum or minimum precisely over the causative magnetic body. Because the
magnetic signature of magnetized things at low latitudes is usually bipolar, it is difficult to match the observed anomalous
maxima with the placements of sources at low magnetic latitudes (between 15
o
S and 15˚N). Researchers vary the analytical maps
in the space domain, as is the case with the reduction-to-the-equator (RTE), to make it easier to explain the anomalies at very low
latitudes. The magnetic data can be reduced to the equator (RTE) according to Leu (1982), to make the magnetic bodies appear
horizontal. Since the study area is located within low-magnetic latitudes (i.e., areas with geomagnetic inclination less than 15
o
),
where a satisfactory reduction to the pole (RTP) of magnetic data is not possible, and the TMI grid data were then transformed
using the RTE filter rather than the reduction to the pole filter. In order to modify and accentuate magnetic anomalies connected
to the boundaries of surface and near-surface geological bodies, structures, and depths, the TMI-RTE grid data for the region
were processed.
The RTE filter is represented mathematically by equation (1) (Bhattachanya, 1966).
where, I= Geomagnetic Inclination, D= Geomagnetic Declination and
= Directions of the wavenumber vector in degrees of
azimuth.
Analytic Signal
Nabighian (1972, 1974) presented the idea of the analytic signal for mathematical interpretation and demonstrated how its
amplitude produces a function with a bell shape over each corner of a two-dimensional body with a polygonal cross section. The
depth of a contact can be determined by the width of an anomaly, as long as the signal from a single point of contact can be
detected. This method is particularly useful at low magnetic latitudes and is commonly employed for reduction to pole data.
Moreover, even data that has not undergone reduction to pole can still yield the desired outcome when applying the method, as
the anomalies are usually accurately positioned above the causal bodies (Tsepav 2020). The analytic signal (AS) utilizes the
magnetization properties, including strength and direction, of inclined magnetizing bodies to transform their shapes and
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symmetrically align the peaks over their sources. This technique effectively displays the amplitude strength of litho-structural
features by highlighting their magnetization contrast. It serves as a valuable tool for mapping and classifying litho-structural
characteristics, as described by MacLeod et al. (1993). Roest et al (1992) showed that the amplitude of the three dimensional
analytic signal at location (x, y, z) can be derived from the three orthogonal gradients of the total magnetic field using the
equation:
----------------------2
where
is the amplitude of the analytic signal at (x, y, z).
First Horizontal and First Vertical Derivatives
The horizontal and vertical derivatives suppress the long wavelengths, which are deeper sources and regional features, and
enhance the shallow wavelength features, which are the product of near surface structures obscured by stronger effects of broader
regional features. First Vertical Derivative, FVD (Fig. 5) and First Horizontal Derivative, FHD (Figure 6) involve the process of
taking the derivative of the magnetic field data with respect to the vertical and horizontal direction respectively. According to the
references cited (Salem et al., 2007 and Okpoli & Akingboye, 2016), the tilt derivative has the ability to enhance both weak and
strong magnetic anomalies by placing the anomaly directly over its source. Mathematically, the Vertical derivative is defined as:
-----------------------------------------------3
Where is the x component of the wavenumber,
and n is the order of differentiation while the horizontal component is
represented mathematically by the algorithm:
---------------------------------------------4
where V represents the y component of the wavenumber,
and n is the order of differentiaton.
Tilt derivative is the arctan of the ratio of vertical derivative to the horizontal derivative,
, ------------------------------------------5
where the numerator and denominator are vertical and horizontal derivatives of the anomaly, respectively, the latter given by
--------------------6
Centre For Exploration Targeting (CET) Grid Analysis
The Centre for Exploration Targeting (CET) Grid Analysis extension for Oasis Montaj consists of a number of tools that provide
automated lineament detection of gridded data, which can be used for first-pass data processing. These tools provide a rapid
unbiased workflow that reduces the time with which one can interpret gridded data. The method contains tools for texture
analysis, phase analysis, and structure detection which are versatile algorithms useful for grid texture analysis, lineament, edge
and threshold detection. It utilizes standard deviation, which provides an estimation of the local variation in the data. It calculates
the standard deviation of the values within the local neighbourhood of each location in the grid, revealing features of significance
which often exhibit high variability with respect to the background signals. The standard deviation of the cell values x
i
for a
window of N cells whose mean value is is given as:
--------------------------------------------------5
3.5 3D Euler Deconvolution
Euler deconvolution’s technique is an equivalent method based on the Euler’s homogeneity equation as developed by Reid et al.
(1990) following Thompson’s (1973) suggestion and operating on gridded magnetic data. These Euler depth solutions not only
estimate the depth, but also delineate the horizontal boundaries, Wilsher (1987). In a general case, scattered data points cannot
provide superior solutions. Various researchers have used 3D Euler deconvolution technique for source depth estimations
(Nabighian, et al., 2001). The method is based on the concept that anomalous magnetic fields of localized structures are
homogeneous function of the source coordinate and, therefore, satisfies Euler’s homogeneity equation. Usually the structural
index
(SI) is fixed and the locations and depths
of any sources are found using the following equation:
------------------6
where is the observed field of location
and f is the base level of the field [regional value at the point (x,y,z)] and
SI is the structural index or degree of homogeneity
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III. Results and Discussion
The interpretation of aeromagnetic data requires drawing conclusions about the geology of a region from the pattern of observed
magnetic signatures. Because magnetic minerals are important in the mineralization process, aeromagnetic survey can be used to
map the geology of a region; boundaries in magnetic properties are frequently correlated with lithological boundaries (Reeves
2005).
Total Magnetic Intensity Map
Positive anomalies are depicted by magenta and red shades, while negative anomalies are represented by light and dark blue hues.
These anomalies, characterized by low magnetic amplitudes, are believed to be related to the Formation of Bida basin, similar to
the Patti formation. Authors like Ladipo (1988), Nwajide (1990), Pettijohn (2004), and Akande et al. (2006) reported that these
areas consist of sedimentary rocks such as clay stones, silt stones, limestone, and interbedded shale. Fig. 3 shows the overall
strength of aeromagnetic intensity, ranging from -99.63 nT to 109.33 nT.
The map reveals negative features which is broadly seen in the southwestern part of the study area. These negative anomalies are
also visible around Suleja, Checheyi, Zuba, east of Dadabiri, south of Gwarinpa, the area around latitude N9
o
00′ and areas around
the far north-eastern corner of the area of study. The negative values range between - -99.63 nT and 10.47 nT. Strong magnetic
anomalies are also observed with the highest value peaking at 109.33 nT. These positive anomalies are observed to be trending in
the NW-SE direction. These strong magnetic anomalies can be visibly noticed around west of Dadabiri, north of Pangu, Yenche,
west of Kwali, southwest of Abaji and the northern part of Suleja. The map reveals strong correlation between regions of high
magnetic anomalies and locations of mining pits within the area of study. These regions of strong magnetic anomalies can be
observed in fig. 2 to correspond geologically with migmatite, migmatitic gneiss, banded gneiss/ biotite gneiss and undifferentiated
schists including phylitties assemblages.
Fig.3: Total Magnetic Intensity map of the study area.
Total Magnetic Intensity Reduction to the Equator (TMI-RTE) Map
In order to properly map and delineate inclined and other aligned forms of structures, the TMI-RTE image (Fig. 4) is created by
centering the peaks of magnetic anomalies on their sources based on the inclination and declination of the local field of the
magnetizing body.
Using Oasis Montaj program v8.4, the entire Intensity Magnetic Map (Fig. 3) is reduced to the equator. Fig. 2 and fig. 4 depict
litho-structural similarities between the geological map and the RTE_TMI image. The severely distorted rocks in the region
clearly match the Migmatite-Gneiss Complex because they show evidence of both positive and negative intensity values, which
ranged from -101.98 to 101.69 nT. These abnormal variations in the rocks are linked to ferromagnetic minerals, which frequently
result in extremely high magnetic intensities, strong degrees of metamorphism, and deformities that yield low and negative
intensity values. The area of study can be divided into four magnetic zones on the TMI-RTE map, each of which has a distinct
magnetic anomaly pattern.
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The first zone occupies the southwestern (south of Abaji), northeastern (Suleja and Zuba) and southeastern parts of the area. This
is also observed to occupy the northeastern and southeastern fringes of the study area and is underlain by the Precambrian
crystalline basement rocks (migmatites, sandstones, siltstones, shale and porphyritic granites). It is distinguished by extremely
low wavelength (low wavenumber) anomalies (bright and dark blue colors), with a magnetic intensity amplitude ranging from -
101.98 nT to -14. nT. The second magnetic anomalies zone has its values ranging from -1.02 nT to 27.96 nT. This zone shows
positive anomalies which are distinguishable by low magnetic values. These low values are observed around south of Gwarinpa
(areas around latitude 9
o
00’N), the northern part of Zuba and the areas around latitude 8
o
30’N (west of Abaji). This is also
noticeable around the southeastern part of the study area extending to southeastern part of Abaji. These areas are characterized by
the presence sanbstone, siltstone shale, medium to fine and coarse-grained biotite granite (fig. 2). The third region comprises of
anomalies ranging from 33.16 nT to 59.33 nT (light yellow to red coloration). This region is noticeable around Shanzhi and the
adjoining areas, Kwali, Pangu, north of Yenche, Dadabiri and southwest of Abaji. The zone represents transition between
sedimentary and basement complex zones of the lower Bida basin. The fourth zone occupies the northwestern (north of Pangu,
west of Kwali, west of Dadabiri and the extreme north ) and central (Yenche) part of the study area. This zone is characterized by
very high magnetic anomalies values ranging from 66.40 nT to 101.69 nT. The areas are geologically underlain by sanbstone,
siltstone, shale, migmatic gnesis and banded gnesis/biotite gnesis. The high anomalies are oserved to be trending in mainly in the
NW-SE direction.
Fig. 4: RTE map of the study area.
Analytic Signal map
By altering the inclined magnetizing bodies' forms based on the direction of geologic strike with respect to the magnetization
vector, the analytic signal (AS) focuses the peaks of the magnetizing bodies uniformly over their origins. Based on the
magnetization of various rock compositions, the Analytic Signal map (fig. 5) displays all the edges of anomalous occurrences,
structural patterns, and lithological contacts, (Faruwa et al. 2021 and Lawal, 2020). This has suggested the characterization of the
study area into three magnetic anomalies zones: the low zone (0.004 nT/m to 0.013 nT/m), the intermediate zone (0.016 nT/m to
0.048 nT/m) and the high zone (0.057 nT/m to 0.282 nT/m). The geologic units (undifferentiated schists, granite gneiss, biotite
gneiss, migmatite, and migmatite gneiss) around Gwagwalada, Kwali and Wuna can be said to be responsible for the very high
magnetic anomalies with the study area. This strong signal amplitude also corresponds with that of the mining sites of Pangu,
Wuna and Checheyi, an indication that these minerals are structurally controlled. The younger metasediments, like the phyllites,
are represented within the intermediate zone. The low magnetic zones (areas south of Pangu, west and south of Abaji, south of
Gwarinpa and the region around latitude 8
o
45'N to 9
o
00'N and longitude 7
o
15'E to 7
o
30'E) correspond to the sedimentary terrains
consisting of clay, pebbles, and sandstones. Arewa and Fahad (2024) obtained (> 0.094 nT/m), (0.028 to 0.094 nT/m) and (<
0.028 nT/m) as high, intermediate and low zones respectively in the north-western part of Nigeria.
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Fig. 5: Analytic Signal map of the study area.
Horizontal and Vertical Derivatives maps
The first vertical derivative map (fig. 6) has been successful in showcasing the short wavelength magnetic characteristics and
capturing long wavelength geologic features. The magnetic intensities of structures around the mining sites range from 0.003 to
0.0112nT/m which corresponds with sediments with thicker formations.
The horizontal derivative map displays both positive and negative anomaly with values ranging from -0.061 to 0.061 nT/m as
shown in fig. 7.
Fig. 6: First Vertical Derivative map of the study
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Fig. 7: First Horizontal Derivative map of the study
3-D Euler Deconvolution (ED) Map
In this study the 3-D Euler Deconvolution technique has been applied to the TMI_RTE grid data of the study area to characterize
depth and location of basement rock contact (faults or dykes) at structural index of one (SI=1) which conforms with what Reid, et
al (1990) have reported. The result of the Euler depth shows the depth of magnetic sources range to be >2000 m, 1000 to 2000 m,
500 to 1000 m, and < 500 m. Most of the highly magnetic structures and intrusive depth sources are within the range of < 100 to
500 m (Fig. 8). The depth estimation of mineralization potential sources around the mining sites of Pangu, Checheyi and Wuna in
the central part of the study area is found to be <500 m which is in agreement with the result of Analytic Signal, the FVD, FHD
and CET structural maps.
Fig. 8: 3D Euler deconvolution depth solution.
Lineament map
The lineaments derived from aeromagnetic images were evaluated based on their length and orientation, so investigating their
spatial arrangement in order to better illustrate the faults in the study area and obtain additional insight into the distribution and
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presence of lineaments. Fig. 9 shows the concentration of these structures predominantly in the northern half of the study area. An
analysis of the structures for the orientation was carried out and plotted as rose diagram (Fig.10). The predominant directional
patterns are shown as E-W, NE-SW, and W-NW to E-SE in Fig. 10. Authors like Holden, et al. (2008), Faruwa, et al. (2021) and
Arewa and Fahad (2024) have reported that lineaments are essential to the interaction with mineral deposits. They are thought of
to offer channels for fluids rich in minerals to build up in the upper crust of the Earth. As a result, lineaments have been widely
used in mineral exploration as a useful reference point, and their significance in this sector has been emphasized. A visual
observation of the lineament map (Fig. 8) reveals the dominant presence of these structures around the mining sites of Pangu,
Wuna, Checheyi and other locations like Kwali, Gwagwalada, Dadabiri, Shanzhi, Suleja, Zuba and Gwarinpa which shows that
mineralization potential of the area is structurally controlled. Therefore, these zones of high lineament density are the areas with
high mineralisation.
Fig. 9: Lineament map of the study area.
Fig. 10: Rose diagram showing structural trending direction within the study area.
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IV. Conclusion
The aeromagnetic data collected over the North-Central Basement Complex area has been interpreted using various filtering
techniques. The results suggest the presence of magnetic mineral deposits within this region.
Due to the diverse range of rock types in the area, each with their own unique magnetic susceptibilities, the total magnetic
intensity (TMI) map created shows considerable variability in magnetic intensity across the research area. The magnetic intensity
values observed within the study region span a range from -99.63 nT to 109.33 nT. The 3-D Euler Deconvolution technique
applied revealed the depth of magnetic sources range to be >2000 m, 1000 to 2000 m, 500 to 1000 m, and < 500 m. Most of the
highly magnetic structures and intrusive depth sources are within the range of < 100 to 500 m. from the lineament map the
predominant directional patterns are shown as E-W, NE-SW, and W-NW to E-SE. Based on the visual observation of the
lineament map (Figure 8), the dominant presence of these structures is evident around the mining sites of Pangu, Wuna,
Checheyi, as well as other locations like Kwali, Gwagwalada, Dadabiri, Shanzhi, Suleja, Zuba, and Gwarinpa. This suggests that
the mineralization potential of the area is structurally controlled which is responsible for localization of gold deposits in the study
areas.
Conflict of Interest
Authors declare that they do not have any conflict of interest whatsoever.
Acknowledgement
The authors sincerely appreciate the Nigeria Geological Survey Agency (NGSA) for releasing the data used for the study. The
authors also acknowledge the management and staff of Sheda Science and Technology Complex (SHESTCO), Abuja for their
moral and for their support
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