Interpretation of aeromagnetic and remote sensing data of Auchi and Idah sheets of the Benin-arm Anambra basin: Implication of mineral resources

1.1 Detailed geology of the study area

The study area covers Auchi and Idah of the Benin-arm of the Anambra basin (Figures 1 and 2). The area mostly covers the northern part of Edo state, extending into the southern part of Kogi state, Nigeria. The area lies within the geographical coordinates of latitude 7°0′0″ and 7°30′00″ North and longitude 6°0′0″ and 6°60′0″ East. The climate is tropical and humid; the temperature varies from 42°C in February to 23°C in July. The area is marked by two distinct seasons: the dry and rainy seasons. The dry season spans between late September and early April, while the rainy season is between late April and early September. The Anambra Basin is one of Nigeria’s Cretaceous sedimentary basins [6,7]. The basin is bounded by the Niger Delta Hinge Line on the south–western flank, the Benue Flank in the north–west, and the Abakaliki fold belt in the south–east [6]. The southern frontier of the basin overlaps with the northern frontier of the Niger Delta basin [8].

Figure 1

Map of the study area (Auchi and Idah).

Figure 2

Geological map of the study area.

The basin is coarsely triangular in shape, which covers an approximate area of 40,000 km² in which the sediment thickness aggregates southwards to a maximum thickness of 12,000 m in the central fragment of the Niger Delta region [6,8]. The Anambra basin stretches from the southern part of the confluence of the west of the River Niger and Benue through terrains like Asaba, Okene, Auchi, Agbo, and to the east of the river Awka, Idah, Onitsha, Nsukka, and Anyangba area. The Udu-Idah cliff slopes slightly in the southwest direction into the flood plain of the River Niger and across to the west at an elevation of about 300 m [9]. The basin is mainly drained by the Anambra River and its main tributaries, the Mamu and Adada [9].

2.1 Aeromagnetic data processing

To process the aeromagnetic data, two databases were created using the magnetic data processing software by Oasis Montaj Geosoftware 7.0. The data processing consists of four procedures, which are gridding, determination of the residual magnetic field by subtracting IGRF from total magnetic data measured from the field, micro-levelling of the whole dataset to eliminate any form of errors [10,11], and integration of the different windows for each different type of data using different filtering techniques expressed in equations (1)–(3).

Interpretation was carried out by inspecting the total magnetic field. The residual map was generated after the removal of regional effects from the total magnetic intensity using the Butterworth low-pass filtering method. Butterworth helped in simplifying the filter by using a normalised low-pass polynomial. The residual anomaly maps are derived using polynomial fitting to desirable degrees. Polynomial fitting degrees 1 and 2 were used to generate residual anomaly maps and to investigate how the degree of polynomial fitting affects the appearance of the maps. The equation used in the algorithm for the separation of the regional field is expressed as follows:

where r represents the regional field, a 0 , a 1 , and a 2 are known as the regional polynomial coefficients, and X ref and Y ref are the X and Y coordinates of the geographic centre of the dataset, respectively. X and Y offsets are used to prevent higher order coefficients from being very small in the polynomial equation. The residual magnetic intensity (RMI) data were processed to improve the signal-to-noise ratio and eliminate any high frequency events that may reflect cultural or other noise types or error in the data and are then subjected to further processing. Only the residual fields are of concern and thus form the basis of this study. Various filters such as reduction-to-pole (RTP), vertical derivative, analytic signal, and Euler deconvolution were used for the aeromagnetic processing.

RTP algorithm was applied to the total magnetic anomaly because the data were acquired closer to the magnetic latitude. The RTP map was produced by applying RTP filter on the RMI map using Fast Fourier Transform (FFT). The RTP operation in wave number domain can be represented as follows:

where A p ( u , v ) is the Fourier transform of the observed magnetic data, A e ( u , v ) is the Fourier transform of the vertical magnetic field, I is the inclination, D is the declination of the core field, ( u , v ) is the wave number corresponding to the ( x , y ) directions, respectively and θ = arctan u v [12]. Since the data were collected near the magnetic equator, RTP algorithm was applied to the total magnetic anomaly.

The parameters involved include an inclination value of −11.27 and a declination value of −1.18. An amplitude correction value of 60 was also applied to reduce latitudinal effects because the survey was carried out in an area close to the equator, hence RTP at low latitude.

The RTP grid data were subjected to a first vertical derivative (FVD) filter. The FVD filter allows small and large amplitude responses to be more equally represented. The FVD grid helps enhance linear features in the area. Vertical derivative filters are generally applied to gridded data using FFT filters. These maps were produced by the application of derivative filters on the RTP map of the area of study using FFT. An upward continuation value of 200 was also applied to the filter in order to reduce the influence of signatures that may be due to manmade features. Different vertical derivatives of the magnetic fields can be measured through multiplication of the field’s amplitude spectra by a factor in the form as follows:

where n is the order of the vertical derivative and ( u , v ) is the wave number corresponding to the ( x , y ) directions, respectively. The use of the first horizontal gradient or derivative (1HD) was used to enhance the RTP grid and is important for mapping linear geological structures such as faults in the magnetic data.

The analytic signal behaves like the RTP filter. It actually takes the magnitude of the square root of the vertical and horizontal components of the magnetic responses [13]. This processing enhancement was used in mapping the edges of the permanently magnetised sources and for centering anomalies over their causative bodies in areas of low magnetic latitude but does not depend on the direction of magnetisation. The analytic signal map was created via the application of an analytic signal filter on the RMI map of the area of study. The extension of the Geosoft Oasis Montaj’s depth to basement, which defines the location (distance along the profile and depth), dip (orientation), and intensity (susceptibility) of magnetic source bodies for magnetic profiles, was used in an attempt to measure the depth to basement of the magnetic bodies in the study area. In calculating the depth of the magnetic anomaly, the Euler deconvolution function, which makes use of both the horizontal and vertical derivatives, was employed.

The Euler deconvolution function assumes that the source body is either a dyke or contact of limitless depth and employs the least-square method in a series of shifting the profile windows to solve for source body parameters [14]. Solutions deduced from total field profile are referred to as “Dyke” solutions, while solutions resulting from horizontal gradient are referred to as “Contact” solutions. Euler deconvolution algorithm was used for the location and depth determination of causative anomalous bodies from gridded aeromagnetic data. The 1.0 and 2.0 values of the structural index (N) were used based on individual dykes and sills models of the source [15]. Through the application of the window method, the solutions derived were further refined to eliminate ambiguity. This was accomplished by limiting the solutions obtained from Euler deconvolution to a maximum tolerance depth of 10% while rejecting the uncertainty in depth ( d z in percentage) that is greater than 10% just for easy processing. Likewise, horizontal ( d x in percentage) uncertainty has been fixed at 20%. This automatically appends a mask to those solutions whose outputs do not fall within the stated window.

2.2 Landsat 8 processing

The image processing software Environment for Visualising Images (ENVI) version 5.1 was used for processing the remote sensing data. The remote sensing data were interpreted using ArcGIS, Rockware, PCI Geomatic software, Rockworks, and GeoRose software. Atmospheric correction was applied to the Landsat8 scenes using the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercube (FLAASH) algorithm [16]. The FLAASH algorithm was implemented using the Sub-Arctic Summer atmospheric and maritime aerosol models [13]. During the atmospheric correction, raw radiance data from the imaging spectrometer were re-scaled to reflectance data.

Colour composite images were produced based on known spectral properties of rocks and alteration minerals in relation to the selected spectral bands. Bands 4, 3, and 2 were used to produce true colour composite, while bands 5, 4, and 1 were used to produce false colour composite images of the study area for lithological mapping. Both the true and false colour maps were produced by importing individual 8-bit greyscale surface reflectance bands of the Landsat8 data into the ENVI workspace. This was applied after the Landsat8 data for the area had been processed and corrected using ENVI and ArcGIS software. Band ratio indices were developed and implemented to Landsat8 spectral bands for mapping poorly exposed lithological units, geological structures, and mineral alteration in the study area. Spectral-band ratio indices were used to map the spectral signatures of iron oxide/hydroxide minerals, the OH– and Fe, and Mg–O–H bearing lithological units within the area of study. The surface and subsurface lineaments were merged in ArcGIS to get a detailed understanding of the lineament structure or lineament pattern of the research area. Two band ratios were formed based on the laboratory spectral of minerals for mapping the abundance of iron oxide/hydroxide minerals in rocks using the Landsat8 bands [17,18]. Goethite, jarosite, hematite, and limonite are typically heavily absorbed into visible and near-infrared (0.4–1.1 μm), coinciding with Landsat8 bands 2, 3, 4, and 5, and high reflectance into short wave infrared (SWIR) (1.56–1.70 μm) coinciding with Landsat8 band 6 [19]. Therefore, band ratio of 4/2 was used to map iron oxide mineral groups. The Landsat8 SWIR bands were used to detect hydroxyl-bearing (Al–OH and Fe, Mg–OH) groups within the study area. Clay minerals contain 2.1–2.4 μm spectral absorption features and 1.55–1.75 μm reflectance, coinciding with Landsat8 band 7 (2.11–2.29 μm) and band 6 (1.57–1.65 μm) [19]. Therefore, ratio 6/7 was used to map the hydroxyl-bearing mineral group in this study. For the ferromagnesium (Fe, Mg–OH) group of minerals, bands 5 and 6 of Landsat8 were used due to the group’s high absorption feature in band 5, and low absorption characteristics in band 6.

For a detailed structural and lithological mapping of the study area, surface structures were extracted from processed Landsat8 data of the area using the automatic lineament extraction tool in PCI Geomatica. The lineament density map was produced by combining both the surface and subsurface lineaments and calculating their densities using the line density tool. The surface and subsurface lineaments were merged in ArcGIS to aid a detailed understanding of lineament structure or lineament pattern in the study area.

5.1 Implication for mineral resources

This aim of this study was mapping the lithology and structure of sheets 266 and 267. The objectives of the study are to map out the various lithologies and structures present in the area and assess their potential for mineral exploration. Mineralised pegmatite vein, which are often easily weathered due to the effect of water on feldspar [26], appear to produce clay-like signatures on the band ratio maps. The ability to find such areas helps in easily detecting veins, which can play a major role to host minerals of economic benefit. Also, gold mineralisation has often been discovered in areas within the iron oxide alteration zones [26]. These outcomes provide focus on knowledgeable objectives for better preparation before exploration, rather than random excavation that can lead to waste of resources, time, and money. The lineament map shows areas of high, moderate, and low lineament density. The presence of lineaments shows that the area has the potential to host mineralisation because lineaments can potentially serve as conduits through which hydrothermal alteration can occur. The result from the rose diagram revealed that the surface lineaments are aligned in the NW–SE, N–S, NE–SW, E–W, NNE–SSW, ENE–WSW, and ESE–WNW. The orientations of the subsurface lineaments align mostly in the NE–SW, N–S, and E–W directions, which agrees with the previous studies [21,22].

However, aeromagnetic method results showed that the magnetic intensity values ranged between −431.38 nT for magnetic low and 399.82 nT for magnetic high, with RTP map magnetic intensity ranging from −416.45 to 664.45 nT. The FVD map showed magnetic intensity values which ranged from −0.5863 to 0.9060 nT/km². The total horizontal derivative map intensity ranged from −0.00031 to 0.762691 nT/km² while the analytical map showed magnetic intensity ranging from −14.0664 to 394607.3438 nT/cm². The windowed Euler deconvolution depth to magnetic source showed depth range of <20 to 2,000 m. The results also show the major lithological boundary between the basement and sedimentary rocks in the area. Various structures were deduced from the first, second, and total vertical and horizontal derivative maps of the area. These structures are a product of tectonism and their various orientations agree with the findings from previous research in the area. For example, areas characterised by iron oxide alteration are the most suitable for gold exploration due to various hydrothermally altered structures, which is in line with the works of Ejepu et al. [26]. Also, clay alteration zones sometimes are regions where mineralized pegmatites are weathered, which agrees with the work of Obaje [27]. Based on the findings of this research, it is concluded that the area of study has huge mineral exploration potential and it is not surprising that various deposits have been recorded already, which include iron ore, marble, coal, and kaolinite.