Introduction

Groundwater, because of its natural microbiological quality and overall physicochemical quality, is frequently recommended for drinking and domestic uses (Zhu and Ierland 2012), since surface water is affected by different geological factors than groundwater. Hence there is increase demand for understanding of groundwater movement, occurrence, quality, and availability, to match-up the increase of agricultural, industrial, and municipal development (Osgrove and Loucks 2015; Hamidu et al. 2016). Water is a valuable natural resource, and its development ensures the continuation of life and global civilization. An average human requires three (3) liters of drinkable water per day to maintain necessary body fluids for regular body metabolism, and larger amounts are required for energy (thermoelectric power plant cooling, hydroelectric power generation) and food production via irrigation (Delleur 1999; Cox et al. 1996). In the formation of storm and snowmelt runoff in streams, groundwater plays a far more active, responsive, and substantial role. Groundwater dominates the runoff hydrographs in many basins, except for the most intense rain storms and the most prolific melting days, according to basin-wide tracer experiments using environmental isotopes (18O, deuterium, tritium) and hydrometric studies carried out in hydrogeologically diverse watersheds (Hiscock 2005).

However, as a result of urbanization, increasing water supply has become a common goal and priority of all nations in order to continue irreversible economic growth and increase per capita water consumption for home, municipal, irrigation, and industrial uses (Bayewu et al. 2017; Olatokunbo-Ojo and Akintorinwa 2007). Meanwhile, groundwater availability, movement, and chemistry are influenced by topography, physiography, drainage basin, surficial geology, and vegetation, as well as the interrelationship with precipitation; physical and chemical disintegration/decomposition processes resulting in cracks, fractures, and joints, which form voids/void spaces; and sediment accumulation (Freeze and Cherry 1979; Fetter 2007; Brassington 1988). As a result, in groundwater evaluation and development, the composition of sediments, fissures, fractures, cracks, and pore spaces in earth materials is critical; likewise porosity, hydraulic conductivity, and hydraulic gradient. As significant as these characteristics are in groundwater study, they indicate the type of aquifer/water bearing unit productiveness (Logan 1964; Bouwer 1978; Chaanda and Alaminiokuma 2020; Heigold et al. 1979).

The characteristics of the overburden or weathered layer, as well as cracks, are the key hydrogeological components of Nigeria's crystalline basement complex (Adepelumi et al. 2013; Aina et al. 2019; Akinrinade and Adesina 2016; Alaminiokuma and Chaanda 2020). The overburden is primarily the product of weathering or chemically precipitated materials (formed by compaction process, removal/addition of materials, mineral replacement or change in mineral phases). Lithological factors, which are primarily determined by the composition, texture, and sequence of rock types; and structural factors, which include faults and folds that disrupt the continuity or uniformity of occurrence of a rock type or sequence of rock types, are examples of geologic factors that act as controls on surface water phenomena (Bawallah et al. 2019; Bayewu et al. 2018). Structures such as beds and joints can have a significant impact on water circulation and drainage pattern formation. These elements, in combination with hydroclimatic processes, govern soil and topographic development, which has a significant impact on water distribution and movement (Schwartz and Zhang 2003; Walton 1991).

An aquifer in a lithologic unit is the one that has a significantly higher transmissibility, holds and transmits water that can be recovered in economically useful quantities (De Marsily 1986; Agyemang 2021; Kosinski and Kelly 1981; Aina et al. 2019). Aquicludes, or confining beds, are low-permeability lithologic strata that surround the aquifer. Aquitards are limiting beds that allow amounts of water to flow through them. A geohydrologic unit is an aquifer or a mixture of aquifers and confining beds that form the foundation for a relatively distinct hydraulic system (Assaad et al. 2004; Chenini et al. 2015). Consequently, for an aquifer to be adjudged prolific, it must have undergone aquifer potential assessment or mapping. Groundwater potential mapping is a method of using the surface and sub-surface symptomatic scientific parameters for determining the prolificacy of the aquifer zones in an area by quantitative and qualitative assessment (Adebo et al. 2008, Ilugbo et al. 2019; Chaanda and Alaminiokuma 2020; Bayewu et al. 2018).

The sequence, lithology, thickness, and structure of rock formations have an impact on groundwater occurrence, transport, and storage, while permeability and porosity are the primary determinants of movement and storage capacity (Singhal and Niwas 1981).

More than any other electrical approach, electrical resistivity has been used in groundwater studies (Adebo et al. 2022; Bisson and Lehr 2004; Delleur 1999; Zohdy et al. 1974; Ramanuja 2012). Electrical resistivity is a measurement of a medium’s or materials’ capacity to conduct current. It’s a physical attribute that's measured in ohm-m. The electrical geophysical survey identifies aquifer units and determines geoelectric properties of formations; and depth and lateral extent (Loke 1997; Burger 1992). Depending on the field design, the procedure entails introducing time-varying direct current or very low frequency current into the subsurface between potential electrodes. Current travels from the positive current electrode to the negative current electrode in a two-electrode system, forming equipotential surfaces and orthogonal current flow lines (Telford et al. 1976). As the distance between the electrodes is increased, the percentage of current flowing at depth increases, and the subsurface resistivity is measured as voltage or potential (V) using a resistivity meter. This measurement is used to calculate resistivity using the supplied current (I) and a geometric factor (K) that is a function of electrode layout, as indicated in Eq. 1.

$$\rho = \frac{\Delta V}{I}K.$$
(1)

The Wenner and Schlumberger arrays are the commonly used electrode configuration (Abdullahi et al. 2015; Adagunodo et al. 2018). In Wenner array, the electrode spacing is fixed value and the apparent resistivity of the subsurface is computed using Eq. 2, while in the Schlumberger array the spacing between the potential electrodes is much smaller than the spacing between the current electrodes (Telford et al. 1976; Andreatta et al. 2016; Olatokunbo-Ojo and Akintorinwa 2016).

$$\rho = \frac{\Delta V}{I}2\pi a.$$
(2)

Electrical conduction can also be utilized to map subsurface structure and stratigraphy and infer lithology information in addition to deducing hydrogeological parameters (Olayinka and Oladunjoye 2013). In fluids, electrical conduction occurs most commonly in linked pore spaces, grain boundaries, and fractures. Porosity, moisture content, texture, the presence of clay minerals, and the resistivity of the pore fluid all affect electrical resistivity/conductivity; while all of these factors lower resistivity, higher soil salinity, moisture content, clay content, and grain size decrease resistivity (Robinson and Coruh 1988). An increase in porosity, increased fractures, and intense weathering all lower resistivity in a water-filled geological formation.

Aquifer vulnerability assessment is a pragmatic technique of defining the susceptibility of groundwater to contamination (Connell and Daale 2003), based on a number of physical considerations that control the movement of pollutants through the vadose zone to the water table (Olojoku et al. 2017; Brindha and Elango 2015). The objective of vulnerability mapping is to identify the most vulnerable zones of catchment areas and to provide criteria for protecting the groundwater used for drinking water supply (Doerfliger et al. 1999). Intrinsic vulnerability is evaluated and mapped based on the hydrogeological properties of the aquifer system and is independent of the nature of pollutant (Van Stempvoort et al. 1993a, b; Civita and Maio 2000; Zwahlen 2004). The aquifer system subsequently can be protected by the overlying earth layers (often regarded as the protecting layers) depending on their permeabilities, hydraulic conductivities and thicknesses. Aquifer protectivity in this case is the ability of the aquifer overburden layers to act as natural barriers to filter percolating surface contaminated fluid There have been many models developed for the assessment of groundwater contamination studies, which include statistical, overlay and index methods (Brindha and Elango 2015; Chenini et al. 2015; Vias et al. 2006; Zwahlen 2004; Daly and Drew 1999; Foster 1987; Foster and Hirata 1998; Civita and Maio 2000; Aller et al. 1987; Adewumi et al. 2016); and each model is unique in the sense that it is applicable to a particular type of geoenvironmental conditions e.g. GOD (Foster 1987), IRISH (Daly and Drew 1999), AVI (Van Stempvoort et al. 1993a, b), DRASTIC for the USA (Aller et al. 1987), SINTACS which is modified DRASTIC for Mediterranean condition (Civita, and Maio 2000), COP (Vias et. al. 2006), EPIK for Karst region (Doerfliger et al. 1999). No vulnerability assessment model is generic enough which can cater to the needs of all kinds of geological environments (Olojoku et al. 2017; Daly and Drew 1999). However for this study, the susceptibility index utilizing longitudinal unit conductance was used, due to simplicity, rapidity, with parameters from derived electrical resistivity.

The groundwater modeling using groundwater potential index value (GWPIV) has been playing significant role in sustainable water resource management (Mogaji 2016; Ariff et al. 2008; Oyedele 2019; Pietersen 2006). It includes data mining and classification or zonation of aquifers properties (Adebiyi et al. 2018; Adiat et al. 2013; Adebo et al. 2018). The data used in GWPIV modeling can emanate from geophysical, geological, hydrogeological and Geographic Information System (GIS) data which provides adequate information in delineating groundwater potential zone (Ilugbo et al. 2018; Epuh et al. 2020; Adiat et al. 2012). Modeling is an attempt to reproduce the behavior of groundwater or hydrologic system by defining the essential features of the system using some controlled physical or mathematical manner (Mogaji 2016; Adiat et al. 2013). Modeling plays an extremely important role in the management of hydrologic and groundwater system. Moreover, in recent years, the use of the geographic information system (GIS) has grown rapidly in groundwater studies and management.

The hydrogeological study of water bearing units in Itaogbolu was carried out to determine their potential and the vadose zone’s protective capabilities. The study's motivation was sparked by the town's lack of or insufficient water supply. Furthermore, most boreholes are dug without first conducting a hydrogeophysical research, resulting in low yield/failed boreholes. The town's population has grown by about 50% in the last decade, putting a strain on the existing water supply infrastructure provided by the state government, while individually drilled water wells are insufficient to fulfill daily family demand. As a result, it became necessary to supplement the existing water supply by first conducting hydrogeological research in the area (Alam et al. 2012; Alfonsina and Chaanda 2020; Bedient et al. 1994), for sustainable use of the resource, by using electrical resistivity, hydraulic property through pumping test (Kruseman and de Ridder 1994; Bear 1979), longitudinal unit conductance, hydrogeological parameters and AVI (Stempvoort et al. 1993a, b). Because groundwater is thought to be naturally protected vis-a-vis vadose layer (Olayinka and Oladunjoye 2013), relatively minor treatment is necessary (EPA 1977; Driscoll 1986). However, the vadose’s total protective capacity is determined by its hydraulic property, the composition of the material, and its bonding (Birsoy and Summer 1980; Ndatuwong and Yadav 2014). To logically preserve, regulate, or sustain groundwater depletion, large volumes of resource data are required. The majority of difficulties with water wells/boreholes are caused by a lack of planning, or a lack of data and understanding when preparing for or managing the resource. Hydrogeologic data is one type of environmental data that can be quite useful in resource planning and management. Unfortunately, many engineers and well/borehole developers are unaware of it or use it infrequently, and its importance and usefulness are often overlooked. If any inferences about the groundwater regime are made at all, they are typically incorrect and unjustified, and crucial planning and management decisions are frequently based on assumptions.

Location and physiography

Ita Ogbolu is the administrative center of Ondo State’s Akure North Local Area, located between 747,400 m and 749,400 m East and 813,500 m and 817,000 m North (Fig. 1). It may be reached via Igoba on the Akure-Ado-Ikere Ekiti Highway. It is bordered on the north by Ikere and on the west by Ijare.

Fig. 1
figure 1

Location map of Ondo State showing the study area. Inset: Map of Africa

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The climate is equatorial belt, with the rainy season from March to October. The geography of Ita-Ogbolu is undulating/rugged, with elevations ranging from 437 to 496 m (Fig. 2). The average yearly temperature ranges from 25 °C in July to 32 °C in February, with a mean of 26 °C (Iloeje 1981; NIMET 2012). The research area's average annual rainfall and temperature are 1500 mm and 27 °C, respectively, with 250–500 mm in the dry season (Iloeje 1981). 830 mm, 60 percent, and 290 mm, respectively, represent the runoff, runoff coefficient, and projected groundwater contribution to recharge. As a result of the study area's nature (highly and steeply rocky terrain) and modest cultivation, runoff in the study area would be high.

Fig. 2
figure 2

Surface elevation map of the study area

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The steep topography enhances drainage, although the amount of runoff that contributes to groundwater recharge is minimal (Delleur 1999). People in the study area are public servants (such as teachers and local government employees) and farmers who engage in trading activities by selling their farm produce both within and outside the town. Many streams that are tributaries of the Ogbeese and Oda rivers drain the area. During the rainy season, these rivers and their tributaries provide the town with water. However, these water sources in the area are insufficient to support the needs of the inhabitants. The area's principal hydrogeological units are the weathered basement and jointed/fractured basement aquifers. In the tropical environment, high rainfall intensity/frequency and extent result in deep/high weathering processes, resulting in overburden with different degrees of porosity and permeability. The potential water bearing rocks in this unit with fractured aquifers (with obvious discontinuity) are capable of producing a reasonable volume of water for household, agricultural, municipal, and industrial uses.

The research region lies in southwestern Nigeria's Basement Complex, which has been widely investigated (Obaje 2009). The 600 Ma Pan-African orogeny impacted the Nigerian basement (Fig. 3), which now occupies the reactivated zone created by plate collision between the West African craton’s passive continental margin and the active Pharusian continental margin (Obaje 2009). At least four major orogenic cycles of deformation, metamorphism, and remobilization are thought to have produced the basement rocks, corresponding to the Liberian (2700 Ma), Eburnean (2000 Ma), Kibaran (1100 Ma), and Pan-African cycles (600 Ma). The first three cycles were marked by substantial deformation and isoclinal folding, which was followed by regional metamorphism and then extensive migmatization.

Fig. 3
figure 3

Geology map of Akure area showing the study area (modified after NGSA, 2006)

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In the Nigerian basement and study region, the Migmatite—Gneiss Complex is the most common of the component units. Migmatites, orthogneisses, paragneisses, and a sequence of basic and ultrabasic metamorphosed rocks make up the diverse assemblage. Many of the constituent minerals of the Migmatite—Gneiss Complex were recrystallized by partial melting during Pan-African reworking, according to petrographic data, with the bulk of rock types demonstrating medium to higher amphibolite facies metamorphism. The ages of the Migmatite—Gneiss Complex range from Pan-African to Eburnean.

Migmatite (M); charnockite (Ch); older granite (OGu); fine to medium grained biotite—muscovite granite (OGf); and coarse to porphyritic biotite—hornblende granite (OGf) are the principal lithologic units found in the area (Fig. 3). (OGp). Early gneiss, mafic and ultramafic bands, and granitic or felsic components make up the migmatite-gneiss. These are the most common rock types found in the research region. Quartz, alkali feldspars, plagioclase, orthopyroxene, clinopyroxene, hornblende, biotite, and a little quantity of opaque mineral apatite, zircon, and allanite make up the charnockitic rocks that occur in conjunction with earlier granite (under hand lens).

The rocks are foliated with main minerals of biotite, quartz and feldspar. The granite and granite gneiss, charnockite are weakly foliated. The gneissic portion of the migmatite in the area has well developed banding of leucocratic/melanocratic minerals.

Data and methodology

Electrical geophysical survey, pumping test, and groundwater static water level measurement were used in this study, Fig. 4 depicts the data acquisition map. The electrical resistivity method can be used to determine aquifer borders, stratigraphy, and faults, as well as to estimate hydrogeological parameters, demarcate contaminated areas, and determine the extent or degree of water intrusion.

Fig. 4
figure 4

Data acquisition map for the study showing locations of VES and well points where pumping test data were obtained

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The area was initially geologically surveyed, with important units being noted. Following that, the area was gridded into separate units with the use of a global positioning system (GPS). The GPS was also utilized to record the location of the wells, boreholes, and geophysical survey in precise detail. Vertical electrical sounding was used in the electrical resistivity geophysical investigation (VES). The electrical resistivity of soil and rock materials is a fundamental electrical attribute that is intimately related to their lithology (Agyemang 2021; Telford et al. 1976).

The VES was used in this study, while detailed description of this method is available in Ramanuja (2012), Poongothai and Sridhar (2017), Umar and Igwe (2019), Bayewu et al. (2018), Using an Ohmega resistivity meter, the VES acquired resistivity data using the Schlumberger array with a maximum electrode separation or current spread of 250 m. The electrode array and spread that were used were critical in acquiring correct subsurface geological data. The VES data were displayed as depth sounding curves, and the partial curve matching approach was used to quantitatively evaluate the data (Loke 1997; Aina et al. 2019). The resulting field resistance data was multiplied by the geometric factor to get apparent resistivity values. After that, the data was plotted on a bi-logarithm graph sheet against half electrode spacing. Then, utilizing resist software, they were subjected to partial curve matching and computer iterative modeling (1-D forward modeling) (Zohdy 1974; Markus et al. 2018). The resistivity readings were interpreted using the method of Idornigie et al. (2006), which is displayed in Table 1, with minor modifications depending on the geology of the area and previous experience. The VES stations were chosen based on geology, terrain/topography, the presence of existing water wells/boreholes, and accessibility.

Table 1 Lithological interpretation from apparent resistivity data
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The nature and thickness of the overburden, fracture contrast, reflection coefficient, formation factor, traverse resistance, hydraulic conductivity, transmissivity, and geology were all combined to create a groundwater potential map for the area (Bayewu et al. 2018; Robinson and Coruh 1988). The best groundwater yield is frequently found where the cracked basement underlies rather thick overburden (Oladapo and Akintorinwa 2007). Equation 3 was used to get the reflection coefficient.

$$r= \frac{\left({\rho }_{n}- \rho \right)(n-1)}{{\rho }_{n}+ \rho (n-1)},$$
(3)

where r is reflection coefficient, \({\rho }_{n}\) is the layer resistivity of the nth layer and \(\rho (n-1)\) is the layer resistivity overlying the nth layer. The fracture contrast was calculated using Eq. 4.

$${F}_{c}=\frac{{\rho }_{n}}{{\rho }_{n}-1},$$
(4)

The traverse resistance was calculated using Eq. 5:

$$T=\sum_{i=1}^{n}{\rho }_{i}{h}_{i},$$
(5)

where T is traverse resistance, \(\rho \) and h are resistivity and thickness of the nth layer respectively.

The vadose zone/overburden material's vulnerability or protective capacity to pollution/contamination was determined using the longitudinal conductance and aquifer vulnerability index (AVI). Texture, structure, thickness organic carbon content, clay mineral content, permeability, geology and other hydrogeologic intrinsic qualities are all reflected in these methods (Stempvoort et al. 1993a, b; Focazio et al. 2002; Obiora et al. 2016). Using the geoelectrical parameters, the longitudinal unit conductance (Eq. 6) was utilized to predict the water's contamination susceptibility (Bhattacharya and Patra 1968; Loke 1997).

$$LC = \sum_{i}^{n}\frac{{h}_{i}}{{\rho }_{i}},$$
(6)

where LC is longitudinal conductance, \({h}_{i}\) and \({\rho }_{i}\) are the thickness and resistivity of \({n}^{th}\) layer, respectively. The AVI method measures hydraulic resistance (c) to vertical flow (Kruseman and de Ridder 1994; Stempvoort et al. 1993a, b) using the thickness of the water bearing units and hydraulic conductivity as shown in Eq. 7.

$$c= \sum {d}_{i}/{K}_{i},$$
(7)

for layers 1 to i. The interpretation of “c” was done using Table 2. In addition, data were gathered from existing boreholes, including two government-drilled boreholes and fifty (50) water wells. The static water level was calculated using these wells. Some of the VES (i.e. 15) were located or conducted in these well locations for the purpose of correlation.

Table 2 Relationship of hydraulic resistance (c) and aquifer vulnerability index
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Pumping tests were conducted in all fifteen existing wells in the area, which span five different geological units: charnockite, older granite, migmatite, fine to medium biotite granite, and coarse to porphyritic biotite-hornblende. The pumping test was carried out with a 1-horsepower submersible pump, and the time it took to fill a reservoir tank with 2000 L was recorded, taking into account the drawdown (Birsoy and Summer 1980; Brassington, 1988). Depending on the yield/response to abstraction, the pumping time/period ranged from 2 to 6 h (Matthess 1982; Mazac et al. 1990). Using mathematical equations, the results of this test were utilized to calculate hydraulic conductivity and transmissivity (Eq. 8):

$$T=Kh,$$
(8)

where T is transmissivity, K is hydraulic conductivity, and h is thickness of the water bearing unit. For each of the geological units in the area, the Formation factor (Fm) was calculated. The Fm takes into account all of the material's qualities that influence electrical current flow, such as diagenetic cementation, pore geometry, and porosity (Mazac et al. 1985). The obtained Fm was correlated (r2) with hydraulic conductivity in this investigation (Mazac et al. 1985). Equation 9 was used to calculate the Formation factor. The conductivity (in mhos) of the water at the site was first measured and converted to ohm-m using a conductivity meter. The hydraulic conductivity of aquifers with no well/borehole data was computed using the average Fm derived for each of these geological units. Since a regression expression was built between the two; for each of the formations, transmissivity was estimated as well.

$$ F{\text{m}} = \frac{{\text{average aquifer water resistivity}}}{{\text{resistivity of water at site}}}. $$
(9)

The hydrogeological investigation includes static water level and hydraulic head determination from fifty one (51) non flowing wells across all geological units in the area, using steel tape with lower end marked with carpenter’s chalk. The measurement was taken twice, while the average values were recorded.

Results and discussion

Geoelectric characteristics and water bearing unit delineation

The overview of the geoelectric characteristics collected for the research area is shown in Table 3 and Fig. 5. The VES data revealed seven (07) different types of curves, ranging from three geoelectric layers [A (16.9%), H (18.3%), and K (8.5%)] to four layers [QH (1.4%), HK (1.4%), and KH (52.1%)] to five layers [HKH (1.4%)]. The KH curve has the highest percentage of VES curves, accounting for 52% of all VES curves. Due to the variability of subsurface composition, the variety of curve types obtained is identical with lateral and vertical facies alteration (Idornigie et al. 2006). The interpretation of the curves implies that the weathered layer is the dominant hydrogeological unit in the area. Furthermore, the existence of an A-curve type could indicate a shallow bedrock depth in some places. Topsoil resistivity in the area ranges from 37 to 2363 Ω m, with an average (avg.) of 207 Ω-m. The topsoil is between 0.5 and 4.9 m thick (avg. of 1.6 m). Clay, sandy clay, clayey sand, and sand make up the topsoil.

Table 3 Summary of geoelectrical (VES) parameters
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Fig. 5
figure 5

Curve types obtained from VES

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A layer of sandy clay/clayey sand/sand lies beneath the topsoil, with resistivity ranging from 96 to 3561 Ω m (avg. 1060 Ω m) and thickness ranging from 1.2 to 21.2 m. (avg. of 6.0 m). This layer is unique to curve types KH, QH, and HK. The resistivity of the weathered layer that makes up the principal water bearing unit (Fig. 6) ranges from 38 to 2232 Ω m (avg. of 301 Ω m) signifying a clayey sand weathered layer, with possibility of limited storage and supply capacity (Matthess 1982; Schwartz and Zhang 2003). The thickness varies between 1.0 and 33.3 m (avg. 14.1 m), indicating a reasonably thick water holding layer. The weathered layer can be found at depths ranging from 0.5 to 21.2 m. The weathered layer’s iso-resistivity map (Fig. 6b) revealed a prominent resistivity in the 100–300 Ω m range, corresponding to a sandy clay water bearing unit, whereas resistivity above 700 Ω m (sand aquifer) was observed in the migmatite/older granite environment in the southern part. The isopach map of the weathered layer (Fig. 6a) likewise shows that the southern regions have thin thickness, whereas the northern parts have thickness in the range of 10–30 m. On the southwestern flank, a tiny thickness—closure of more than 30 m has been identified. Under VES 32, a confined basement aquifer with a resistivity of 226 Ω m and a thickness of 23.1 m was identified. In the meantime, the depth of this layer is 39.3 m. The partly fractured/basement rock has a resistivity of 101 to 9652 Ω m, and the basement depth ranges from 2.1 to 47.5 m. (avg. of 20 m). The resistivity of pore water varies from 25.58 to 71.94 Ω m (49.37 Ω m avg.)

Fig. 6
figure 6

Spatial map of the weathered layer (a) thickness (b) resistivity

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The overburden thickness of all VES curves ranges from 2.1 to 47.5 m (Fig. 7), with an average of 19.6 m. Overburden thicknesses of 4.7–27.3 m (13.7 m avg.), 5.2–11.0 m (8.1 m avg.), 2.1–27.8 m (9.8 m avg.), 8.2–47.5 m (23.6 m avg.), 4.0–44.1 m (25.8 m avg.) and 4.0–44.1 m (25.8 m avg.) were obtained for Ch, OGu, M, OGe, and OGp respectively. The high OGp values could be related to colored (melanocratic) minerals' high vulnerability to weathering and erosion, resulting in clayey products. In reality, the presence of a thick aquiferous geologic unit does not imply a high water yield, as essential parameters such as resistivity, hydraulic gradient, sorption, hydraulic conductivity, transmissivity, and so on still play a role.

Fig. 7
figure 7

Overburden map of the study area showing a predominant thickness between 15 and 30 m

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Table 4 shows that the traverse resistance (TR) ranges from 214.5 to 30,307.7 Ω m2 (avg. 7668.5 Ω m2). Aquifer transmissivity and traverse resistance can be correlated (Asaad et al. 2004). Transmissivity increases as the TR increases. The average values found for different geologic units in the area: charnockite, older granite, migmatite, biotite granite, biotite—hornblende granite are 9492.78 Ω m2, 2076.45 Ω m2, 3650.64 Ω m2, 11,836.04 Ω m2, and 7638.46 Ω m2; with biotite -granite having the highest value. The fracture coefficient (Fc) for charnockite (0.41–42.04; 10.87 avg.), granite (4.34–8.12; 6.23 avg.), migmatite (0.25–123.23; 23.73 avg.), biotite granite (0.3–96.11; 23.42 avg.) and biotite-hornblende granite (0.21–213.51; 30.12 avg.) The reflection coefficient (Rc) is also linked to groundwater yield, as a low Rc indicates a high-density water-filled fracture with a high yield/potential (Olayinka and Oladunjoye 2013).

Table 4 Summary of geoelectrical/hydraulics characteristics, vulnerability and groundwater potential index values of the Water Bearing units in the Study and
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Table 4 shows the Rc values obtained which vary from − 0.65 to 0.99. (0.69 avg.). The groundwater potential appears to be high based on Table 5 rating and overburden thickness. The Rc values for the geologic units are Ch. (− 0.42 to 0.95; 0.43 avg.), OGu (0.63–0.78; 0.71 avg.), M (− 0.60 to 0.98; 0.71 avg.), OGe (− 0.54 to 0.98; 0.72 avg.) and OGp (− 0.65 to 0.99; 0.71 avg.). The spatial distribution of Fc and Rc over the study area (Fig. 8) indicates moderate to high groundwater potential, as both indices have significant overlap in their values throughout the study area, with no clear separation. The biotite-hornblende granite-derived soil exhibits a significant fracture contrast, implying a high groundwater potential, based on these data. The groundwater formation factor (Fm) ranges from 2.35 to 13.61, with average values of 13.61 for charnockite, 2.35 for granite, 3.68 for migmatite, 5.11 for biotite granite, and 4.79 for biotite-hornblende granite. Based on the value of Fm, the charnockite appears to have a high groundwater yield/potential.

Table 5 Longitudinal unit conductance, overburden thickness, and reflection coefficient with corresponding protective rating (modified after Oladapo and Akintorinwa 2007)
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Fig. 8
figure 8

Spatial distribution of Fc and Rc across the study area

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Water bearing unit/aquifer hydraulic characterization

The SWL of wells (Table 6) ranges from 1.4 to 9.9 m (6.41 m avg.), while hydraulic head ranges between 341 to 371 m (352 m avg.). The thickness of the vadose zone which corresponds to the static water level (SWL) is generally higher in the north and relatively lower in the south, while the southwestern flank is characterized with values less than 5 m (Fig. 9a). Consequently, the southern part is likely to be vulnerable to contamination than northern area. The hydraulic map (Fig. 9b) shows a decreasing north–south trend, which implies that groundwater movement is southward, and the hydraulic gradient is 9 m.

Table 6 Summary of well information and sample locations
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Fig. 9
figure 9

Spatial distribution of (a) thickness of the vadose zone/SWL (b) groundwater hydraulic head

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The hydraulic conductivity determined by pumping gives values ranging from 0.21 to 1.22 m/day and average of 0.67 m/d; while those recorded for the geologic units are charnockite: 0.21–0.68 m/day (0.32 m/day avg.); granite: 0.94–1.22 m/day (1.08 m/day avg.); migmatite: 0.59–0.72 m/day (0.63 m/day avg.); biotite granite: 0.8–0.95 m/day (0.86 m/day avg.); biotite-hornblende granite: 0.54–0.66 m/day (0.60 m/day avg.). All these values fall within the hydraulic conductivity (material) classification of clay-sand-gravel mixtures (Bouwer 1978) of moderate groundwater yield.

The values of transmissivity (Table 4) derived from the area range between 0.59 and 24.2 m2/d, while the average is 9.82 m2/d. From Fig. 10, higher values of transmissivity (> 8 m2/day) and hydraulic conductivity (> 0.6 m/day) are observed in the northern/central part, however, decreases towards the southern part (< 8.0 m). The average transmissivity of the water bearing units for different geological rocks are in the following (descending) order: 13.21 m/day (biotite granite), 12.24 m/day (biotite-hornblende granite), 8.14 m/day (granite), 4.644 m/day (charnockite) and 4.641 m/day (migmatite). The high values of coefficient of permeability and transmissivity recorded for granite and biotite and/or hornblende rich granitic rock could be attributed to their texture as they are generally of medium—coarse—porphyritic texture. These textural characteristics could enhance infiltration capacity, porosity, storability, permeability, and transmissivity.

Fig. 10
figure 10

Distribution of hydraulic conductivity and transmissivity across the study area

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Aquifer vulnerability assessment

The calculated longitudinal unit conductance values (in mhos) of water bearing units using resistivity parameters are shown in Table 4, and the values range from 0.0118 to 0.6575 mhos with an average of 0.1470 mhos. Total longitudinal unit conductance greater than one are usually associated with impervious materials, which offer good/excellent protectivity and vice-versa. Hence using Table 5, the groundwater protective capacity varies from poor–moderate, while taking the average value, the protective capacity is weak.

The calculated AVI values range from 0.23 to 1.74, with an average of 1.22. Therefore using Table 2, the groundwater in the study area is extremely vulnerable to contamination. The relationship or correlation of hydraulic conductivity (K) taking as dependent variable and Formation factor (Fm) as independent variable for the different lithological units in the study area gives the following exponential expressions, and correlation coefficient (r2) shown in Table 7. The relationship shows weak positive (< 0.5) for all the rock units.

Table 7 Empirical relationship between hydraulic conductivity and formation factor for different water bearing formations derived geological units
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Synthesis of results/hydrogeological parameters modeling

Modeling of groundwater potentiality zones using groundwater potential index value (GWPIV) by aquifer hydrogeological properties, is a veritable scheme for effective management of groundwater resources, as demonstrated by Bawallah et al. (2021), Epuh et al. (2020), Adebiyi et al. (2018); Adewumi (2016), (Mogaji 2016). Therefore using multi criteria parameters (as rated in Table 8) obtained from VES and pumping test hydraulics, the summation of these parameters resulted into generation of groundwater potential index values (GWPIV) which was used in developing groundwater potential map for the area. All the parameters: weathered layer thickness (WT), weathered layer resistivity (WR), overburden thickness (OT), traverse resistance (TR), transmissivity (TM), reflection coefficient (RC), fracture contrast (FC), and formation factor (FM) were summed up (Eq. 8) by attaching different weights (w) and ratings (r) based on their significance on groundwater accumulation/storage and exploitation (Table 8).

Table 8 Muti-criteria parameters and its probability rating and weights for selected measured parameters in relation to groundwater evaluation
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The geology was rated/weighted based on influence on groundwater occurrence, texture, structure (field observation of fracture, discontinuity), end product of weathering, resistance to weathering, and mineralogy

$$\mathrm{GW}=f\left(\mathrm{WT},\mathrm{ WR},\mathrm{ OT},\mathrm{ TR},\mathrm{ TM},\mathrm{ RC},\mathrm{ FC},\mathrm{ GL}\right).$$

Therefore the GWPIV was determined using the expression below:

GWPIV = \({\mathrm{WT}}_{w}{\mathrm{WT}}_{r}+ {\mathrm{WR}}_{w}{\mathrm{WR}}_{r}+ {\mathrm{OT}}_{w}{\mathrm{OT}}_{r}+{\mathrm{TR}}_{w}{\mathrm{TR}}_{r}+{\mathrm{TM}}_{w}{\mathrm{TM}}_{r}+ {\mathrm{RC}}_{w}{\mathrm{RC}}_{r}+ {\mathrm{FC}}_{w}{\mathrm{FC}}_{r}.\)

The GWPIV was rated as low: 0.0–4.0; moderate: 4.0–6.0; and high: 6.0–10.0. Consequently the GWPIV obtained ranges from 2.80 (VES 70)–7.73 (VES 56) with an average of 5.04 indicating a moderate potential. The developed groundwater potential map (Fig. 11) showed predominant moderate potential with aquifers underlain by older granite, coarse to porphyritic biotite-hornblende granite, and migmatite have better hydrogeological significance in the study area.

Fig. 11
figure 11

Groundwater potential map generated from the GWPIV

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Conclusion

The study demonstrated the significance of integrated methods in groundwater assessment/aquifer delineation; especially in the basement complex of southwestern Nigeria where groundwater yield and acculation depends on many indices/factors. Consequently the VES was complemented with in-situ pumping test and hydrogeological parameters measurement coupled with detailed geological mapping. The study showed that the area exhibits low–moderate groundwater potential with high risk of contamination. The weathered layer (major) and confined fracture basement (minor) are the water bearing units, with the weathered layer average resistivity of 301 Ω m signifying clayey-sand signature. The confined fracture aquifer was not widespread as it was only delineated under VES 32. In conlusion subsoil underlain by older granite, coarse to porphyritic biotite-hornblende granite, and migmatite have better hydrogeological importance in the study area, while formation factor and hydraulic conductivity showed weakly positive correlation coefficient. Based on the model map, which synthesis all the hydrogeological parameters, the groundwater potentiality of the study area is generally moderate with thin vadose zone highly vulnerable to contamination with sand-clay composition.