Applied Water Science

, 8:49 | Cite as

Investigation of the influence of lineaments, lineament intersections and geology on groundwater yield in the basement complex terrain of Ondo State, Southwestern Nigeria

  • Francis O. Akinluyi
  • Martins O. Olorunfemi
  • Oyelowo G. Bayowa
Open Access
Review Article
  • 473 Downloads

Abstract

The influence of lineaments, lineament intersections and geology on the groundwater yield of the basement terrain of Ondo State was investigated using optical remote sensing data, Aster DEM, geology, and borehole yield data. Landsat-7 ETM+ and Aster DEM were processed to generate composite lineament map. The study area was traversed by five (5) main lineament populations trending N–S, NE–SW, E–W, ENE–WSW, NNW–SSE. Boreholes sited on lineament exhibited a yield range of between 0.8 and 1.28 l/s with an average yield of 1.04 l/s. Boreholes sited close to lineament gave groundwater yield values of between 0.5 and 1.28 l/s and an average yield of 1 l/s, while boreholes located outside lineament gave groundwater yield range of between 0.2 and 1.26 l/s with an average yield of 0.98 l/s. The investigation of the hydrogeological characteristics of the lithologies by superimposing the yield data showed average yield of 0.98 l/s for migmatite gneiss biotite granite undifferentiated (M), 1.01 l/s for porphyritic granite (OGp), 1.03 l/s for medium- to coarse-grained (OGe), 1.17 l/s for pelitic schist undifferentiated (Su), 1.24 l/s for quartz schist and quartzite (Eq), 1.12 l/s for older granite undifferentiated (OGu), 0.5 l/s for slightly migmatised medium-grained granite-gneiss (gg) and 1.23 l/s for fine-grained flaggy quartzite and schists (Sf). The study concluded that borehole data located on or near lineaments or at intersection of lineaments gave higher yields more than those located before lineaments or outside lineaments, while quartz schist and quartzite exhibited the highest average groundwater yield of all the lithological units.

Keywords

Lineament Lineament intersection Lithology Groundwater yield Crystalline basement complex 

Introduction

Basement aquifers constitute relevant sources of water in the hard rock terrain of Ondo State in which groundwater occurs mainly in fractured zones, with or without saturated weathered zones (Olorunfemi and Olorunniwo 1985; Olorunfemi 1990; Olorunfemi et al. 1991; Olorunfemi and Okhue 1992; Idornigie and Olorunfemi 1992; Olorunfemi and Fasuyi 1993; Olorunfemi 2007; Ekwe et al. 2010; Bayowa et al. 2014; Akinlalu et al. 2017). However, the development of crystalline bedrock aquifers is highly complex, and groundwater occurrence is spatially variable due to many factors (Satpathy and Kanungo 1976; Mabee et al. 1994; Mabee 1999; Mabee et al. 2002; Ojo et al. 2015; Aladejana et al. 2016). Though tectonic history, geomorphology and climatic conditions play prominent role in the development of weathered and fractured aquifer systems, in this kind of terrain, water percolation and accumulation is essentially controlled by fractures and other rock discontinuities (Lattman et al. 1964; Siddiqui and Parizek 1971; Edet et al. 1994; Edet 1996; Sander 1997; Taylor and Howard 2002, Anifowose et al. 2012; Akinlalu et al. 2017). Therefore, knowledge of some surficial features such as lineament (originated possibly by faults, fractures, joints, foliation, etc.) in an area underlie by Basement Complex rocks for site selection prior to localized geophysical surveys could maximize search for developing productive-well representing positions of potential deformation zones in bedrocks. Hence, the surficial features can be used as surface indicators to the sub-surface fracturing and weathering which are essential for high yielding productive aquifers in hard rocks.

Optical remote sensing with the availability of multi-spectral and progressive development in the image processing techniques provides the opportunity to prepare relatively more reliable and comprehensive lineament maps. The wide ground coverage and relative high resolution with respect to scale presented by the satellite images, also enables regional and local lineament analysis (Masoud and Koike 2006; Nag et al. 2011; Adiat et al. 2012; Dasho et al. 2017). Although there have been significant approaches for the evaluation and automatic detection of the lineaments and curvilinear features from satellite images (Cross 1988; Taud and Parrot 1992; Hardcastle 1995; Odeyemi et al. 1999; Henriksen and Braathen 2005, Yene et al. 2015; Lobatskaya and Strelchenko 2016, Ilugbo et al. 2017), however, semi-automatic extraction combining the expert human nature and computer algorithm is an asset for lineament detection and interpretation (Mallast et al. 2011; Vaz et al. 2012; Vasuki et al. 2014; Middleton et al. 2015; Bonetto et al. 2017).

This study intends to investigate the influence of lineaments, lineament intersections and geology on the groundwater yield in the Basement Complex terrain of Ondo State. The result will provide enhanced knowledge and improved understanding of the hydrogeological characteristics for evaluating groundwater potentiality in the study area and the world as a whole.

The study area and geological setting

The study area (Fig. 1) lies between latitudes 5°27′ and 8°09′ N and longitudes 4°00′ and 6°00′ E. It covers a geographic area of approximately 105, 239.49 sq km. It is bounded in the East by the Edo State, in the West by Ogun, and Osun and in the North by Ekiti and Kogi States. It is composed of some prominent hills found in Idanre and Akoko which rise to over 250 m above sea level (m.a.s.l).
Fig. 1

Administrative map of Ondo State (modified from administrative map of Ondo State published by the office of the surveyor-general of the State, 1998)

Ondo State is underlain by both Precambrian rocks and sedimentary formations (Fig. 2). The Basement Complex underlies the northern part (60%) of the state. The Precambrian crystalline rocks consist of metamorphic and igneous rocks with isotopic ages greater than 300–450 Ma (Ajibade and Fitches 1988). They are mainly composed of biotite granites, porphyritic granites, gneisses, schists, pelitic schists, quartzites, schistose quartzites and charnockites. Some of the metasediments are also intruded by pegmatites. The granites are part of the older granites of the Dahomeyan Embayment. The Basement rocks outcrop in many localities in the state. They occur as isolated hills and inselbergs which stand out against low-lying surroundings. The granite rocks are mostly made up of feldspathic porphyries that are generally aligned along the N–S direction at Idanre and Ikare, where they assume batholithic dimensions.
Fig. 2

Digitized generalized geology of Ondo State (adapted from the nigerian geological survey agency (NGSA) map, 1966)

Materials and methods

Three Landsat ETM+ scenes of paths/rows: 189/055, 190/055 and 190/056, Aster DEM, Ondo State administrative and topographical maps at 1:250,000 scales were acquired. In order to reduce processing and computational time during digital image processing, a vector map representing the geomerty of the study area was created to subset all the acquired data and Landsat ETM+ was pre-processed for haze reduction, noise filtering, re-sampling and ortho-rectification. For maximum utilization of multi-spectral Landsat ETM+, optimum index factor (OIF) was carried out using Ilwis 3.7.2 to select the optimum combination of three bands out of all possible 3-band combinations to create a color composite. Bands 457 contain the highest OIF and used to create false colour composite while band 7 being a geologic band was subjected to linear stretching and spatial enhancement filters, spectral enhancement of Landsat ETM+ bands (principal components analysis, PCA), the Aster DEM to topographic analysis such as sink fill, shielded relief and image fusion. All of these processes were then integrated to generate lineament map. The extracted lineaments were validated by overlaying the lineaments on high resolution Google Earth image and topographical maps to expunge anthropogenic linear/curvilinear features of non-geologic origin. The output of the corrected lineaments from high resolution Google Earth image and topographical maps was further processed for hydrogeological analysis whereby non-hydrogeologic significant lineaments lying on DEM highs were removed. Hydro-lineament and hydro-intersection maps were then sub-mapped from the general lineament map. Hydrogeologic characteristics of the generalized geology and hydro-lineament, intersection of the basement complex area were carried out using superimposition function with the groundwater yield data 92 borehole yield data that were used for this characterization.

Results and discussion

Lineaments mapping

Figure 3 shows bands 754 color composite of Landsat-7. Visually, it reveals land cover classes ranging from vegetations, linear features, built-ups, roads, soil/rock outcrops, and aeroplane track. Linearly stretched gray scale band 7 ETM+ (Fig. 4) reveals some linear/curvilinear structures which are particularly visible in the centre and northwest of the study area. While Fig. 5a–f show kernels and output raster images of spatial filters. These arrays of spatial frequency filters (i.e., edge enhancement, high pass, Laplace, cross edge enhancement, vertical and low pass) suppress and emphasize structural appearance of topographic/geologically induced linear/curvilinear features. Principal components (PC1 to PC6) are shown in Figs. 6a–f. It depicts varying degrees of spectral information content inherent in bands 1, 2, 3, 4, 5, and 7 (Table 1). Table 1 shows the transformation coefficients of the set of input bands. The columns correspond to the six principal components and the rows correspond to the six bands. It depicts the factor score (eigenvector) contributed to the individual components. Higher values indicate a greater factor loading, i.e., that band is contributing more to that component. This is the same regardless of sign, i.e., a high negative value also indicates a high factor loading. Table 2 shows the decreasing variance in successive principal components. As can be seen from the eigenvalues (Table 2), the first three components made 96.79% of the available variance and carry most of the spectral information in the total data set available for analysis while the other shows noise in the data. For geomorphic linear features, generated Hill-shaded map is shown in Fig. 7. It reveals lows and highs of different geomorphic linear features. This represents both natural and man-made linear features. Ambiguities in the interpretation of hill-shaded relief map for verifying and discarding geologic and non-geologic linear features were reduced by comparing it with other images generated and fused image results. Figure 8 shows the general lineament map in the basement terrain study area of Ondo State. The spatial distribution of lineaments expresses itself in terms of topography in the study area, which manifested as straight stream valley, sudden changes in flow direction of drainage patterns, contrasting tone, straight/curvilinear ridges and alignment of vegetation (Mabee et al. 1994; Masoud and Koike 2006; Ekwe et al. 2010; Adiat et al. 2012; Akinluyi 2013; Dasho et al. 2017). Figure 8 depicts a characteristic feature of the occurrence of underlying structures in the basement complex of Ondo State. It shows the fracture distribution and pattern in the study area on a scale of 1:200,000 as exhibited in each of the sixteen (16) local government areas (LGA). The central and north-eastern parts of the study area present a structurally complex landscape around the Idanre (Owena), Oke Agbe, Ikare, Ido-ani, Oba and Isua Akoko. High concentration of lineaments of topographic highs origin is evident in the rugged terrain of Idanre local government area (LGA) and Akoko area particularly in Akoko northwest local government area (LGA). Therefore, Fig. 8 play a significant role as one of the criteria used during evaluation of groundwater potential, engineering studies, erosion risk mapping, landfill characterization, etc. The Rose diagram (Fig. 8b) shows that the study area is traversed by five (5) main lineament populations trending N–S, NE–SW, E–W, ENE–WSW, NNW–SSE. The large spread in the azimuths of the lineaments is due to the polycyclic history as well as the brittle nature of the migmatites, gneisses and quartzites which together constitute the major lithology of the basement complex rocks of Ondo State. The N–S lineament trend pattern coincided with the general N–S foliation strike emplacement in the basement complex of Nigeria. This invariably implies that these lineaments might have resulted from plate movements and tectonics in the region.
Fig. 3

Bands 457 false color composite of the study area

Fig. 4

Linear stretch of Band7 of landsat-7 ETM+

Fig. 5

a–f Shows kernels and output raster images of anthropogenic and topographic structures

Fig. 6

a–f Decreasing quality of principal component images of basement complex

Table 1

Eigenvectors of principal component analysis results

 

Eigenvectors

P1

P2

P3

P4

P5

P6

Band 1

0.0927

− 0.7969

0.2293

0.5440

− 0.0536

− 0.0706

Band 2

0.1401

− 0.3929

0.0664

− 0.5152

0.1866

0.7220

Band 3

0.2652

− 0.2539

0.2183

− 0.5777

0.1863

− 0.6701

Band 4

− 0.1166

− 0.3355

− 0.7609

− 0.2140

− 0.4851

− 0.1173

Band 5

0.6336

0.0559

− 0.5188

0.2429

0.5169

− 0.0051

Band 6

0.6974

0.1745

0.2173

− 0.0055

− 0.6520

0.1041

Table 2

Eigenvalues of principal component analysis results

Components

1

2

3

4

5

6

Value

341.27

72.41

25.02

8.28

4.31

1.94

Eigenvalues (%)

75.30

15.98

5.52

1.83

0.95

0.43

Fig. 7

Hill-shaded relief map of the study area

Fig. 8

a Lineament map and b rose diagram of the study area

Geology, hydro-lineament/intersection and groundwater yield in the basement complex area of Ondo state

Figure 9 shows the spatial distribution of the borehole yield data on the generalized geological map of the study area. The ninety-two groundwater yield data fall on eight lithological units as shown in Fig. 9 and Table 3. These lithological units and the range of borehole yield (in bracket) are migmatite gneiss undifferentiated (M: 0.2–1.28 l/s), porphyritic granite (OGp: 0.6–1.28 l/s), medium-to-coarse-grained biotite granite (OGe: 0.5–1.24 l/s), pelitic schist undifferentiated (Su: 1.1–1.2 l/s), quartz bschist and quartzite (Eq: 1.23–1.24 l/s), older granite undifferentiated (OGu: 0.74–1.26 l/s), slightly migmatised medium-grained granite-gneiss (gg: 0.5 l/s) and fine-grained flaggy quartzite and schists (Sf: 1.23 l/s). Within the limits of the groundwater yield data, quartz schist and quartzite and flaggy quartzite have the highest average groundwater yield followed by older granite/pelitic schist (which is suspected to be pegmatitic); porphyritic/coarse-grained granite and lastly by migmatite/granite-gneiss. The relatively high groundwater yield observed on quartzite is not unconnected with the relatively high fracture (lineament) density with consequently high permeability. Figure 10 shows the overlay of borehole location on the hydro-lineament map. Table 4 presents groundwater yield characteristics on lineament, close to lineament and outside lineament obtained on a scale of 1:100,000. Boreholes sited on lineament exhibit a yield range between 0.8 and 1.28 l/s with an average yield of 1.04 l/s. Boreholes sited close to lineament gave groundwater yield values between 0.5 and 1.28 l/s and an average yield of 1 l/s, while boreholes located outside lineament gave groundwater yield range between 0.2 and 1.26 l/s with an average yield of 0.98 l/s. This shows that boreholes located on lineaments are expected to give relatively high groundwater yield due to the enhanced groundwater flow through the lineaments. Similar experiences have been reported by Edet et al. (1994), Edet (1996), Adiat et al. 2012, Yene et al. (2015), Aladejana et al. (2016). Figure 11 shows the spatial distribution of boreholes on the hydro-intersection map. Table 5 shows groundwater yield characteristics of hydro-lineament intersection with respect to on intersection, close to intersection and outside intersection. The borehole located on lineament intersection gave the highest average yield of 1.28 l/s. The next highest average groundwater yield of 1.01 l/s was recorded from boreholes located close to lineament intersections, while those located outside the lineament intersections gave the lowest average of 1 l/s. The analysis in Tables 4 and 5 corroborated the works of Lattman and Parizek (1964), Sander 1997, Mabee et al. (2002), Bayowa et al. 2014, Dasho et al. (2017), that wells located on or near lineaments or at the intersection of lineaments gave yields greater than wells placed between lineaments and outside lineaments. Siddigui and Parizek (1971), Mabee (1999), Bayowa et al. (2014), Adiat et al. (2012), Yene et al. (2015) and Ilugbo et al. (2017) also found that wells sited on lineament had productivity greater than randomly located wells.
Fig. 9

Overlay of yield data on generalized geology of the study area (adapted from the nigerian geological survey agency (NGSA) map, 1966)

Table 3

Hydraulic characteristics of some lithological units

Lithology

Percentage distribution of yield data on lithology (%)

Yield range (l/s)

Average yield (l/s)

Migmatite gneiss undifferentiated (M)

72

0.2–1.28

0.98

Porphyritic granite (OGp)

8

0.6–1.28

1.01

Medium- to coarse-grained biotite granite (OGe)

5

0.5–1.24

1.03

Pelitic schist undifferentiated (Su)

3

1.1–1.2

1.17

Quartz schist and quartzite (Eq)

2

1.23–1.24

1.24

Older granite undifferentiated (OGu)

5

0.74–1.26

1.12

Slightly migmatised medium-grained granite-gneiss (gg)

1

0.5

0.5

Fine-grained flaggy quartzite and schists (Sf)

1

1.23

1.23

Fig. 10

Overlay of yield data on hydro-lineament map

Table 4

Hydraulic characteristics of hydro-lineament map

Borehole location

Yield range (l/s)

Mean yield (l/s)

On lineaments

0.8–1.28

1.04

Close to lineaments

0.5–1.28

1

Outside lineaments

0.2–1.26

0.98

Fig. 11

Overlay of yield data on hydro-intersection map

Table 5

Hydraulic characteristics of hydro-lineament intersection map

Borehole location

Yield range (l/s)

Mean yield (l/s)

On intersections

1.28

1.28

Close to intersections

0.50–1.27

1.01

Outside intersections

0.20–1.28

1.00

Conclusions

This study evolved a systematic remote sensing and GIS-based methodology for mapping lineaments and sub-mapped it into hydro-lineament and hydro-intersection maps. It related lineament and lineament intersection and geology to groundwater yield in the basement complex terrain of Ondo State.

The remote sensing offers a synoptic view to study the large geographical areas. In our study, spatial statistical application for optimal band selection, multi-sensor data analysis and image fuse were demonstrated for mapping reliable geologically induced lineament patterns. The Ondo State basement complex area was characterized by five (5) main lineament populations trending N–S, NE–SW, E–W, ENE–WSW, NNW–SSE. The azimuths of the lineaments are due to the polycyclic history as well as the brittle nature of the migmatites, gneisses and quartzites which together constitute major lithology of the basement complex rocks of Ondo State. This result agrees with the N–S lineament trend pattern coinciding with the general N–S foliation strike emplacement in the basement complex of Nigeria.

Ninety-two borehole yield data overlaid on eight lithological units exhibit variable yield. These lithological units and the range of borehole yield are migmatite gneiss undifferentiated (M: 0.2–1.28 l/s), porphyritic granite (OGp: 0.6–1.28 l/s), medium-to-coarse-grained biotite granite (OGe: 0.5–1.24 l/s), politic schist undifferentiated (Su: 1.1–1.2 l/s), quartz schist and quartzite (Eq: 1.23–1.24 l/s), older granite undifferentiated (OGu: 0.74–1.26 l/s), slightly migmatised medium-grained granite-geniss (gg: 0.5 l/s) and fine-grained flaggy quartzite and schists (Sf: 1.23 l/s).

Boreholes sited on lineament exhibit yield range between 0.8 and 1.28 l/s with an average yield of 1.04 l/s. Boreholes sited close to lineament gave groundwater yield values between 0.5 and 1.28 l/s and an average yield of 1 l/s, while boreholes located outside lineament gave groundwater yield range between 0.2 and 1.26 l/s with an average yield of 0.98 l/s.

This study concluded that boreholes located on or near lineaments or at intersection of lineaments gave yields more than those located before lineaments or outside lineaments, while quartz schist and quartzite exhibited the highest average groundwater yield of all the lithological units. It, therefore, optimizes the hydrogeological investigation for predicting groundwater potential in terms of proximity to structural lineaments and hydro-lithological characteristics as a guide to siting productive boreholes and invariably reduces considerably the rate of failure during exploitation.

Notes

Acknowledgements

The authors express profound gratitude to Geotech consultancy services, Akure, Ondo State, Nigeria for providing access to their geophysical database to download part of vertical electrical sounding (VES) data used to carry out this research.

Compliance with ethical standards

Informed consent

Informed consent was obtained from all individual authors included in this study titled Investigation of the Influence of Lineaments, Lineament Intersections and Geology on Groundwater Yield in the Basement Complex Terrain of Ondo State, Southwestern Nigeria. We all agreed to every component of the manuscript.

References

  1. Adiat K, Nawawi M, Abdullah K (2012) Assessing the accuracy of GIS-based elementary multi criteria decision analysis as a spatial prediction tool. A case of predicting potential zones of suitable ground water resources. J. Hydrology 440–441:75–89.  https://doi.org/10.1016/j.jhydrol.2012.03.028 CrossRefGoogle Scholar
  2. Ajibade AC, Fitches WR (1988) The nigerian precambrian and the pan-african orogeny. In: Oluyide PO, Mbonu WC, Ogezi AE, Egbuniwe IG, Ajibade AC, Umeji AC (eds) Precambrian geology of Nigeria. Geological survey of Nigeria, Abuja pp, pp 45–53Google Scholar
  3. Akinlalu AA, Adegbuyiro A, Adiat KAN, Akeredolu BE, Lateef WY (2017) Application of multi-criteria decision analysis in prediction of groundwater resources potential: A case of Oke-Ana, Ilesa Area Southwestern Nigeria. NRIAG J Astron Geophys 6:184–200.  https://doi.org/10.1016/j.nrjag.2017.03.001 CrossRefGoogle Scholar
  4. Akinluyi FO (2013) Integration of remotely sensed and geophysical data in groundwater potential evaluation of the basement complex terrain of Ondo state, Southwestern Nigeria. Dissertation. Obafemi Awolowo University, Ile-IfeGoogle Scholar
  5. Aladejana OO, Anifowose AYB, Fagbohun BJ (2016) Testing the ability of an empirical hydrological model to verify a knowledge-based groundwater potential zone mapping methodology. Model Earth Syst Environ 2(174):1–17.  https://doi.org/10.1007/s40808-016-0234-3 Google Scholar
  6. Anifowose AYB, Kolawole F (2012) Tectono-hydrological study of Akure metropolis, Southwestern Nigeria. Special publication of the Nigerian association of hydrological sciences. Hydrology for disaster management: 106–120Google Scholar
  7. Bayowa OG, Olorunfemi MO, Akinluyi FO, Ademilua OL (2014) Integration of hydrogeophysical and remote sensing data in the assessment of groundwater potential of the basement complex Terrain of Ekiti State, Southwestern Nigeria. Ife J Sci 16(3):353–363Google Scholar
  8. Bonetto S, Facello A, Umili G (2017) A new application of CurvaTool semi-automatic approach to qualitatively detect geological lineaments. Environ Eng Geosci 23(3):179–190.  https://doi.org/10.2113/gseegeosci.23.3.179 CrossRefGoogle Scholar
  9. Cross AM (1988) Detection of circular geologic features using the Hough Transform. Int J Remote Sens 9:1519–1528.  https://doi.org/10.1080/01431168808954956 CrossRefGoogle Scholar
  10. Dasho OA, Ariyibi EA, Akinluyi FO, Awoyemi MO, Adebayo AS (2017) Application of satellite remote sensing to groundwater potential modelling in Ejigbo area. Model Earth Syst Environ, Southwestern Nigeria.  https://doi.org/10.1007/s40808-017-322-z Google Scholar
  11. Edet AE (1996) Evaluation of borehole sites based on air photo derived parameters. Bull Int Assoc Eng Geol 54:71–76.  https://doi.org/10.1007/BF02600699 CrossRefGoogle Scholar
  12. Edet AE, Teme SC, Okereke CS, Esu AO (1994) Lineament analysis for groundwater exploration in precambrian Oban Massif and Obudu Plateau, SE Nigeria. J Min Geol 30(1):87–95Google Scholar
  13. Ekwe AC, Nnodu IN, Ugwumbah KI, Onwuka OS (2010) Estimation of aquifer hydraulic characteristics of low permeability formation from geosounding data: a case study of Oduma town, Enugu state. Online J Earth Sci 4(1):19–26CrossRefGoogle Scholar
  14. Hardcastle KC (1995) Photolineament factor: a new computer-aided method for remotely sensing the degree to which the bedrock is fractured. Photogrammetric Eng Remote Sens 61:739–747.  https://doi.org/10.1016/0148-9062(96)81851-1 Google Scholar
  15. Henriksen H, Braathen A (2005) Effects of fracture lineaments and in situ rock stresses on groundwater flow in hard rocks: a case study from Sunnfjord, Western Norway. Hydrogeol J 14:444–461.  https://doi.org/10.1007/s10584-017-2001-5 CrossRefGoogle Scholar
  16. Idornigie AI, Olorunfemi MO (1992) A geoelectrical mapping of the basement structures of the south-central part of the Bida basin and its hydrogeophysical implications. J Min Geol 28(1):93–103Google Scholar
  17. Ilugbo SO, Adebiyi AD (2017) Intersection of lineaments for groundwater prospect analysis using satellite remotely sensed and aeromagnetic dataset around Ibodi, Southwestern Nigeria. Int J Phys Sci 12(23):329–353.  https://doi.org/10.5897/IJPS2017.4692 CrossRefGoogle Scholar
  18. Lattman LH, Parizek RR (1964) Relationship between fracture trace and occurrence of groundwater in carbonate rocks. J Hydrol 2:73–91.  https://doi.org/10.1016/0022-1694(64)90019-8 CrossRefGoogle Scholar
  19. Lobatskaya RM, Strelchenko IP (2016) GIS-based analysis of fault patterns in urban areas: a case study of Irkutsk city, Russia. Geosci Front 7:285–297.  https://doi.org/10.1016/j.gsf.2015.07.004 CrossRefGoogle Scholar
  20. Mabee SB (1999) Factors influencing well productivities in glacial metamorphic rocks. Groundwater 37(1):88–97CrossRefGoogle Scholar
  21. Mabee SB, Hardcastle KC, Wise DW (1994) A method of collecting and analyzing lineaments for regional-scale fractured bedrock aquifer studies. Ground Water 32:884–894.  https://doi.org/10.1111/j.1745-6584.1994.tb00928.x CrossRefGoogle Scholar
  22. Mabee SB, Curry PJ, Hardcastle KC (2002) Correlation of lineaments to groundwater inflows in a bedrock aquifer tunnel. Groundwater 32(6):884–894.  https://doi.org/10.1111/j.1745-6584.2002.tb02489.x CrossRefGoogle Scholar
  23. Mallast U, Gloaguen R, Geyer S, Rodiger T, Siebert (2011) Derivation of groundwater flow-paths based on semi-automatic extraction of lineaments from remote sensing data. Hydrol Earth Syst Sci 15:2665–2678.  https://doi.org/10.5194/hess-15-2665-2011 CrossRefGoogle Scholar
  24. Masoud A, Koike K (2006) Tectonic architecture through Landsat-7 ETM +/SRTM DEM-derived lineaments and relationship to the hydrogeologic setting in Siwa region, NW Egypt. J Afr Earth Sci 45:467–477.  https://doi.org/10.1016/j.jafrearsci.2006.04.005 CrossRefGoogle Scholar
  25. Middleton M, Schnur T, Sorjonen-Ward P, Hyvonen E (2015) Geological lineament interpretation using the object-based image analysis approach: results of semi-automated analyses versus visual interpretation. In: Sarala P (ed), Geological Survey of Finland, Special Paper 57, pp 135–154Google Scholar
  26. Nag SK, Lahiri A (2011) Integrated approach using remote sensing and GIS techniques for delineating groundwater potential zones in Dwarakeswar watershed, Bankura district, West Bengal. Int J Geom Geosci 2(2):430–442Google Scholar
  27. Odeyemi IB, Asiwaju-Bello YA, Anifowose AYB (1999) Remote sensing fracture characteristics of Pan-African granite batholiths in the basement complex of parts of Southwestern Nigeria. J Technosci 3:56–60Google Scholar
  28. Ojo JS, Olorunfemi MO, Akintorinwa OJ, Bayode S, Omosuyi GO, Akinluyi FO (2015) GIS Integrated geomorphological, geological and geoelectrical assessment of the groundwater potential of akure metropolis, Southwestern Nigeria. J Earth Sci Geotech Eng 5(14):85–101Google Scholar
  29. Olorunfemi MO (1990) The hydrogeological implication of topographic variation with overburden thickness in basement complex area of SW Nigeria. J Min Geol 26(1):145–152Google Scholar
  30. Olorunfemi MO (2007) Groundwater exploration, borehole site selection and optimum drill depth in basement complex terrain. Water resources special publication series 1. Nigerian Association of Hydrogeologists (NAH), BeninGoogle Scholar
  31. Olorunfemi MO, Fasuyi SA (1993) Aquifer types and the geoelectric/hydrogeologic characteristics of part of the central basement terrain of Nigeria (Niger State). J Earth 16(3):309–317Google Scholar
  32. Olorunfemi MO, Okhue ET (1992) Hydrogeologic and geologic significance of a geoelectric survey at Ile-Ife, Nigeria. J Min Geol 28(20):221–229Google Scholar
  33. Olorunfemi MO, Olorunniwo MA (1985) Geoelectric parameters and aquifer characteristics of some parts of southwestern Nigeria. Geologia Applicata. E Idrogeologia 20:99–109Google Scholar
  34. Olorunfemi MO, Olarewaju VO, Alade O (1991) On the electrical anisotropy and groundwater yield in a basement complex area of SW Nigeria. J Afr Earth Sc 12(3):462–472CrossRefGoogle Scholar
  35. Satpathy BN, Kanungo DN (1976) Groundwater exploration in hard rock terrain—a case history. Geophys Prospect 2(4):725–736.  https://doi.org/10.1029/WR007i005p01295 CrossRefGoogle Scholar
  36. Siddiqui SH, Parizek RR (1971) Hydrogeologic factors influencing well yields in folded and faulted carbonate rocks in central Pennsylvania. Water Resour Res 7:1295–1312CrossRefGoogle Scholar
  37. Taud H, Parrot JF (1992) Detection of circular structures on satellite images. Int J Remote Sens 13:319–335.  https://doi.org/10.1080/01431169208904041 CrossRefGoogle Scholar
  38. Taylor R, Howard K (2002) A tectono-geomorphic model of the hydrogeology of deeply weathered crystalline rock: evidence from Uganda. Hydrogeol J 8:279–294.  https://doi.org/10.1007/s100400000069 CrossRefGoogle Scholar
  39. Vasuki Y, Holden E, Kovesi P, Micklethwaite (2014) Semi-automatic mapping of geological structures using UAV-based photogrammetric data: an image. Analysis Approach.  https://doi.org/10.1016/j.cageo.2014.04.012 Google Scholar
  40. Vaz DA, Achille GD, Barata MT, Alves EI (2012) Tectonic lineament mapping of the Thaumasia Plateau, Mars: comparing results from photointerpretation and a semi-automatic approach. Comput Geosci 48:162–172.  https://doi.org/10.1016/j.cageo.2012.05.008 CrossRefGoogle Scholar
  41. Sander P (1997) Water-well siting in hard-rock areas: identifying promising targets using a probabilistic approach. Hydrogeol J 5(3):32–43.  https://doi.org/10.1007/s100400050109 CrossRefGoogle Scholar
  42. Yenne EY, Anifowose AYB, Dibal HU, Nimchak RN (2015) An assessment of the relationship between lineament and groundwater productivity in a part of the basement complex, southwestern Nigeria. IOSR J Environ Sci Toxicol Food Technol 9(6):23–35Google Scholar

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© The Author(s) 2018

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Francis O. Akinluyi
    • 1
  • Martins O. Olorunfemi
    • 2
  • Oyelowo G. Bayowa
    • 3
  1. 1.Department of Remote Sensing and GISFederal University of TechnologyAkureNigeria
  2. 2.Department of GeologyObafemi Awolowo UniversityIle-IfeNigeria
  3. 3.Department of Earth SciencesLadoke Akintola University of TechnologyOgbomosoNigeria

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