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Analysis and interpretation of Ilorin aeromagnetic data, North—Central, Nigeria, using geostatistical techniques

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Abstract

The use of geostatistical approaches in the structural dependency analysis and interpretation of magnetic response in conjunction with bedrock composition cannot be de-emphasised. This study utilised the two geostatistical tools namely; variogram and kriging in investigating the structural and spatial dependence index of the magnetic variation in different lithological bedrock constituents. Aeromagnetic data of Ilorin sheet 223 obtained from Nigeria Geological Survey Agency was analysed using a geostatistical approach. Nine lithological units (L1 – L9) were assessed such that their magnetic structural dependency was delineated by fitting the variogram models while the spatial magnetic variation was estimated using ordinary kriging method. Three variogram models; Spherical, Exponential and Gaussian models were adopted. The Nugget Sill Ratio (NSR) and Coefficient of Variability (CV) were as well deduced. Variogram cloud together with box and whisker plot were used to delineate the anomalous magnetic responses across the units. From the fitted models, the results accounted for NSR in the range of 4.1 – 46.2%, 4.0 – 42.4% and 4.1 – 43.7% for Spherical, Exponential and Gaussian models, respectively. Two structural dependencies levels; strong and moderate, were estimated across the nine lithological units. Six units revealed strong autocorrelation levels while three units give rise to a moderate level. Based on kriging estimation, three distinct levels; low, moderate and high magnetic response were identified across the nine lithological units. The least (- 482.6 nT) and highest (401.5 nT) magnetic responses were found associated with L6 lithological unit. Low variability (homogeneity) was found associated with four units (CV < 60%), while five units accounted for heterogeneous response (CV > 60%) in the source of magnetic intensity. In this study, geostatistical methods have proven to be good in estimating the magnetic structural and spatial dependency associated with Ilorin lithological bedrock units.

Highlights

• Airborne magnetic data sets of Ilorin, North central Nigeria were analysed using Variogram and Kriging.

• Three variogram models; Spherical, Gaussian and Exponential were adopted for estimating the degree of structural spatial dependency.

• Strong and moderate spatial dependence were obtained across the nine (9) classified lithological units.

• Based on kriging, the undifferentiated, granitic and granodiorite rock units have the highest magnetic responses.

• The cross validation accounted for strong relationship between the observed and the predicted values thereby making kriging a good predictor of geophysical data set.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  • Abildin Y, Madani N, Topal E (2019) A hybrid approach for joint simulation of geometallurgical variables with inequality constraint. Minerals 9(1):24. https://doi.org/10.3390/min9010024

    Article  Google Scholar 

  • Adeli A, Emery X, Dowd P (2018) Geological modelling and validation of geological interpretations via simulation and classification of quantitative covariates. Minerals 8(1):7. https://doi.org/10.3390/min8010007

    Article  Google Scholar 

  • Agou VD, Varouchakis EA, Hristopulos DT (2019) Geostatistical analysis of precipitation in the island of Crete (Greece) based on a sparse monitoring network. Environ Monit Assess 191:353. https://doi.org/10.1007/s10661-019-7462-8

    Article  Google Scholar 

  • Alahgholi S, Shirazy A, Shirazi A (2018) Geostatistical studies and anomalous elements detection, Bardaskan Area, Iran. Open J Geol 8(7):697–710

    Article  Google Scholar 

  • Annor AE, Olasehinde PI, Pal PC (1987) Basement fracture pattern in the control of river channels. An example from central Nigeria. J Min Geol 26(1):5–11

    Google Scholar 

  • Asmael N, Dupuy A, Huneau F, Hamid S, Le Coustumer P (2015) Groundwater modeling as an alternative approach to limited data in the Northeastern Part of Mt. Hermon (Syria), to develop a preliminary water budget. Water 7:3978–3996

    Article  Google Scholar 

  • Aydin A, Ferré EC, Aslan Z (2007) The magnetic susceptibility of granitic rocks as a proxy for geochemical composition: example from the Saruhan granitoids, NE Turkey. Tectonophysics 441:85–95

    Article  Google Scholar 

  • Azevedo L, Pereira MJ, Ribeiro MC, Soares A (2020) Geostatistical COVID-19 infection risk maps for Portugal. Int J Health Geogr 19(25):1–8. https://doi.org/10.1186/s12942-020-00221-5

    Article  Google Scholar 

  • Battalgazy N, Madani N (2019) Stochastic modeling of chemical compounds in a limestone deposit by unlocking the complexity in bivariate relationships. Minerals 9(11):683

    Article  Google Scholar 

  • Burgess TM, Webster R (2019) Optimal interpolation and isarithmic mapping of soil properties: I the semi-variogram and punctual kriging. Eur J Soil Sci 70(1):11–19. https://doi.org/10.1111/ejss.12784

    Article  Google Scholar 

  • Buttafuoco G, Guargliardi I, Tarvainen T, Jarva J (2017) A multivariate approach to study the geochemistry of urban topsoil in the city of Tampere, Finland. J Geochem Explor 181:191–204

    Article  Google Scholar 

  • Caers J, Zhang T (2002) Multiple-point geostatistics: a quantitative vehicle for integrating geologic analogs into multiple reservoir models. In: GM Grammer et al (eds) Integration of outcrop and modern analog data in reservoir models: AAPGMemoir. American Associa-tion of Petroleum Geologist (AAPG), Tulsa, 24 pp 17

  • Cambardella CA, Moorman TB, Parki NTB, Novack JM, Karlen DL, Turco RF, Knopka AE (1994) Field-scale variability of soil properties in Central Iowa Soils. Soil Sci Soc Am J 58:1501–1511

    Article  Google Scholar 

  • Cameron K, Hunter P (2002) Using spatial models and kriging techniques to optimize long-term ground-water monitoring networks: a case study. Environmetrics 13:629–659. https://doi.org/10.1002/env.582

    Article  Google Scholar 

  • Castrignanò A, Buttafuoco G, Quarto R, Vitti C, Langella G, Terribile F, Venezia A (2017) A combined approach of sensor data fusion and multivariate geostatistics for delineation of homogeneous zones in an agricultural field. Sensors (Basel) 17(12):2794. https://doi.org/10.3390/s17122794

    Article  Google Scholar 

  • Chiles JP, Delfiner P (2012) Geostatistics: modeling spatial uncertainty, 2nd edn. Wiley, New York

    Book  Google Scholar 

  • Cressie NAC (1993) Statistics for spatial data, revised. John Wiley & Sons Inc, New York, p 920

    Google Scholar 

  • David M (1977) Geostatistical ore reserve estimation. Elsevier Science Publishing Co, New York

    Google Scholar 

  • De Benedetto D, Castrignanò A, Sollitto D, Modugno F (2010) Spatial relationship between clay content and geophysical data. Clay Miner 45(2):197–207. https://doi.org/10.1180/claymin.2010.045.2.197

    Article  Google Scholar 

  • Demyanov V, Gloaguen E, Kanevski M (2020) A special issue on data science for geosciences. Math Geosci 52:1–3. https://doi.org/10.1007/s11004-019-09846-0

    Article  Google Scholar 

  • Deutsch CV (1996) Direct assessment of local accuracy and precision. In: 5th International Geostatistics Congress, Wollongong’ 96, pp 115–125

  • Deutsch CV, Journel AG (1998) GSLIB Geostatistical software llibrary and user’s guide, 2nd edn. Oxford University Press, New York, p 369

    Google Scholar 

  • Devlin SJ, Gnanadesikan R, Kettenring JR (1975) Robust estimation and outlier detection with correlation coefficients. Biometrika 62:531–545. https://doi.org/10.2307/2335508

    Article  Google Scholar 

  • Elogne SN, Hristopulos DT, Varouchakis E (2008) An application of spartan spatial random fields in environmental mapping: focus on automatic mapping capabilities. Stoch Env Res Risk Assess 22(5):633–646

    Article  Google Scholar 

  • Emery X, Maleki M (2019) Geostatistics in the presence of geological boundaries: application to mineral resources modeling. Ore Geol Rev 114:103–124. https://doi.org/10.1016/j.oregeorev.2019.103124

    Article  Google Scholar 

  • Emmerich MTM, Yang K, Deutz AH (2020) Infill criteria for multiobjective bayesian optimization. In: Bartz-Beielstein T, Filipi B, Koros̆ec P et al (eds) High-performance simulation-based optimization. Springer International Publishing, Cham, pp 3–16

    Chapter  Google Scholar 

  • Eze PN, Madani N, Adoko AC (2019) Multivariate mapping of heavy metals spatial contamination in a Cu–Ni exploration field (Botswana) using turning bands co-simulation algorithm. Nat Resour Res 28:109–124. https://doi.org/10.1007/s11053-018-9378-3

    Article  Google Scholar 

  • Goovaerts P (1997) Geostatistics for natural resources evaluation. Oxford University Press, New York, 483 p. https://doi.org/10.1017/S0016756898631502

  • Goovaerts P (2011) A coherent geostatistical approach for combining choropleth map and field data in the spatial interpolation of soil properties. Eur J Soil Sci 62:371–380. https://doi.org/10.1111/j.1365-2389.2011.01368.x

    Article  Google Scholar 

  • Gringarten AG (1986) Computer-Aided Well Test Analysis, SPE 14099. Proceedings of the SPE 1986 International Meeting on Petroleum Engineering, Beijing, China, March 17–20

  • Gringarten E, Deutsch CV (2001) Teacher’s aide:variogram interpretation and modeling. Math Geol 33:507–534. https://doi.org/10.1023/A:1011093014141

    Article  Google Scholar 

  • Hosseini SA, Asghari O (2018) Multivariate geostatistical simulation on block-support in the presence of complex multivariate relationships: iron ore deposit case study. Nat Resour Res 28:125–144. https://doi.org/10.1007/s11053-018-9379-2

    Article  Google Scholar 

  • Hotelling H (1953) New light on the correlation coefficient and its transforms. J R Stat Soc 15:193–232. https://www.jstor.org/stable/2983768

  • Humphreys JM, Mahjoor A, Reiss KC, Uribe AA, Brown MT (2019) A geostatistical model for estimating edge effects and cumulative human disturbance in wetlands and coastal waters. Int J Geogr Inf Sci 34(8):1508–1529

    Article  Google Scholar 

  • Ikuemonisan FE, Ozebo VC, Olatinsu OB (2020) Geostatistical evaluation of spatial variability of land subsidence rates in Lagos, Nigeria. Geod Geodyn 11(5):316–327

    Article  Google Scholar 

  • Isaaks EH, Srivastava M (1989) An introduction to applied geostatistics. Oxford University Press, New York, p 561

    Google Scholar 

  • Journel AG, Huijbregts C (1978) Mining geostatistics. Academic Press, London, p 600

    Google Scholar 

  • Krige DG (1951) A statistical approach to some mine valuations problems at the Witwatersrand. J South Afr Inst Min Metall 52:119–139

    Google Scholar 

  • Kölbel L, Kölbel T, Maier U (2020) Water–rock interactions in the Bruchsal geothermal system by U–Th series radionuclides. Geotherm Energy 8(24). https://doi.org/10.1186/s40517-020-00179-4

  • Koopmans LH, Owen DB, Rosenblatt JI (1964) Confidence intervals for the coefficient of variation for the normal and log normal distributions. Biometrika 51:25–32

    Article  Google Scholar 

  • Linde N, Lochbühler T, Dogan M, Van Dam RL (2015) Tomogrambased comparison of geostatistical models: application to the Macrodispersion Experiment (MADE) site. J Hydrol 531:543–556. https://doi.org/10.1016/j.jhydrol.2015.10.073

    Article  Google Scholar 

  • Liu D, Wang Z, Zhang B, Song K, Li X, Li J (2006) Spatial distribution of soil organic carbon and analysis of related factors in croplands of the black soil region, Northeast China. Agric Ecosyst Environ 113:73–81. https://doi.org/10.1016/j.agee.2005.09.006

    Article  Google Scholar 

  • Madani N (2019) Multi-collocated cokriging: an application to grade estimation in the mining industry. In: Mueller C, Assibey-Bonsu W, Baafi E, Dauber C, Doran C, Jaszczuk MJ, Nagovitsyn O (eds) Mining goes digital. CRC Press, Wrocław, pp 158–167. https://doi.org/10.1201/9780429320774-18

    Chapter  Google Scholar 

  • Madani N, Carranza EM (2020) Co-simulated size number: an elegant novel algorithm for identification of multivariate geochemical anomalies. Nat Resour Res 29:13–40. https://doi.org/10.1007/s11053-019-09547-9

    Article  Google Scholar 

  • Mahmoudvand R, Hassani H (2008) Two new confidence intervals for the coefficient variation in a normal distribution. J Appl Stat 00:1–14

    Google Scholar 

  • Maliva RG (2016) Geostatistical methods and applications. In: Aquifer characterization techniques. Springer hydrogeology. Springer, Cham. https://doi.org/10.1007/978-3-319-32137-0_20

  • Mariethoz G, Renard P, Straubhaar J (2010) The Direct sampling method to perform multiple-point geostatistical simulations. Water Resour Res. https://doi.org/10.1029/2008WR007621

    Article  Google Scholar 

  • McKay AT (1932) Distribution of the coefficient of variation and the extended “t” distribution. J Roy Stat Soc 95:695–698

    Article  Google Scholar 

  • McKinley JM, Atkinson PM (2020) A Special Issue on the Importance of Geostatistics in the Era of Data Science. Math Geosci 52:311–315

    Article  Google Scholar 

  • Moriya N (2008) Noise-related multivariate optimal joint-analysis in longitudinal stochastic processes. In: Yang F (ed) Progress in applied mathematical modeling. Nova Science Publishers Inc., New York, pp 223–260

    Google Scholar 

  • Narciso J, Azevedo L, Van De Vijver E, Van Meirvenne M (2020) Geostatistical electromagnetic inversion for landfill characterization. NSG2020 26th European Meeting of Environmental and Engineering Geophysics, Conference Proceedings. Presented at the NSG2020 26th European Meeting of Environmental and Engineering Geophysics, Online. https://doi.org/10.3997/2214-4609.202020154

  • Nguyen H, Cressie N, Braverman A (2012) Spatial statistical data fusion for remote-sensing applications. J Am Stat Assoc 107:1004–1018

    Article  Google Scholar 

  • Nyam GG, Adeeko TO, Umar M, Abdulkafar K (2019) Assessment of magnetic susceptibility of some selected rock samples from Karu Area, North-central Nigeria. Asian J Adv Res Rep 3(2):1–6. https://doi.org/10.9734/ajarr/2019/v3i230085

    Article  Google Scholar 

  • Ogunsanwo FO, Ozebo VC, Olurin OT, Ayanda JD, Coker JO, Sowole O, Ogunsanwo BT, Olumoyegun JM, Olowofela JA (2021) Geostatistical analysis of uranium concentrations in north-western part of Ogun State, Nigeria. J Environ Radioact 237:106706. https://doi.org/10.1016/j.jenvrad.2021.106706

    Article  Google Scholar 

  • Olea RA (1995) Fundamentals of semivariogram estimation, modeling, and usage. In: Yarus JM,  Chambers  RL  (eds) Stochastic modeling and geostatistics: principles, methods, and case studies. AAPG Computer Applications in Geology, no 3, pp 27–36

  • Oluyide PO, Nwajide CS, Oni AO (1998) The Geology of Ilorin Area with Explanations on the 1:250,000 Series, Sheet 50 (Ilorin). Geol Surv Nigeria Bull 42:1–84

    Google Scholar 

  • Oyawoye MO (1964) The geology of Nigerian basement complex—a survey of our present knowledge of them. J Nigerian Min Geol Metall Soc 1(2):87–102

    Google Scholar 

  • Ozebo VC, Ogunsanwo FO, Adebayo GA, Adeniran OJ (2013) Analysis and interpretation of Ibuji spring magnetic anomaly using the Mellin transform. Cent Eur J Geosci 5:43–52. https://doi.org/10.2478/s13533-012-0116-9

    Article  Google Scholar 

  • Rahaman MA (1976) Review of the basement geology of southwestern Nigeria. In: Kogbe CA (ed) Geology of Nigeria, 2nd edn. Elizabethan Publication, Lagos, pp 41–58

    Google Scholar 

  • Raji WO, Abdulkadir KA (2020) Evaluation of groundwater potential of bedrock aquifers in Geological Sheet 223 Ilorin, Nigeria, using geo-electric sounding. Appl Water Sci 10:220. https://doi.org/10.1007/s13201-020-01303-2

    Article  Google Scholar 

  • Seidel EJ, Oliveira MS (2014) Novo índice geoestatístico para a mensuração da dependência espacial. Revista Brasileira Ciência Do Solo 38:699–705. https://doi.org/10.1590/S0100-06832014000300002

    Article  Google Scholar 

  • Shirazy A, Ziaii M, Hezarkhani A, Timkin T (2020) Geostatistical and remote sensing studies to identify high metallogenic potential regions in the Kivi Area of Iran. Minerals 10:869. https://doi.org/10.3390/min10100869

    Article  Google Scholar 

  • Singh BD (2001) Plant breeding: principles and methods. Kalyani Publishers, New Delhi, 889 p. https://doi.org/10.1007/s11004-020-09858-1

  • Sun G, Tian Y, Wang R, Fang J, Li Q (2020) Parallelized multiobjective efficient global optimization algorithm and its applications. Struct Multidiscip Optim 61(2):763–786

    Article  Google Scholar 

  • Talebi H, Mueller U, Tolosana-Delgado R (2019) Geostatistical simulation of geochemical compositions in the presence of multiple geological units: application to mineral resource evaluation. Math Geosci 51:129–153. https://doi.org/10.1007/s11004-018-9763-9

    Article  Google Scholar 

  • Thakur M, Samanta B, Chakravarty DA (2018) non-stationary geostatistical approach to multigaussian kriging for local reserve estimation. Stoch Environ Res Risk Assess 32(32):2381–2404. https://doi.org/10.1007/s00477-018-1533-1

    Article  Google Scholar 

  • Tolosana-Delgado R, Mueller U, van den Boogaart KG (2019) Geostatistics for compositional data: an overview. Math Geosci 51:485–526. https://doi.org/10.1007/s11004-018-9769-3

    Article  Google Scholar 

  • Upton GJG, Fingleton B (1985) Spatial data analysis by example. Wiley, Chichester (432)

    Google Scholar 

  • Varouchakis E A (2019) Geostatistics. Spatiotemporal analysis of extreme hydrological events, 1–38.https://doi.org/10.1016/b978-0-12-811689-0.00001-x

  • Vallejo M, Dimitrakopoulos R (2019) Stochastic orebody modelling and stochastic long-term production scheduling at the Ke´Mag iron ore deposit, Quebec, Canada. Int J Min Reclam Environ 33(7):462–479. https://doi.org/10.1080/17480930.2018.1435969

    Article  Google Scholar 

  • Watson DF, Philip GM (1989) Measures of variability for geological data. Math Geol 21(2):233–254. https://doi.org/10.1007/bf00893217

    Article  Google Scholar 

  • Zhang C, Sargent I, Pan X, Li H, Gardiner A, Hare J, Atkinson PM (2019) Joint deep learning for land cover and land use classification. Remote Sens Environ 221:173–187

    Article  Google Scholar 

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Acknowledgements

The authors wish to thank the Nigerian Geological Survey Agency for their support in releasing the airborne magnetic data for the study area. The authors also thank the anonymous reviewers for their constructive comment which gave new insight to the work.

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F.O. Ogunsanwo conceived the idea and is the principal investigator, V.C Ozebo, J.D Ayanda and J.A Olowofela edited the manuscript, O.T Olurin and J.M Olumoyegun geologically characterized the sheet into units, F.O. Ogunsanwo, J.O. Coker A.D, Adelaja, E.O Falayi and J.O Adepitan sorted the data into the lithological units and developed the Matlab program code for the geostatistical analysis.

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Correspondence to Fidelis Olatoyosi Ogunsanwo.

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Ogunsanwo, F.O., Ozebo, V.C., Olurin, O.T. et al. Analysis and interpretation of Ilorin aeromagnetic data, North—Central, Nigeria, using geostatistical techniques. Earth Sci Inform 15, 2195–2212 (2022). https://doi.org/10.1007/s12145-022-00867-8

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