Pure and Applied Geophysics

, Volume 176, Issue 4, pp 1701–1715 | Cite as

Improving the Resolution of 3-D Resistivity Surveys Along the Perimeter of a Confined Area Using Optimized Arrays

  • Fathi M. AbdullahEmail author
  • Meng H. Loke
  • Mohd Nawawi
  • Khiruddin Abdullah


Three-dimensional (3-D) resistivity surveys usually use a rectangular grid of electrodes to accurately resolve the subsurface structures in areas with very complex geology. However, in some survey areas such as heavily urbanized areas, it is not possible to use a normal grid of electrodes due to physical obstructions such as constructions, buildings, or other types of obstacle. The only practical arrangement is to use electrodes confined to the perimeter of the survey area. Many approaches have been proposed to investigate subsurface features for confined areas, such as the “Baker” and “L and Corner” arrays. These techniques normally use heuristic rules and are designed for perimeters with sharp corners such as rectangles, but might not be applicable for perimeters with smooth shapes such as a circle. New techniques that automatically select arrays that maximize the model resolution can be adapted to select optimum arrays for perimeters of any shape. We study the effectiveness of two sets of optimized perimeter arrays generated based on a modified “Compare R” (CR) method. The performance of these optimized perimeter arrays is compared with the standard “L and Corner” arrays. This is demonstrated by using two synthetic examples and one field survey dataset. In the synthetic models, the results show that, when using both the optimized and standard arrays, the vertical resolution is poorer than the horizontal resolution. However, the optimized perimeter arrays produce better resolution and structure detectability than “L and Corner” arrays. In addition, there is a slight improvement with the noise-weighted optimized dataset, which shows slightly higher resistivity contrasts and the lowest data misfits.


Optimized arrays 3-D resistivity compare R urban areas 



We would like to thank two anonymous reviewers for their insightful comments that considerably improved the clarity of the paper. The first author expresses appreciation to Taiz University, Yemen for financial support. We would like to thank Dr. A. Tejero-Andrade (Universidad Nacional Autónoma de México) for supplying a copy of the “L and Corner” arrays measurement sequences.


  1. Aizebeokhai, A. P. (2010). 2D and 3D geoelectrical resistivity imaging: Theory and field design. Scientific Research and Essays, 5, 3592–3605.Google Scholar
  2. Argote-Espino, D., Tejero-Andrade, A., Cifuentes-Nava, G., Iriarte, L., Farías, S., Chávez, R. E., et al. (2013). 3D electrical prospection in the archaeological site of El Pahñú, Hidalgo State, Central Mexico. Journal of Archaeological Science, 40, 1213–1223.CrossRefGoogle Scholar
  3. Auken, E., Doetsch, J., Fiandaca, G., Christiansen, A. V., Gazoty, A., Cahill, A. G., et al. (2014). Imaging subsurface migration of dissolved CO2 in a shallow aquifer using 3-D time-lapse electrical resistivity tomography. Journal of Applied Geophysics, 101, 31–41.CrossRefGoogle Scholar
  4. Baker, H.A., Djeddi, M., Boudjadja, A.G., Benhamam, K., (2001). A different approach in delineating near surface buried structures. In: 63rd EAGE Conference and Exhibition.Google Scholar
  5. Blome, M., Maurer, H., & Greenhalgh, S. (2011). Geoelectric experimental design—Efficient acquisition and exploitation of complete pole-bipole data sets. Geophysics, 76(1), F15–F26.CrossRefGoogle Scholar
  6. Carpenter, E. W., & Habberjam, G. M. (1956). A tri-potential method of resistivity prospecting. Geophysics, 11, 455–469.CrossRefGoogle Scholar
  7. Carrigan, C. R., Yang, X., LaBrecque, D. J., Larsen, D., Freeman, D., Ramirez, A. L., et al. (2013). Electrical resistance tomographic monitoring of CO2 movement in deep geologic reservoirs. International Journal of Greenhouse Gas Control, 18, 401–408.CrossRefGoogle Scholar
  8. Chambers, J. E., Wilkinson, P. B., Penn, S., Meldrum, P. I., Kuras, O., Loke, M. H., et al. (2013). River terrace sand and gravel deposit reserve estimation using three-dimensional electrical resistivity tomography for bedrock surface detection. Journal of Applied Geophysics, 93, 25–32.CrossRefGoogle Scholar
  9. Chavez, R., Chavez-Hernandez, G., Tejero, A., Alcantara, M., (2011). The “L-Array”, a 3D tool to characterize a fracture pattern in an urban zone. In: Near Surface 2011—17th EAGE European Meeting of Environmental and Engineering Geophysics.Google Scholar
  10. Chávez, R. E., Cifuentes-nava, G., Tejero, A., Hernández-quintero, J. E., & Vargas, D. (2014). Special 3D electric resistivity tomography (ERT) array applied to detect buried fractures on urban areas: San Antonio Tecómitl, Milpa Alta, México. Geofísica Internacional, 53(4), 425–434.CrossRefGoogle Scholar
  11. Dahlin, T., Bernstone, C., & Loke, M. H. (2002). A 3-D resistivity investigation of a contaminated site at Lernacken, Sweden. Geophysics, 67, 1692–1700.CrossRefGoogle Scholar
  12. Dey, A., & Morrison, H. F. (1979). Resistivity modeling for arbitrarily shaped three-dimensional structures. Geophysics, 44, 753–780.CrossRefGoogle Scholar
  13. Farquharson, C. G., & Oldenburg, D. W. (1998). Non-linear inversion using general measures of data misfit and model structure. Geophysical Journal International, 134, 213–227.CrossRefGoogle Scholar
  14. Farquharson, C. G., & Oldenburg, D. W. (2004). A comparison of automatic techniques for estimating the regularization parameter in non-linear inverse problems. Geophysical Journal International, 156, 411–425.CrossRefGoogle Scholar
  15. Fiandaca, G., Cosentino, P. L., Martoran, R., & Messina, P. (2010). The MYG methodology to carry out 3D electrical resistivity tomography on media covered by vulnerable surfaces of artistic value. Il Nuovo Cimento, 125, 711–718.Google Scholar
  16. Friedel, S. (2003). Resolution, stability and efficiency of resistivity tomography estimated from a generalized inverse approach. Geophysics Journal International, 153(May), 305–316.CrossRefGoogle Scholar
  17. Gharibi, M., & Bentley, L. R. (2005). Resolution of 3-D electrical resistivity images from inversions of 2-D orthogonal lines. Journal of Environmental & Engineering Geophysics, 10, 339–349.CrossRefGoogle Scholar
  18. Jones, G., Zielinski, M., & Sentenac, P. (2012). Mapping desiccation fissures using 3-D electrical resistivity tomography. Journal of Applied Geophysics, 84, 39–51.CrossRefGoogle Scholar
  19. Kiflu, H., Kruse, S., Loke, M. H., Wilkinson, P. B., & Harro, D. (2016). Improving resistivity survey resolution at sites with limited spatial extent using buried electrode arrays. Journal of Applied Geophysics, 135, 338–355.CrossRefGoogle Scholar
  20. Legault, J. M., Carriere, D., & Petrie, L. (2008). Synthetic model testing and distributed acquisition dc resistivity results over an unconformity uranium target from the Athabasca Basin, northern Saskatchewan. The Leading Edge, 27, 46–51.CrossRefGoogle Scholar
  21. Loke, M. H., Acworth, I., & Dahlin, T. (2003). A comparison of smooth and blocky inversion methods in 2D electrical imaging surveys. Exploration Geophysics, 34, 182–187.CrossRefGoogle Scholar
  22. Loke, M. H., & Barker, R. D. (1996). Practical techniques for 3D resistivity survey and data inversion. Geophysical Prospecting, 44, 499–523.CrossRefGoogle Scholar
  23. Loke, M. H., Chambers, J. E., Rucker, D. F., Kuras, O., & Wilkinson, P. B. (2013). Recent developments in the direct-current geoelectrical imaging method. Journal of Applied Geophysics, 95, 135–156.CrossRefGoogle Scholar
  24. Loke, M. H., Dahlin, T., & Rucker, D. F. (2014a). Smoothness-constrained time-lapse inversion of data from 3D resistivity surveys. Near Surface Geophysics, 12, 5–24.CrossRefGoogle Scholar
  25. Loke, M. H., Wilkinson, P. B., Uhlemann, S. S., Chambers, J. E., & Oxby, L. S. (2014b). Computation of optimized arrays for 3-D electrical imaging surveys. Geophysical Journal International, 199, 1751–1764.CrossRefGoogle Scholar
  26. Loke, M. H., Kiflu, H., Wilkinson, P. B., Harro, D., & Kruse, S. (2015a). Optimized arrays for 2-D resistivity surveys with combined surface and buried arrays. Near Surface Geophysics, 13, 505–517.CrossRefGoogle Scholar
  27. Loke, M. H., Wilkinson, P. B., Chambers, J. E., Uhlemann, S. S., & Sorensen, J. P. R. (2015b). Optimized arrays for 2-D resistivity survey lines with a large number of electrodes. Journal of Applied Geophysics, 112, 136–146.CrossRefGoogle Scholar
  28. Loke, M.H., Wilkinson, P.B., Tejero-Andrade, A., Kruse, S., (2015c). Optimized arrays for resistivity measurements confined to the perimeter of a survey area. In: Near Surface Geoscience. Turin, Italy.Google Scholar
  29. Martinez-Pagan, P., Cano, A. F., Aracil, E., & Arocena, J. M. (2009). Electrical resistivity imaging revealed the spatial properties of mine tailing ponds in the Sierra Minera of southeast Spain. Journal of Environmental and Engineering Geophysics., 14, 63–76.CrossRefGoogle Scholar
  30. Maurya, P. K., Rønde, V. K., Fiandaca, G., Balbarini, N., Auken, E., Bjerg, P. L., et al. (2017). Detailed landfill leachate plume mapping using 2D and 3D electrical resistivity tomography-with correlation to ionic strength measured in screens. Journal of Applied Geophysics, 138, 1–8.CrossRefGoogle Scholar
  31. Menke, W. (1984). Geophysical data analysis: Discrete inverse theory. Oxford: Academic.Google Scholar
  32. Miller, C. R., Routh, P. S. (2007). Resolution analysis of geophysical images: Comparison between point spread function and region of data influence measures. Geophysical Prospecting, 55, 835–852.CrossRefGoogle Scholar
  33. Ong, W. S. (1993). The geology and engineering geology of Penang Island, Geological Survey of Malaysia.Google Scholar
  34. Rossi, M., Dahlin, T., Olsson, P. I., & Günther, T. (2018). Data acquisition, processing and filtering for reliable 3D resistivity and time-domain induced polarization tomography in an urban area: field example of Vinsta, Stockholm, Special issue on urban geophysics. Near Surface Geophysics, 16, 220–229.Google Scholar
  35. Sauret, E. S. G., Beaujean, J., Nguyen, F., Wildemeersch, S., & Brouyere, S. (2015). Characterization of superficial deposits using electrical resistivity tomography (ERT) and horizontal-to-vertical spectral ratio (HVSR) geophysical methods: A case study. Journal of Applied Geophysics, 121, 140–148.CrossRefGoogle Scholar
  36. Tejero-Andrade, A., Cifuentes, G., Chávez, R.E., Aideé, E., & Delgado-solórzano, C. (2015). L- and CORNER-arrays for 3D electric resistivity tomography: An alternative for geophysical surveys in urban zones. Near Surface Geophysics, 13, 355–367.CrossRefGoogle Scholar
  37. Trogu, A., Ranieri, G., & Fischanger, F. (2011). 3D electrical resistivity tomography to improve the knowledge of the subsoil below existing buildings. Environmental Semeiotics, 4, 63–70.CrossRefGoogle Scholar
  38. White, R. M. S., Collins, S., Denne, R., Hee, R., & Brown, P. (2001). A new survey design for 3D IP inversion modelling at Copper Hill. Exploration Geophysics, 32, 152–155.CrossRefGoogle Scholar
  39. Wilkinson, P. B., Loke, M. H., Meldrum, P. I., Chambers, J. E., Kuras, O., Gunn, D. A., et al. (2012). Practical aspects of applied optimised survey design for electrical resistivity tomography. Geophysical Journal International, 189, 428–440.CrossRefGoogle Scholar
  40. Wilkinson, P. B., Meldrum, P. I., Chambers, J. E., Kuras, O., & Ogilvy, R. D. (2006). Improved strategies for the automatic selection of optimized sets of electrical resistivity tomography measurement configurations. Geophysical Journal International, 167, 1119–1126.CrossRefGoogle Scholar
  41. Zhou, B., & Dahlin, T. (2003). Properties and effects of measurement errors on 2D resistivity imaging surveying. Near Surface Geophysics, 1, 105–117.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Fathi M. Abdullah
    • 1
    • 3
    Email author
  • Meng H. Loke
    • 2
  • Mohd Nawawi
    • 1
  • Khiruddin Abdullah
    • 1
  1. 1.Geophysics Section, School of PhysicsUniversiti Sains MalaysiaPenangMalaysia
  2. 2.Geotomo Software Sdn BhdGelugorMalaysia
  3. 3.Geology Department, Faculty of Applied ScienceTaiz UniversityTaizYemen

Personalised recommendations