ArcGIS fuzzy modeling to assess the relationship between seismic wave velocity and electrical resistivity with limestone mass quality (case study: Asmari Formation, southwest Iran)

  • M. Talkhablou
  • M. Kianpour
  • S. M. Fatemi AghdaEmail author
Original Article


In this study, seismic-wave velocity (Vp) and electrical resistivity (ER) were used to determine the quality of Asmari limestone in the southwest of Iran. In addition, the efficiency of geophysical methods in prediction of rock mass classification system (Q) and modified classification for sedimentary rock masses (Qsrm) has been compared. Models for the prediction of Q and Qsrm in Asmari limestone were rendered by extracting about 1200 data point sets of studded sections of Seymareh and Karoun 2 Dam Sites (SDS and KDS) in south-west Iran, and using multivariate regression analyses and ArcGIS fuzzy overlay (AFO). Because Qsrm considers bedding, dipping of the layers, texture, and presence of cavities, prediction of Qsrm value by combining geophysical methods has a better prediction of limestone rock mass quality. The coefficient of determination (R2) for empirical equations obtained for predicting of Q and Qsrm 0.49 and 0.66, respectively; and the coefficient of determination 0.82 for the estimated Qsrm from the AFO model was calculated. In addition, root mean square error (RMSE) and mean average error (MAE) were measured for model accuracy evaluation shows that the AFO method was interesting because it had good accuracy for prediction of Qsrm by geophysical methods.


Sedimentary rock mass quality index Asmari limestone Geophysical methods Empirical equations Fuzzy inference system 



  1. Alavi M (2004) Regional stratigraphy of the Zagros fold-thrust belt of Iran and its preforland evaluation. Am J Sci 304:1–20CrossRefGoogle Scholar
  2. Alvarez Grima M, Babuska R (1999) Fuzzy model for the prediction of unconfined compressive strength of rock samples. Int J Rock Mech Min Sci 36:339–349CrossRefGoogle Scholar
  3. Azwin IN, Saad R, Saidin M, Nordiana MM, Bery AA, Hidayah IN (2015) Combined analysis of 2-D electrical resistivity, seismic refraction and geotechnical investigations for Bukit Bunuh complex crater. IOP Conf Ser Earth Environ Sci 23(1):12–13Google Scholar
  4. Barton N (2007) Rock quality, seismic velocity, attenuation, and anisotropy. Taylor & Francis Group, London, pp 19–48Google Scholar
  5. Barton N, Lien R, Lunde J (1974) Engineering classification of rock masses for the design of tunnel support. J Rock Mech Eng 6(4):189–236CrossRefGoogle Scholar
  6. Bieniawski ZT (1973) Engineering classification of jointed rock masses. Trans S Afr Inst J Civ Eng 15:335–344Google Scholar
  7. Cardarelli E, Marrone C, Orlando L (2006) Evaluation of tunnel stability using integrated geophysical methods. J Appl Geophys 52(2–3):93–102Google Scholar
  8. Carrozzo MT, Leucci T, Margiotta S, Mazzone F, Negri S (2008) Integrated geophysical and geological investigations applied to sedimentary rock mass characterization. Lecce, Italy, University of Salento, Department of Science and Material. Ann Geophys 51(1):191–202Google Scholar
  9. Chrisman N (1997) Exploring geographic information systems. Wiley, New York, p 250Google Scholar
  10. Costamagna E, Oggeri C, Segarra P, Castedo R, Navarro J (2018) Assessment of contour profile quality in D&B tunneling. J Tunnel Undergr Space Technol 75:67–80CrossRefGoogle Scholar
  11. Deere DU (1963) Technical description of rock cores for engineering purposes. J Rock Mech Eng Geol 1:16–22Google Scholar
  12. Dutta NP (1984) Seismic refraction method to study the foundation rock of a dam. J Geophys Prospect 32:1103–1110CrossRefGoogle Scholar
  13. Feizi F, Ramezanali AK, Mansouri E (2017) Calcic iron skarn prospectively mapping based on fuzzy AHP method, a case study in Varan area, Markazi province. J Geosci 21:123–126CrossRefGoogle Scholar
  14. Fisne A, Kuzu C, Hudaverdi T (2010) Prediction of environmental impacts of quarry blasting operation using fuzzy logic. Environ Monit Assess. (epub ahead of print)Google Scholar
  15. Ghasemi E, Ataei M, Shahriar K (2011) Prediction of roof fall rate in coal mines using fuzzy logic. In: Proceedings of the 30th international conference on ground control in mining, University of West Virginia, Morgantown, pp 186–191Google Scholar
  16. Ghasemi E, Ataei M, Hashemolhosseini H (2012) Development of a fuzzy model for predicting ground vibration caused by rock blasting in surface mining. J Vib Control. Google Scholar
  17. Gokceoglu C, Yesilnacar E, Sonmez H, Kayabasi A (2004) A neuro-fuzzy model for modulus of deformation of jointed rock masses. J Comput Geotech 31(5):375–383CrossRefGoogle Scholar
  18. Havenith HB, Jongmans D, Faccioli E, Abdrakhmatov K, Bard PY (2002) Site effects analysis around the seismically induced Ananevo rockslide, Kyrgyzstan. Bull Seismol Soc Am 92:3190–3209CrossRefGoogle Scholar
  19. Hemmati Nourani M, Taheri Moghadder M, Safari M (2017) Classification and assessment of rock mass parameters in Choghart iron mine using P-wave velocity. J Rock Mech Geotech Eng 9(2):318–328CrossRefGoogle Scholar
  20. Iran Water and Power Resourced Development Co (2007) Final report of rock mechanics of Karun 2 dam. Iran Water and Power Resourced Development Co, TehranGoogle Scholar
  21. Iran Water and Power Resourced Development Co (2012) Final report of rock geophysical study of Karun 2 dam. Iran Water and Power Resourced Development Co, TehranGoogle Scholar
  22. Jalalifar H, Mojedifar S, Sahebi AA (2014) Prediction of rock mass rating using fuzzy logic and multi-variable RMR regression model. Int J Min Sci Technol 24:237–232CrossRefGoogle Scholar
  23. Jongmans D, Hemroulle P, Demanet D, Renardy F, Vanbrabant Y (2000) Application of 2D electrical and seismic tomography techniques for investigating landslides. Eur J Environ Eng Geophys 5:75–89Google Scholar
  24. Juang CH, Lee DH (1990) Rock mass classification using fuzzy sets. In: Tenth Southeast Asian geotechnical conference. Chinese Institute of Civil and Hydraulic Engineering, TaipeiGoogle Scholar
  25. Karimi H, Tavakkoli M (2007) Assessment of the appeared water origin in the water tunnel of powerhouse of Seymarehdam, Ilam. J Eng Geol 2(1):23–30Google Scholar
  26. Kearey P, Brooks M, Hill I (2013) An introduction to geophysical exploration. Blackwell, LondonGoogle Scholar
  27. Koleini M (2012), Engineering geological assessment and rock mass characterization of the Asmari formation (Zagros range) as large dam foundation rocks in southwestern Iran. Ph.D. thesis, University of Pretoria, South AfricaGoogle Scholar
  28. Lapenna V, Lorenzo P, Perrone A, Piscitelli S, Sdao F, Rizzo E (2003) High-resolution geoelectrical tomographies in the study of Giarrossa landslide (southern Italy). Bull Eng Geol Environ 62:259–268CrossRefGoogle Scholar
  29. Larson VE, Golaz J, Cotton W (2002) Small-scale and mesoscale variability in cloudy boundary layers: joint probability density functions. J Atmos Sci 59:3519–3539CrossRefGoogle Scholar
  30. Leucci G, Giorgi LD (2006) Experimental studies on the effects of fracture on the P and S wave velocity propagation in sedimentary rock (“Calcarenite del Salento”). J Eng Geol 84(3–4):130–142CrossRefGoogle Scholar
  31. Lewellen WS, YohS (1993) Binormal model of ensemble partial cloudiness. J Atmos Sci 50:1228–1237CrossRefGoogle Scholar
  32. Mahab Ghods Consulting Engineers (2009) Engineering geology report on Seymareh dam. Mahab Ghods Consulting Engineers, TehranGoogle Scholar
  33. Marquis G, Hyndman RD (1992) Geophysical support for aqueous fluids in the deep crust: seismic and electrical relationships. Int J Geophys 110:91–105CrossRefGoogle Scholar
  34. Mosadeghi R, Warnken J, Tomlinson R, Mirfenderesk H (2015) Comparison of Fuzzy-AHP and AHP in a spatial multi-criteria decision-making model for urban land-use planning. J Comput Environ Urban Syst 49:54–65CrossRefGoogle Scholar
  35. Nguyen VU, Ashworth EA (1985) Rock mass classification by fuzzy sets. In: 26th US symposium on rock mechanics, RapidCity, pp 937–945Google Scholar
  36. Ross TJ (1995) Fuzzy logic with engineering applications. McGraw-Hill, New York, p 600Google Scholar
  37. Rudman AJ, Blake JF, Biggs ME (1975) Transformation of resistivity to pseudo-velocity logs. J Am Assoc Pet Geol 59:1151–1165Google Scholar
  38. Willmott CJ, Matsuura K (2005) Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim Res 30:79–82CrossRefGoogle Scholar
  39. Worboys MF, Duckham M (2004) GIS: a computing perspective, 2nd edn. CRC Press, New YorkCrossRefGoogle Scholar
  40. Zadeh LA (1984) Making computer think like people. IEEE Spectr 8:26–32CrossRefGoogle Scholar
  41. ZaminKavGostar Co (2006) Tomography technical report of Seymareh dam site. ZaminKavGostar Co, TehranGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • M. Talkhablou
    • 1
  • M. Kianpour
    • 1
  • S. M. Fatemi Aghda
    • 1
    Email author
  1. 1.Department of Applied Geology, Faculty of Earth ScienceKharazmi UniversityTehranIran

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