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Volume 11 Number 5
  • ISSN: 1569-4445
  • E-ISSN: 1873-0604

Abstract

ABSTRACT

Estimating the dimensions (defined here as width, height and depth of burial) of discrete targets within resistivity models produced as a result of applying smoothness constraints in most inversion algorithms is difficult, especially when targets are closely spaced. Here we couple an image processing technique (watershed algorithm) with a trained Artificial Neural Network (ANN) model to arrive at predictions of the geometry and resistivity of discrete targets from an initial smoothness constraint resistivity model. These predictions are compared with those obtained from (1) applying the watershed algorithm alone, (2) inversion using the regular L1 norm and (3) when a smoothing disconnect is defined using image processing with data subsequently reinverted to arrive at a revised model estimate. Synthetic studies were conducted on a single cavity model, a model for two widely spaced cavities (spacing >> unit electrode spacing), a model for two closely spaced cavities (spacing < unit electrode spacing) and a model for three closely spaced cavities (spacing < unit electrode spacing). In all model scenarios, the average root mean square (RMS) model error using our ANN approach is below 1 whilst the average combined RMS model error when including target resistivity is 35 for the single cavity, 30 for widely spaced targets and 75 for the closely spaced targets. Despite the higher errors in the closely spaced cavity models, application of the algorithm confirms the presence of multiple features, which is not ascertainable from the smooth inversion, or even when using a disconnect constraint. The ANN derived model significantly reduces the RMS misfit between synthetic and inverted models. We demonstrate the approach using field measurements collected over a precisely known void and also apply the method to smooth resistivity images obtained from measurements collected over an archaeological site at Qurnet Murai, Luxor city, Egypt.

© European Association of Geoscientists and Engineers © 2013 European Association of Geoscientists and Engineers
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2013-04-01
2024-04-23

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References

  1. BarraudJ.2006. The use of watershed segmentation and GIS software for textural analysis of thin sections. Journal of Volcanology and Geothermal Research154, 17–33.
     [Google Scholar]
  2. BlaschekR., HordtA. and KemnaA.2008. A new sensitivity‐controlled focusing regularization scheme for the inversion of induced polarization data based on the minimum gradient support. Geophysics73(2), F45–F54.
     [Google Scholar]
  3. BoucheddaA., ChouteauM., BinleyA. and GirouxB.2012. 2‐D joint structural inversion of cross‐hole electrical resistance and ground penetrating radar data. Journal of Applied Geophysics78, 52–67.
     [Google Scholar]
  4. DahlinT. and ZhouB.2004. A numerical comparison of 2D resistivity imaging with 10 electrode arrays. Geophysical Prospecting52, 379–398.
     [Google Scholar]
  5. EllisR.G. and OldenburgD.W.1994. Applied geophysical inversion. Geophysical Journal International116, 5–11.
     [Google Scholar]
  6. El‐QadyG., HafezM., AbdallaM. and UshijimaK.2005. Imaging Subsurface Cavities Using Geoelectric Tomography and Ground‐Penetrating radar. Journal of Cave and Karst Studies67(3), 174–181.
     [Google Scholar]
  7. ElwaseifM. and SlaterL.2010. Quantifying Tomb Geometries in Resistivity Images Using Watershed Algorithms. Journal of Archaeological Science37(7), 1424–1436.
     [Google Scholar]
  8. GuntherT., RuckerC. and SpitzerK.2006. 3‐D modeling and inversion of DC resistivity data incorporating topography ‐ Part II: Inversion. Geophysical Journal International166, 506–517.
     [Google Scholar]
  9. HordtA., BlaschekR., KemnaA. and ZisserN.2007. Hydraulic conductivity estimation from induced polarisation data at the field scale ‐ The Krauthausen case history. Journal of Applied Geophysics62, 33–46.
     [Google Scholar]
  10. KaipioJ.P., KolehmainenV., VauhkonenM. and SomersaloE.1999. Inverse problems with structural prior information. Inverse Problems15, 713–729.
     [Google Scholar]
  11. LaBrecqueD. and WardS.1990. 2‐D cross‐borehole resistivity model fitting. In: Geotechnical and Environmental Geophysics, (ed. S.Ward ), 51–47. SEG, Tulsa, OK.
     [Google Scholar]
  12. LokeM.H.2004. 2‐D and 3‐D electrical imaging surveys, (PDF available from http://www.geoelectrical.com/).
     [Google Scholar]
  13. LokeM.H., AcworthI. and DahlinT.2003. A comparison of smooth and blocky inversion methods in 2‐D electrical imaging surveys. Exploration Geophysics34, 182–187.
     [Google Scholar]
  14. LokeM.H. and BarkerR.D.1996. Rapid least‐squares inversion of apparent resistiity pseudosections by a quasi‐Newton method. Geophysical Prospecting44, 131–152.
     [Google Scholar]
  15. LokeM.H. and LaneJ.W.2002. The use of constraints in 2D and 3D resistivity modelling. Proceedings of the 8th meeting of the Environmental and Engineering Geophysical Society ‐ European Section.
     [Google Scholar]
  16. MetwalyM., GreenA., HorstmeyerH., MaurerH., AbbasA. and HassaneenA.2005. Combined Seismic Tomographic and Ultra shallow Seismic Reflection Study of an Early Dynastic Mastaba, Saqqara, Egypt. Archaeological Prospection12, 245–256.
     [Google Scholar]
  17. MillerC.R., AllenA.L., SpeeceM.A., El‐WerrA.K. and LinkC.A.2005. Land Streamer Aided Geophysical Studies at Saqqara, Egypt. Journal of Environmental & Engineering Geophysics10, 371–380.
     [Google Scholar]
  18. NguyenF., GaramboisS., JongmansD., PirardE. and LokeM.H.2005. Image processing of 2D resistivity data for imaging faults. Journal of Applied Geophysics57, 260–277.
     [Google Scholar]
  19. NixonM. and AguadoA.2002. Feature Extraction and Image Processing. Newnes, Linacre House, Jordan Hill, Oxford OX2 8DP.
     [Google Scholar]
  20. OldenburgD. and LiY.1994. Inversion of induced polarization data. Geophysics59, 1327–1341.
     [Google Scholar]
  21. ParkG., ParkS. and KimJ.2010. Estimating the existence probability of cavities using integrated geophysics and a neural network approach. Computers & Geosciences36, 1161–1167.
     [Google Scholar]
  22. PorresJ.A., OrtizandS. and IbanezS J.2012. Subsoil caves characterization by means of the interpretation of electrical resistivity tomography. Application to Clunia and Atapuerca archaeological sites. In: Geotechnical and Geophysical Site Characterization, (eds P.W.Mayne ). CRC Press, ISBN 978‐0‐415‐62136‐6.
     [Google Scholar]
  23. PortniaguineO. and ZhdanovM.S.1999. Focusing geophysical inversion images. Geophysics64, 874–887.
     [Google Scholar]
  24. RaicheA.1991. A pattern recognition approach to geophysical inversion using neural nets. Geophysical Journal International105, 629–648.
     [Google Scholar]
  25. RoueffA., ChanussotJ., MarsJ. and NguyenM.Q.2004. Unsupervised separation of seismic waves using the watershed algorithm on time‐scale images. Geophysical Prospecting52(4), 287–300.
     [Google Scholar]
  26. RumelhartD.E, HintonG.E. and WilliamsR.J.1986. Learning internal representations by error propagation. In: Foundations: Vol. 1. Parallel Distributed Processing, (eds D.E.Rumelhart , L.McClelland & PDP Research Group ). Cambridge, MA: MIT Press.
     [Google Scholar]
  27. SimpsonP.K.1990. Artificial Neural Systems: Foundations, Paradigms, Applications, and Implementations. Pergamon Press, 47–69.
     [Google Scholar]
  28. SlaterL. and BinleyA.2006. Engineered barriers for pollutant containment and remediation. In: Applied Hydrogeophysics, (eds H.Vereeken , A.Binley , G.Cassiani , A.Revil and K.Titov ), 293–317. NATO Science Series IV, Earth and Environmental Sciences, Springer.
     [Google Scholar]
  29. SmithM.1993. Neural Networks for Statistical Modeling. Van Nostrand Reinhold, New York.
     [Google Scholar]
  30. StanleyK.O. and MiikkulainenR.2002d. Evolving neural networks through augmenting topologies. Evolutionary Computation10(2), 99–127.
     [Google Scholar]
  31. WinklerE., SeiberlW. and AhlA.2003. Interpretation of Airborne Electromagnetic Data with Neural Networks. In: Geophysical Applications of Artificial Neural Networks and Fuzzy Logic, (eds W.Sandham and M.Leggett ). Kluwer Academic Publishers.
     [Google Scholar]
  32. WisénR., ChristiansenA., DahlinT. and AukenE.2008. Experience from Two Resistivity Inversion Techniques Applied in Three Cases of Geotechnical Site Investigation. Journal of Geotechnical and Geoenvironmental Engineering134(12), 1730–1742.
     [Google Scholar]
  33. ZhangL. and PoultonM.2001. Neural network inversion of EM39 induction log data. In: Geophysical Applications of Artificial Neural Networks and Fuzzy Logic, (eds B.Sandham and M.Leggett ). Kluwer Academic Publishing, Netherlands.
     [Google Scholar]
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Reconstruction of discrete resistivity targets using coupled artificial neural networks and watershed algorithms
Near Surface Geophysics 11, 517 (2013); https://doi.org/10.3997/1873-0604.2013045
/content/journals/10.3997/1873-0604.2013045
/content/journals/10.3997/1873-0604.2013045
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  • Article Type: Research Article

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