1887
Volume 11 Number 6
  • ISSN: 1569-4445
  • E-ISSN: 1873-0604

Abstract

ABSTRACT

To date, few studies offer a quantitative comparison of the performance of image appraisal tools. Moreover, there is no commonly accepted methodology to handle them even though it is a crucial aspect for reliable interpretation of geophysical images. In this study, we compare quantitatively different image appraisal indicators to detect artefacts, estimate depth of investigation, address parameters resolution and appraise ERT‐derived geometry. Among existing image appraisal tools, we focus on the model resolution matrix (), the cumulative sensitivity matrix () and the depth of investigation index () that are regularly used in the literature. They are first compared with numerical models representing different geological situations in terms of heterogeneity and scale and then used on field data sets. The numerical benchmark shows that indicators based on and are the most appropriate to appraise ERT images in terms of the exactitude of inverted parameters, providing mainly qualitative information. In parallel, we test two different edge detection algorithms – Watershed’s and Canny’s algorithms – on the numerical models to identify the geometry of electrical structures in ERT images. From the results obtained, Canny’s algorithm seems to be the most reliable to help practitioners in the interpretation of buried structures.

On this basis, we propose a methodology to appraise field ERT images. First, numerical benchmark models representing simplified cases of field ERT images are built using available information. Then, ERT images are produced for these benchmark models (all simulated acquisition and inversion parameters being the same). The comparison between the numerical benchmark models and their corresponding ERT images gives the errors on inverted parameters. These discrepancies are then evaluated against the appraisal indicators ( and ) allowing the definition of threshold values. The final step consists in applying the threshold values on the field ERT images and to validate the results with knowledge. The developed approach is tested successfully on two field data sets providing important information on the reliability of the location of a contamination source and on the geometry of a fractured zone. However, quantitative use of these indicators remains a difficult task depending mainly on the confidence level desired by the user. Further research is thus needed to develop new appraisal indicators more suited for a quantitative use and to improve the quality of inversion itself.

Loading

Article metrics loading...

/content/journals/10.3997/1873-0604.2013022
2013-04-01
2020-07-14
Loading full text...

Full text loading...

References

  1. AlumbaughD.L. and NewmanG.A.2000. Image appraisal for 2‐D and 3‐D electromagnetic inversion. Geophysics65, 1455–1467.
    [Google Scholar]
  2. AlvarezP.J.J. and IllmanW.A.2006. Bioremediation and Natural Attenuation: Process Fundamentals and Mathematical Models.John Wiley & Sons, Hoboken, New Jersey, USA.
    [Google Scholar]
  3. ArchieG.E.1942. The electrical resistivity log as an aid in determining some reservoir characteristics. American Institute of Mining and Metallurgical Engineers146, 54–62.
    [Google Scholar]
  4. AtekwanaE.A., AtekwanaE., LegallF.D. and KrishnamurthyR.V.2005. Biodegradation and mineral eathering controls on bulk electrical conductivity in a shallow hydrocarbon contaminated aquifer. Journal of Contaminant Hydrology80, 149–167.
    [Google Scholar]
  5. AtekwanaE.A., AtekwanaE.A., RoweR.S., WerkemaJ.D.D. and LegallF.D.2004a. The relationship of total dissolved solids measurements to bulk electrical conductivity in an aquifer contaminated with hydrocarbon. Journal of Applied Geophysics56, 281–294.
    [Google Scholar]
  6. AtekwanaE.A., SauckW.A., AalG.Z.A. and WerkemaJ.D.D.2002. Geophysical investigation of Vadose Zone Conductivity Anomalies at a Hydrocarbon Contaminated Site: Implications for the Assessment of Intrinsic Bioremediation. Journal of Environmental and Engineering Geophysics7, 103–110.
    [Google Scholar]
  7. AtekwanaE.A., SauckW.A. and WerkemaD.D.2000. Investigations of geoelectrical signatures a hydrocarbon contaminated site. Journal of Applied Geophysics44, 167–180.
    [Google Scholar]
  8. AtekwanaE.A., WerkemaJ.D.D., DurisJ.W., RossbachS., AtekwanaE.A., SauckW.A.et al.2004b. In‐situ apparent conductivity measurements and microbial population distribution at a hydrocarbon‐contaminated site. Geophysics69, 56–63.
    [Google Scholar]
  9. BackusG. and GilbertF.1968. The Resolving Power of Gross Earth Data. Geophysical Journal of the Royal Astronomical Society16, 169–205.
    [Google Scholar]
  10. BackusG. and GilbertF.1970. Uniqueness in the Inversion of Inaccurate Gross Earth Data. Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences266, 123–192. doi:10.2307/73746
    [Google Scholar]
  11. BeaujeanJ., NguyenF., KemnaA. and EngensgaardP.2010. Joint and sequential inversion of geophysical and hydrogeological data to characterize seawater intrusion models. In: 21st Salt Water Intrusion Meeting, Vol. 312640/10, pp. 57–60. Ponta Delgada, Acores, Portugal.
    [Google Scholar]
  12. BeucherS.1994. Watershed, hierarchical segmentation and waterfall algorithm. In: Mathematical Morphology and its Applications to Image Processing, Vol. 2.
    [Google Scholar]
  13. CannyJ.1986. A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence8, 679–698. doi:10.1109/tpami.1986.4767851
    [Google Scholar]
  14. CassidyD.P., WerkemaJ.D.D., SauckW., AtekwanaE., RossbachS. and DurisJ.2001. The Effects of LNAPL Biodegradation Products on Electrical Conductivity Measurements. Journal of Environmental and Engineering Geophysics6, 47–52.
    [Google Scholar]
  15. Day‐LewisF.D., SinghaK. and BinleyA.M.2005. Applying petrophysi‐cal models to radar travel time and electrical resistivity tomograms: Resolution‐dependent limitations. Journal of Geophysical Research110. doi:10.1029/2004jb003569
    [Google Scholar]
  16. DemanetD., PirardE., RenardyF. and JongmansD.2001. Application and processing of geophysical images for mapping faults. Computers & Geosciences27, 1031–1037. doi: http://dx.doi.org/10.1016/S0098-3004(00)00156-4
    [Google Scholar]
  17. ElwaseifM.andL. Slater2010. Quantifying tomb geometries in resistivity images using watershed algorithms. Journal of Archaeological Science37, 1424–1436.doi: http://dx.doi.org/10.1016/j.jas.2010.01.002
    [Google Scholar]
  18. FriedelS.2003. Resolution, stability and efficiency of resistivity tomography estimated from a generalized inverse approach. Geophysical Journal International153, 305–316.
    [Google Scholar]
  19. HermansT., VandenbohedeA., LebbeL., MartinR., KemnaA., BeaujeanJ. and NguyenF.2012a. Imaging artificial salt water infiltration using electrical resistivity tomography constrained by geostatistical data. Journal of Hydrology438–439, 168–180. doi: 10.1016/j.jhy‐drol.2012.03.021
    [Google Scholar]
  20. HermansT., VandenbohedeA., LebbeL. and NguyenF.2012b. A shallow geothermal experiment in a sandy aquifer monitored using electric resistivity tomography. Geophysics77, B11–B21.
    [Google Scholar]
  21. HilbichC., MarescotL., HauckC., LokeM.H. and MäusbacherR.2009. Applicability of electrical resistivity tomography monitoring to coarse blocky and ice‐rich permafrost landforms. Permafrost and Periglacial Processes20, 269–284. doi:10.1002/ppp.652
    [Google Scholar]
  22. HuiZ., BofengZ., AnpingS. and WuZ.2009. An Improved Method to Reduce Over‐Segmentation of Watershed Transformation and its Application in the Contour Extraction of Brain Image. Dependable, Autonomic and Secure Computing, 2009. DASC ’09. 8th IEEE International Conference, pp.407–412, 12‐14 Dec. 2009. doi: 10.1109/DASC.2009.116
    [Google Scholar]
  23. HuyakornP.S., UngsM.J., MulkeyL.A. and SudickyE.A.1987. A 3‐Dimensional Analytical Method for Predicting Leachate Migration. Ground Water25, 588–598.
    [Google Scholar]
  24. KemnaA.2000. Tomographic Inversion of Complex Resistivity: Theory and Application.Der Andere Verlag, Osnabrück.
  25. KemnaA., VanderborghtJ., KulessaB. and VereeckenH.2002. Imaging and characterisation of subsurface solute transport using electrical resistivity tomography (ERT) and equivalent transport models. Journal of Hydrology267, 125–146.
    [Google Scholar]
  26. KerfootI.B. and BreslerY.1999. Theoretical analysis of multispectral image segmentation criteria. IEEE Transactions on Image Processing8(9), 798–820. doi:10.1109/83.766858
    [Google Scholar]
  27. LaBrecqueD.J., MilettoM., DailyW., RamirezA. and OwenE.1996. The effects of noise on Occam’s inversion of resistivity tomography data. Geophysics61, 538–548. doi: 10.1190/1.1443980
    [Google Scholar]
  28. MarescotL. and LokeM.H.2004. Using the Depth of Investigation Index Method in 2D Resistivity Imaging for Civil Engineering Surveys. Symposium on the Application of Geophysics to Engineering and Environmental Problems17, 540–547.
    [Google Scholar]
  29. MarescotL., LokeM.H., ChapellierD., DelaloyeR., LambielC. and ReynardN.2003. Assessing reliability of 2D electrical resistivity imaging in mountain permafrost studies using the depth of investigation index method. Near Surface Geophysics1, 57–67.
    [Google Scholar]
  30. MeyerF.1994. Topographic distance and watershed lines. Signal Processing38, 113–125. doi:10.1016/0165‐1684(94)90060‐4
    [Google Scholar]
  31. MillerC.R. and RouthP.S.2007. Resolution analysis of geophysical images: Comparison between point spread function and region of data influence measures. Geophysical Prospecting55, 835–852. doi:10.1111/j.1365‐2478.2007.00640.x
    [Google Scholar]
  32. MoyseyS., SinghaK. and KnightR.2005. A framework for inferring field‐1 scale rock physics relationships through numerical simulation. Geophysical Research Letters32, L08304. doi:10.1029/2004gl022152
    [Google Scholar]
  33. NguyenF., GaramboisS., JongmansD., PirardE. and LokeM.H.2005. Image processing of 2D resistivity data for imaging faults. Journal of Applied Geophysics57, 260–277. doi:10.1016/j.jappgeo.2005.02.001
    [Google Scholar]
  34. NguyenF., KemnaA., AntonssonA., EngesgaardP., KurasO., OgilvyR.et al.2009. Characterization of seawater intrusion using 2D electrical imaging. Near Surface Geophysics7(5–6) doi:10.3997/1873‐0604.2009025
    [Google Scholar]
  35. NoletG., MontelliR. and VirieuxJ.1999. Explicit, approximate expressions for the resolution and a posteriori covariance of massive tomo‐graphic systems. Geophysical Journal International138, 36–44.
    [Google Scholar]
  36. OldenburgD.W. and LiY.1999. Estimating depth of investigation in DC resistivity and IP surveys. Geophysics64, 403–416.
    [Google Scholar]
  37. OldenborgerG.A. and RouthP.S.2009. The point‐spread function measure of resolution for the 3‐D electrical resistivity experiment. Geophysical Journal International176, 405–414.
    [Google Scholar]
  38. OldenborgerG.A., RouthP.S. and KnollM.D.2007. Model reliability for 3D electrical resistivity tomography: Application of the volume of investigation index to a time‐lapse monitoring experiment. Geophysics72, F167–175. doi:10.1190/1.2732550
    [Google Scholar]
  39. PalN.R. and PalS.K.1993. A review on image segmentation techniques. Pattern Recognition26, 1277–1294. doi:10.1016/0031‐3203(93)90135‐j
    [Google Scholar]
  40. PhamD.L., XuC. and PrinceJ.L.2000. A Survey of Current Methods in Medical Image Segmentation. Annual Review of Biomedical Engineering2, 315–338.
    [Google Scholar]
  41. RadulescuM., ValerianC. and YangJ.2007. Time‐lapse electrical resistivity anomalies due to contaminant transport around landfills. Annals of Geophysics50(3). doi:10.4401/ag‐3075
    [Google Scholar]
  42. RamirezA.L., DailyW.D. and NewmarkR.L.1995. Electrical resistance tomography for steam injection monitoring and process control. Journal of Environmental & Engineering Geophysics1, 39–51.
    [Google Scholar]
  43. ReedT.R. and DubufJ.M.H.1993. A Review of Recent Texture Segmentation and Feature Extraction Techniques. CVGIP: Image Understanding57, 359–372. doi:10.1006/ciun.1993.1024
    [Google Scholar]
  44. RentierC.2002. Methode stochastique de delimitation des zones de protection autour des captages d’eau. Université de Liège.
    [Google Scholar]
  45. RobertT., CaterinaD., DeceusterJ., KaufmannO. and NguyenF.2012. A salt tracer test monitored with surface ERT to detect preferential flow and transport paths in fractured/karstified limestones. Geophysics77, B55–B67.
    [Google Scholar]
  46. RobertT., DassarguesA., BrouyèreS., KaufmannO., HalletV. and NguyenF.2011. Assessing the contribution of electrical resistivity tomography (ERT) and self‐potential (SP) methods for a water well drilling program in fractured/karstified limestones. Journal of Applied Geophysics75, 42–53. doi:10.1016/j.jappgeo.2011.06.008
    [Google Scholar]
  47. RouthP.S., OldenborgerG.A. and OldenburgD.W.2005. Optimal Survey design using the point spread function measure of resolution. SEG Technical Program Expanded Abstracts24, 1033–1036.
    [Google Scholar]
  48. SauckW.A.2000. A model for the resistivity structure of LNAPL plumes and their environs in sandy sediments. Journal of Applied Geophysics44, 151–165.
    [Google Scholar]
  49. SauckW.A., AtekwanaE.A. and NashM.S.1998. High conductivities associated with an LNAPL plume imaged by integrated geophysical techniques. Journal of Environmental & Engineering Geophysics2, 203–212.
    [Google Scholar]
  50. SchieweJ.2002. Segmentation of high‐resolution remotely sensed data ‐ Concepts, applications and problems. International Archives of Photogrammetry and Remote Sensing34, 380–385.
    [Google Scholar]
  51. SchönJ.H.2004. Physical Properties of Rock. In: Physical Properties of Rock, Vol. 18, (eds K.Helbig and S.Treitel ). Elsevier.
    [Google Scholar]
  52. SinghaK., Day‐LewisF.D. and MoyseyS.2007. Accounting for tomo‐graphic resolution in estimating hydrologic properties from geophysical data. In: Subsurface Hydrology: Data Integration for Properties and Processes, Vol. 171, pp. 227–241. AGU.
    [Google Scholar]
  53. SinghaK. and MoyseyS.2006. Accounting for spatially variable resolution in electrical resistivity tomography through field‐scale rock‐physics relations. Geophysics71, A25–A28.
    [Google Scholar]
  54. SlaterL., BinleyA.M., DailyW. and JohnsonR.2000. Cross‐hole electrical imaging of a controlled saline tracer injection. Journal of Applied Geophysics44, 85–102. doi:10.1016/S0926‐9851(00)00002‐1
    [Google Scholar]
  55. TherrienR., McLarenR.G., SudickyE.A. and PandayS.M.2005. HydroGeo‐Sphere: A Three‐Dimensional Numerical Model Describing Fully‐Integrated Subsurface and Surface Flow and Solute Transport.Groundwater Simulation GroupLaval & Waterloo University, 5, 343 pp.
    [Google Scholar]
  56. WerkemaD.D.Jr., AtekwanaE.A., EndresA.L., SauckW.A. and CassidyD.P.2003. Investigating the geoelectrical response of hydrocarbon contamination undergoing biodegradation. Geophysical Research Letters30. doi:10.1029/2003gl017346
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.3997/1873-0604.2013022
Loading
/content/journals/10.3997/1873-0604.2013022
Loading

Data & Media loading...

  • Article Type: Research Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error