1887
  1. Home
  2. A-Z Publications
  3. Near Surface Geophysics
  4. Volume 12, Issue 3
  5. Article
Volume 12, Issue 3
  • ISSN: 1569-4445
  • E-ISSN: 1873-0604

Abstract

ABSTRACT

A unified interpretation technique, based on the computation of total gradient from first order horizontal and vertical derivatives of a measured anomaly, namely, magnetic, gravity or self‐potential, can be used to determine horizontal location, depth, and the nature of the causative source geometry. The total gradient, in general, may exhibit several symmetrical bell‐shaped functions of different magnitudes and with different decay rates ‐termed as ‘source geometry factor’ to describe the nature of the causative sources. ‐values have been presented for idealized source geometries producing three different types of measurements (anomalies). The non‐linear inversion of the total gradient with multiple sources of different geometries is accomplished through Ant Colony Optimization (ACO) – a technique with only a few demonstrated applications in geophysical problems. We have also implemented the ACO which, in its original form, is suitable for discrete space or combinatorial optimization in a continuous model space using decimal coding. Moreover, unlike conventional techniques based on pre‐specified moving/ fixed data length windows, the proposed technique uses much of the total gradient data points except at the ends of the profile (where error in the total gradient can be large). The presence of singularity in the Euler technique in the analysis of a gravity anomaly over a thick faulted slab has been overcome by the total gradient ACO method. A procedure has also been formulated for analysing gravity and reduced to pole (RTP) total field magnetic anomalies over spherical and vertical cylindrical source geometries. To evaluate the efficacy of the proposed technique, ACO results are compared with other modern techniques, namely, Particle Swarm Optimization (PSO), Enhanced Local Wavenumber (ELW) and the Euler method through simulated data contaminated with random noise and three field examples. This study has revealed that the total gradient ACO technique leads to better convergence towards the global minimum and also better inversion results than those obtained by the PSO and other techniques mentioned above. Unlike the ELW and Euler techniques, the total gradient analysis provides enhanced resolution for interfering sources.

© European Association of Geoscientists and Engineers © 2014 European Association of Geoscientists and Engineers
Loading

Article metrics loading...

/content/journals/10.1002/nsg.123001
2013-09-01
2024-04-24

  • From This Site

    /content/journals/10.1002/nsg.123001
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. AbdelrahmanE.M., BayoumiA.I., AbdelhadyY.E., GobashyM.M. and El‐ArabyH.M.1989. Gravity interpretation using correlation factors between successive least‐squares residual anomalies. Geophysics54, 1614–1621.
     [Google Scholar]
  2. AbdelrahmanE.M., El‐ArabyH.M., El‐AlabyT.M. and Abo‐EzzE.R.2001. The least squares minimization approaches to depth, shape and amplitude coefficient determination from gravity data. Geophysics66, 1105–1109.
     [Google Scholar]
  3. AgarwalB.N.P.1984. Quantitative interpretation of self‐potential anomalies. 54th Annual International Meeting, SEG, Expanded Abstracts, 154–157.
     [Google Scholar]
  4. AgarwalB.N.P. and SrivastavaS.2008. FORTRAN codes to implement enhanced local wave number technique to determine location, depth and shape of the causative source using magnetic anomaly. Computers and Geosciences34, 1843–1849. doi: 10.1016/j.cageo.2007.10.012
     [Google Scholar]
  5. AgarwalB.N.P. and SrivastavaS.2009. Analyses of self‐potential anomalies by conventional and extended Euler deconvolution techniques. Computers and Geosciences35, 2231–2238. doi:10.1016/j. cageo.2009.03.005
     [Google Scholar]
  6. BhattacharyyaB.K.1969. Bicubic spline interpolation as a method for treatment of potential field data. Geophysics34, 402–423.
     [Google Scholar]
  7. DorigoM.1992. Optimization, learning and natural algorithms.PhD thesis, Politecno di Milano.
     [Google Scholar]
  8. DorigoM., ManiezzoV. and ColomiA.1996. The ant system: optimization by a colony of cooperating ants. IEEE Transactions on Systems, Man and Cybernatics26, 29–42.
     [Google Scholar]
  9. DorigoM. and CaroG.D.1999. The Ant Colony Optimization metaheuristic. In: New Ideas in Optimization, (eds D.Corne, M.Dorigo and F.Glover). McGraw‐Hill, London, U.K., 11–32.
     [Google Scholar]
  10. FitzGeraldD., ReidA.B. and McInerneyP.2004. New discrimination techniques for Euler techniques. Computers and Geosciences30, 461–469.
     [Google Scholar]
  11. GrantF.S. and WestG.F.1965. Interpretation Theory in Applied Geophysics.McGraw‐Hill Book Company, New York.
     [Google Scholar]
  12. GreenR.1976. Accurate determination of the dip angle of a geological contact using the gravity method. Geophysical Prospecting24, 265–272.
     [Google Scholar]
  13. HaneyM., JohnstonC., LiY. and NabighianM.2003. Envelopes of 2D and 3D magnetic data and their relationship to the analytic signal: Preliminary results. SEG Expanded Abstract22, 596. doi: 10.1190/1.1817997
     [Google Scholar]
  14. InmanS.R.1975. Resistivity inversion with ridge regression. Geophysics40, 798–817.
     [Google Scholar]
  15. KeatingP. and PilkingtonM.2004. Euler deconvolution of the analytic signal and its application to magnetic interpretation. Geophysical Prospecting52, 165–182.
     [Google Scholar]
  16. KeatingP. and SailhacP.2004. Use of the analytic signal to identify magnetic anomalies due to kimberlite pipes. Geophysics69, 180–190. doi: 10.1190/1.1649386
     [Google Scholar]
  17. KuC.C.1977. A direct computation of gravity and magnetic anomalies caused by 2‐and 3‐dimensional bodies of arbitrary shape and arbitrary magnetic polarization by equivalent‐point method and simplified cubic spline. Geophysics42, 610–642.
     [Google Scholar]
  18. MartínezL.F., García‐GonzaloE. and NaudetV.2010. Particle swarm optimization applied to solving and appraising the streaming‐potential inverse problem. Geophysics75, WA3–WA15. doi: 10.1190/1.3460842
     [Google Scholar]
  19. MoreJ.J.1978. The Levenburg – Marquardt algorithm: implementation and theory. In: Numerical Analysis, Dundee, 1977 Lecture Notes in Mathematics, 630, (ed. G.A.Watson), Springer, Berlin, 105–116.
     [Google Scholar]
  20. MenkeW.1984. Geophysical Data Analysis: Discrete Inverse Theory.New York Academic.
     [Google Scholar]
  21. NabighianM.N.1972. The analytic signal of two‐dimensional magnetic bodies with polygonal cross‐section, its properties and use for automated anomaly interpretation. Geophysics37, 507–517.
     [Google Scholar]
  22. NabighianM.N., GrauchV.J.S., HansenR.O., LaFehrT.R., LiY., PeirceJ.W., PhillipsJ.D. and RuderM.E.2005a. 75th Anniversary. The historical development of the magnetic method in exploration. Geophysics70, 33ND–61ND.
     [Google Scholar]
  23. NabighianM.N., AnderM.E., GrauchV.J.S., HansenR.O., LaFehrT.R., LiY., et al. 2005b. 75th Anniversary. The historical development of the gravity method in exploration. Geophysics70, 63ND–89ND.
     [Google Scholar]
  24. NettletonL.L.1940. Geophysical Prospecting for Oil.Mc Graw Hill, NY.
     [Google Scholar]
  25. NettletonL.L.1971. Elementary Gravity and Magnetic for Geologists and Seismologists.SEG, Tulsa, OK.
     [Google Scholar]
  26. ParasnisD.S.1996. Principles of Applied Geophysics.Chapman & Hall publication, New York.
     [Google Scholar]
  27. PressW.H., TeukolskyS.A., VetterlingW.T. and FlanneryB.P.1994. Numerical Recipes in FORTRAN, 2nd Edition. Cambridge University Press, New Delhi.
     [Google Scholar]
  28. PilkingtonM.1997. 3‐D magnetic imaging using conjugate gradients. Geophysics62, 1132–1142.
     [Google Scholar]
  29. PilkingtonM. and KeatingP.2006. The relationship between local wave‐number and analytic signal in magnetic interpretation. Geophysics71, L1–L3.
     [Google Scholar]
  30. RavatD.1996. Analysis of the Euler method and its applicability in environmental magnetic investigations. Journal of Environmental Engineering and Geophysics1, 229–238.
     [Google Scholar]
  31. ReidA.B., FitzGeraldD. and McInernyP.2004. The meaning of, and limits on, Euler structural index and shape factor: Consequences for gravity and magnetic depth analysis. Australian Society of Exploration Geophysicists, Sydney. http://www.intrepid‐geophysics.com/ig/knowledgebase/aseg_2004/Reid_et_al_SI_Shape_Factor.pdf
     [Google Scholar]
  32. ReidA.B., AllsopJ.M., GranserH., MillettA.J. and SomertonI.W.1990. Magnetic interpretation in three dimensions using Euler deconvolution. Geophysics55, 80–91.
     [Google Scholar]
  33. RoestW.R., VerhoefJ. and PilkingtonM.1992. Magnetic interpretation using 3D analytic signal. Geophysics57, 116–125.
     [Google Scholar]
  34. RoyA.1962. Ambiguity in geophysical interpretation. Geophysics27, 90–99. doi: /10.1190/1.1438985.
     [Google Scholar]
  35. RoyL.2001. Source geometry identification by simultaneous use of structural index and shape factor. Geophysical Prospecting49, 159–164.
     [Google Scholar]
  36. SalemA.2005. Interpretation of magnetic data using analytic signal derivaties. Geophysical Prospecting53, 75–82.
     [Google Scholar]
  37. SalemA., RavatD., SmithR.S. and UshijimaK.2005. Interpretation of magnetic data using an enhanced local wave number (ELW) method. Geophysics70, L7–L12.
     [Google Scholar]
  38. SalemA., WilliamsS., FairheadD., SmithR. and RavatD.2008. Interpretation of magnetic data using tilt‐angle derivative. Geophysics73, L1–L10.
     [Google Scholar]
  39. SantosF.A.M.2010. Inversion of self‐potential of idealized bodies’ anomalies using particle swarm optimization. Computers and Geosciences36, 1185–1190.
     [Google Scholar]
  40. SanyiY., ShangxuW. and NanT.2009. Swarm intelligence optimization and its application in geophysical data inversion. Applied Geophysics6, 166–174.
     [Google Scholar]
  41. SatoA., SinghS.K. and FloresC.1993. Spectral analysis of gravity and magnetic anomalies due to a vertical circular cylinder. Geophysics48, 224–228.
     [Google Scholar]
  42. SenM.K. and StoffaP.L.1995. Global Optimization Methods in Geophysical Inversion.Elsevier Science Publishing Company, Netherlands.
     [Google Scholar]
  43. SharmaP.V.1976. Geophysical Methods in Geology.Elsevier Scientific, Amsterdam.
     [Google Scholar]
  44. ShawR.K. and AgarwalB.N.P.1997. A generalized concept of resultant gradient to interpret potential field maps. Geophysical Prospecting45, 1003–1011.
     [Google Scholar]
  45. ShawR.K. and SrivastavaS.2007. Particle swarm optimization: A new tool to invert geophysical data. Geophysics72, F75–F83.
     [Google Scholar]
  46. SkeelsD.C.1947. Ambiguity in gravity interpretation. Geophysics12, 43–56.
     [Google Scholar]
  47. SrivastavaS. and AgarwalB.N.P.2009. Interprettaion of self‐potential anomalies by enhanced local wave number technique. Journal of Applied Geophysics68, 259–268.
     [Google Scholar]
  48. SrivastavaS. and AgarwalB.N.P.2010. Inversion of the amplitude of the two‐dimensional analytic signal of magnetic anomaly by the particle swarm optimization technique. Geophysical Journal International182, 652–662.
     [Google Scholar]
  49. StanleyJ.M. and GreenR.1976. Gravity gradients and interpretation of the truncated plate. Geophysics41, 1370–1376.
     [Google Scholar]
  50. StavrevP.1997. Euler deconvolution using differential similarity transformations of gravity or magnetic anomalies. Geophysical Prospecting45, 207–246.
     [Google Scholar]
  51. StavrevP. and ReidA.B.2007. Degrees of homogeneity of potential fields and structural indices of Euler deconvolution. Geophysics72, L1–L2.
     [Google Scholar]
  52. StavrevP. and ReidA.B.2010. Euler deconvolution of gravity anomalies from thick contact/fault structures with extended negative structural index. Geophysics75, I51–I58.
     [Google Scholar]
  53. StützelT. and DorigoM.2002. A short convergence proof for a class of ant colony optimization algorithms. IEEE Transactions on Evolutionary Computation6, 358–365.
     [Google Scholar]
  54. TarantolaA.1987. Inverse Problem Theory.Elsevier, Amsterdam.
     [Google Scholar]
  55. TelfordW.M., GeldartL.P., SheriffR.E. and KeysD.A.1976. Applied Geophysics.Cambridge University Press.
     [Google Scholar]
  56. ThompsonD.T.1982. EULDPH‐ A new technique for making computer assisted depth estimates from magnetic data. Geophysics47, 31–37.
     [Google Scholar]
  57. WilkenD., WolzS., MullerC. and RabbelW.2009. FINOSEIS: a new approach to offshore‐ building foundation soil analysis using high resolution reflection seismic data and Scholte‐wave dispersion analysis. Journal of Applied Geophysics68, 117–123.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1002/nsg.123001
Loading
Applications of Ant Colony Optimization in determination of source parameters from total gradient of potential fields
Near Surface Geophysics 12, 373 (2013); https://doi.org/10.1002/nsg.123001
/content/journals/10.1002/nsg.123001
/content/journals/10.1002/nsg.123001
Loading

Data & Media loading...

  • Article Type: Research Article

Most Cited This Month Most Cited RSS feed

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