1887
Volume 12 Number 5
  • ISSN: 1569-4445
  • E-ISSN: 1873-0604

Abstract

ABSTRACT

This paper describes an application of 3D inversion of magnetic data to recover a susceptibility model from magnetic anomalies. For this purpose, the subsurface of the desired area of the magnetic anomaly is divided into a mesh with a large number of rectangular prisms with unknown susceptibilities. A Tikhonov cost function with multi‐term regularizers involving boundaries of susceptibility distribution and an edge‐preserving penalty function, as a tool to recover sharp boundaries, was used. Three methods (i.e., the U‐curve, Tikhonov‐curve and L‐curve methods) are applied to determine the optimum regularization parameter during the inversion process. Testing of the applied methods showed that the application of the U‐curve (a well‐known method in applied mathematics) in geophysical inverse problems and Tikhonov‐curve as a proposed technique can be appropriate candidates, like a common L‐curve method, for choosing the optimal regularization parameter. To avoid the natural tendency of magnetic structures to concentrate at the shallow depths in models created by inversion, a depth weighting function derived from information of the depth‐to‐the‐bottom of a generating source was applied. The AN‐EUL technique as a combination of the analytic signal and the Euler deconvolution methods is used to estimate the structural index of causative sources in order to construct an appropriate depth weighting function. Here, it is assumed that there is no remanent magnetization and the observed data are influenced by only the induced magnetization. A case study involving ground based measurements over a porphyry‐Cu deposit located in Kerman providence of Iran, Now Chun deposit, is included. The recovered 3D susceptibility model provided beneficial information for design of the exploration drilling programme. The susceptibility lows in the constructed model, in particular, their depths down to 410 m, coincides with the known locations of copper mineralization.

Loading

Article metrics loading...

/content/journals/10.3997/1873-0604.2014022
2013-12-01
2024-04-19
Loading full text...

Full text loading...

References

  1. AbediM., GholamiA., NorouziG.H. and FathianpourN.2013a. Fast inversion of magnetic data using Lanczos bidiagonalization method. Journal of Applied Geophysics90, 126–137.
    [Google Scholar]
  2. AbediM., NorouziG.H. and FathianpourN.2013b. Fuzzy Outranking Approach: A knowledge‐driven method for mineral prospectivity mapping. International Journal of Applied Earth Observation and Geoinformation21, 556–567.
    [Google Scholar]
  3. AbediM., TorabiS.A., NorouziG.H., HamzehM. and ElyasiG.R.2012a. PROMETHEE II: A knowledge‐driven method for copper exploration. Computers & Geosciences46, 255–263.
    [Google Scholar]
  4. AbediM., NorouziG.H. and BahroudiA.2012b. Support vector machine for multi‐classification of mineral prospectivity areas. Computers & Geosciences46, 272–283.
    [Google Scholar]
  5. AbediM., NorouziG. H. and TorabiS. A.2012c. Clustering of mineral prospectivity area as an unsupervised classification approach to explore Copper Deposit. Arabian Journal of Geosciences. doi:10.1007/s12517‐012‐0615‐5.
    [Google Scholar]
  6. AbediM., TorabiS.A., NorouziG.H. and HamzehM.2012d. ELECTRE III: A knowledge‐driven method for integration of geophysical data with geological and geochemical data in mineral prospectivity mapping. Journal of Applied Geophysics87, 9–18.
    [Google Scholar]
  7. AbediM. and NorouziG.H.2012. Integration of various geophysical data with geological and geochemical data to determine additional drilling for copper exploration. Journal of Applied Geophysics83, 35–45.
    [Google Scholar]
  8. Bertete‐AguirreH., CherkaevE. and OristaglioM.2002. Non‐smooth gravity problem with total variation penalization functional. Geophysical Journal International149, 499–507.
    [Google Scholar]
  9. BhattacharyyaB.K.1964. Magnetic anomalies due to prism‐shaped bodies with arbitrary polarization. Geophysics29, 517–531.
    [Google Scholar]
  10. BoulangerO. and ChouteauM.2001. Constraints in 3D gravity inversion. Geophysical Prospecting49, 265–280.
    [Google Scholar]
  11. Caratori TontiniF., CocchiL. and CarmiscianoC.2006. Depth‐to‐the‐bottom optimization for magnetic data inversion: Magnetic structure of the Latium volcanic region, Italy. Journal of Geophysical Research111, 1–17.
    [Google Scholar]
  12. CellaF. and FediM.2012. Inversion of potential field data using the structural index as weighting function rate decay. Geophysical Prospecting60, 313–336.
    [Google Scholar]
  13. CharbonnierP., Blanc‐FeraudL., AubertG. and BarlaudM.1997. Deterministic edge‐reserving regularization in computed imaging. IEEE Transactions on Image Processing6, 298–310.
    [Google Scholar]
  14. ChasseriauP. and ChouteauM.2003. 3D gravity inversion using a model of parameter covariance. Journal of Applied Geophysics52, 59–74.
    [Google Scholar]
  15. ClarkD.A.1999. Magnetic petrology of igneous intrusions‐Implications for exploration and magnetic interpretation. Exploration Geophysics20, 5–26.
    [Google Scholar]
  16. ElyasiG.R.2009. Mineral Potential Mapping in Detailed Stage Using GIS in One of Exploration Prospects of Kerman Province.Master of Science Thesis, University of Tehran. (in Persian)
    [Google Scholar]
  17. FarquharsonC.G.2008. Constructing piecewise‐constant models in multidimensional minimum‐structure inversions. Geophysics73, K1–K9.
    [Google Scholar]
  18. GemanS. and McClureD.E.1985. Bayesian image analysis: an application to single photon emission tomography. In: Proc. Statistical Computation Section, pp. 12–18, Amer. Statistical Assoc., Washington, DC.
    [Google Scholar]
  19. GholamiA. and HosseiniM.2011. A General Framework for Sparsity‐Based Denoising and Inversion. IEEE Transactions on Signal Processing59, 5202–5211.
    [Google Scholar]
  20. GholamiA. and SiahkoohiH.R.2010. Regularization of linear and nonlinear geophysical ill‐posed problems with joint sparsity constraints. Geophysical Journal International180, 871–882.
    [Google Scholar]
  21. GholamiA. and SiahkoohiH.R.2009. Simultaneous constraining of model and data smoothness for regularization of geophysical inverse problems. Geophysical Journal International176, 151–163.
    [Google Scholar]
  22. HansenP. and O’LearyD.1993. The use of the L‐curve in the regularization of discrete ill‐posed problems. SIAM Journal on Scientific Computing14, 1487–1503.
    [Google Scholar]
  23. HebertT. and LeahyR.1989. A generalized EM algorithm for 3‐D Bayesian reconstruction from Poisson data using Gibbs priors. IEEE Transactions on Medical Imaging8, 194–202.
    [Google Scholar]
  24. HezarkhaniA.2009. Hydrothermal fluid geochemistry at the Chah‐Firuzeh porphyry copper deposit, Iran, evidence from fluid inclusions. Journal of Geochemical Exploration101, 254–264.
    [Google Scholar]
  25. JohnD.A., AyusoR.A., BartonM.D., BlakelyR.J., BodnarR.J., DillesJ.H.et al.2010. Porphyry copper deposit model, chap. B of Mineral deposit models for resource assessment. U.S. Geological Survey Scientific Investigations Report 2010–5070–B, 169.
    [Google Scholar]
  26. LelièvreP.G.2009. Integrating geologic and geophysical data through advanced constrained inversions. PhD thesis, The University of BritishColumbia.
    [Google Scholar]
  27. LelièvreP.G., OldenburgD.W. and WilliamsN.C.2009. Integrating geological and geophysical data through advanced constrained inversions. Exploration Geophysics40, 334–341
    [Google Scholar]
  28. LelièvreP.G. and OldenburgD.W.2009. A 3D total magnetization inversion applicable when significant, complicated remanence is present. Geophysics74, L21–L30.
    [Google Scholar]
  29. LelièvreP.G. and OldenburgD.W.2006. Magnetic forward modelling and inversion for high susceptibility. Geophysical Journal International166, 76–90.
    [Google Scholar]
  30. LiY., ShearerS.E., HaneyM.M. and DannemillerN.2010. Comperhensive approaches to 3D inversion of magnetic data affected by remanent magnetization. Geophysics75, L1–L11.
    [Google Scholar]
  31. LiY. and OldenburgD.W.2003. Fast inversion of large‐scale magnetic data using wavelet transforms and a logarithmic barrier method. Geophysical Journal International152, 251–265.
    [Google Scholar]
  32. LiY. and OldenburgD.W.1998. 3‐D inversion of gravity data. Geophysics63, 109–119.
    [Google Scholar]
  33. LiY. and OldenburgD.W.1996. 3‐D inversion of magnetic data. Geophysics61, 394–408.
    [Google Scholar]
  34. MalehmirA., ThunehedH. and TryggvasonA.2009. A Case History: the Paleoproterozoic Kristineberg mining area, northern Sweden: Results from integrated 3D geophysical and geologic modeling, and implications for targeting ore deposits. Geophysics74, B9–B22.
    [Google Scholar]
  35. NamakiL., GholamiA. and HafiziM.A.2011. Edge‐preserved 2‐D inversion of magnetic data: an application to the Makran arc‐trench complex. Geophysical Journal International184, 1058–1068.
    [Google Scholar]
  36. Oldenburg. D.W., LiY. and EllisR.G.1997. Inversion of geophysical data over a copper gold porphyry deposit: A case history for Mt. Milligan. Geophysics62, 1419–1431.
    [Google Scholar]
  37. PignatelliA., NicolosiI. and ChiappiniM.2006. An alternative 3D source inversion method for magnetic anomalies with depth resolution. Annals of Geophysics49, 1021–1027.
    [Google Scholar]
  38. PilkingtonM.1997. 3‐D magnetic imaging using conjugate gradients. Geophysics62, 1132–1142.
    [Google Scholar]
  39. PortniaguineO. and ZhdanovM.S.2002. 3‐D magnetic inversion with data compression and image focusing. Geophysics67, 1532–1541.
    [Google Scholar]
  40. RaoD.B. and BabuN.R.1991. A rapid method for three‐dimensional modeling of magnetic anomalies. Geophysics56, 1729–1737.
    [Google Scholar]
  41. SacchiM.D. and UlrychT.J.1995. High resolution velocity gathers and offset space reconstruction. Geophysics60, 1169–1177.
    [Google Scholar]
  42. SalemA. and RavatD.2003. A combined analytic signal and Euler method (AN‐EUL) for automatic interpretation of magnetic data. Geophysics68, 1952–1961.
    [Google Scholar]
  43. ShamsipourP., ChouteauM. and MarcotteD.2011. 3D stochastic inversion of magnetic data. Journal of Applied Geophysics73, 336–347.
    [Google Scholar]
  44. StandoD.K. and RudnickiM.2007. Regularization parameter selection in discrete ill‐posed problems‐the use of the U‐curve. International Journal of Applied Mathematics and Computer Science17, 157–164.
    [Google Scholar]
  45. TelfordW.M., GeldartL.P. and SheriffR.E.2004. Applied Geophysics.2nd Editon. Cambridge University Press, pp. 760.
    [Google Scholar]
  46. ThomanM.W., ZongeK.L. and LiuD.2000. Geophysical case history of North Silver Bell, Pima County, Arizona‐A supergene‐enriched porphyry copper deposit. In: Practical Geophysics Short Course Selected Papers on CD‐ROM: Spokane, Washington, Northwest Mining Association 4, (eds R.B.Ellis , R.Irvine and F.Fritz ), Northwest Mining Association 1998, 42 pp.
    [Google Scholar]
  47. TikhonovA.N. and ArseninV.Y.1977. Solutions of Ill‐Posed Problems. Winston, Washington, D.C.
    [Google Scholar]
  48. VogelC.R.2002. Computational Methods for Inverse Problems.SIAM.
    [Google Scholar]
  49. WahbaG.1990. Spline models for observational data. In: CBMS‐NSF Regional Conference Series in Applied Mathematics59, SIAM, Philadelphia, PA.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.3997/1873-0604.2014022
Loading
/content/journals/10.3997/1873-0604.2014022
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