1887
Volume 64, Issue 5
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

We propose a fast method for imaging potential field sources. The new method is a variant of the “Depth from Extreme Points,” which yields an image of a quantity proportional to the source distribution (magnetization or density). Such transformed field is here transformed into source‐density units by determining a constant with adequate physical dimension by a linear regression of the observed field versus the field computed from the “Depth from Extreme Points” image. Such source images are often smooth and too extended, reflecting the loss of spatial resolution for increasing altitudes. Consequently, they also present too low values of the source density. We here show that this initial image can be improved and made more compact to achieve a more realistic model, which reproduces a field consistent with the observed one. The new algorithm, which is called “Compact Depth from Extreme Points” iteratively produces different source distributions models, with an increasing degree of compactness and, correspondingly, increasing source‐density values. This is done through weighting the model with a compacting function. The compacting function may be conveniently expressed as a matrix that is modified at any iteration, based on the model obtained in the previous step. At any iteration step the process may be stopped when the density reaches values higher than prefixed bounds based on known or assumed geological information. As no matrix inversion is needed, the method is fast and allows analysing massive datasets. Due to the high stability of the “Depth from Extreme Points” transformation, the algorithm may be also applied to any derivatives of the measured field, thus yielding an improved resolution. The method is investigated by application to 2D and 3D synthetic gravity source distributions, and the imaged sources are a good reconstruction of the geometry and density distributions of the causative bodies. Finally, the method is applied to microgravity data to model underground crypts in St. Venceslas Church, Tovacov, Czech Republic.

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/content/journals/10.1111/1365-2478.12365
2016-04-07
2024-04-25
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References

  1. BarbosaV.C.F. and SilvaJ.B.C.1994. Generalized compact gravity inversion. Geophysics59, 57–68.
    [Google Scholar]
  2. BlizkovskyM.1979. Processing and applications of microgravity surveys. Geophysical Prospecting27, 848–861.
    [Google Scholar]
  3. CellaF. and FediM.2012. Inversion of potential field data using the structural index as weighting function rate decay. Geophysical Prospecting60(2), 313–336.
    [Google Scholar]
  4. CooperG.R.J.2006. Interpreting potential field data using continuous wavelet transforms of their horizontal derivatives. Computer & Geosciences32, 984–992.
    [Google Scholar]
  5. FediM. and RapollaA.1999. 3‐D inversion of gravity and magnetic data with depth resolution. Geophysics64, 264–276.
    [Google Scholar]
  6. FediM.2007. DEXP: A fast method to determine the depth and the structural index of potential fields sources. Geophysics72(1), I1–I11.
    [Google Scholar]
  7. FediM., FlorioG. and QuartaT.2009. Multiridge analysis of potential fields: geometric method and reduced Euler deconvolution. Geophysics74(4), L53–L65.
    [Google Scholar]
  8. FediM. and PilkingtonM.2012. Understanding imaging methods for potential field data. Geophysics77(1), G13–G24.
    [Google Scholar]
  9. FediM., FlorioG. and CasconeL.2012. Multiscale analysis of potential fields by a ridge consistency criterion: the reconstruction of the Bishop basement. Geophysical Journal international188, 103–114.
    [Google Scholar]
  10. IalongoS., FediM. and FlorioG.2014. Invariant models in the inversion of gravity and magnetic fields and their derivatives. Journal of Applied Geophysics110, 51–62.
    [Google Scholar]
  11. LastB.J. and KubikK.1983. Compact gravity inversion. Geophysics48, 713–721.
    [Google Scholar]
  12. LiY. and OldenburgD.W.1998. 3D inversion of gravity data. Geophysics63, 109–119.
    [Google Scholar]
  13. MenkeW.1984. Chapter 3. In: Geophysical Data Analysis: Discrete Inverse Theory. Academic Press.
    [Google Scholar]
  14. PaolettiV., IalongoS., FlorioG., FediM. and CellaF.2013. Self‐constrained inversion of potential fields. Geophysical Journal international195, 854–869.
    [Google Scholar]
  15. PaolettiV., HansenP.C., HansenM.F. and FediM.2014. A computationally efficient tool for assessing the depth resolution in large‐scale potential‐field inversion. Geophysics79(4), A33–A38.
    [Google Scholar]
  16. ParkerR.L.1972. The inverse theory with grossly inadequate data. Geophysical Journal of the Royal Astronomical Society29, 123–138.
    [Google Scholar]
  17. PilkingtonM.1997. 3‐D magnetic imaging using conjugate gradients. Geophysics62, 1132–1142.
    [Google Scholar]
  18. PilkingtonM.2009. 3D magnetic data‐space inversion with sparseness constraints. Geophysics74, L7–L15.
    [Google Scholar]
  19. PortniaguineO. and ZhdanovM.S.1999. Focusing geophysical inversion images. Geophysics64, 874–887.
    [Google Scholar]
  20. PortniaguineO. and ZhdanovM.S.2002. 3D magnetic inversion with data compression and image focusing. Geophysics67, 1532–1541.
    [Google Scholar]
  21. ReidA.B., AllsopJ.M., GranserH., MillettA.J. and SomertonI.W.1990. Magnetic interpretation in three dimensions using Euler deconvolution. Geophysics55, 80–91.
    [Google Scholar]
  22. SilvaJ.B.C., MedeirosW. and BarbosaV.2001. Potential‐field inversion: choosing the appropriate technique to solve a geologic problem. Geophysics66, 511–520.
    [Google Scholar]
  23. WanL. and ZhdanovM.S.2013. Iterative migration of gravity and gravity gradiometry data. 83rd SEG meeting, Houston, USA, Expanded Abstracts, P1211.
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