Pure and Applied Geophysics

, Volume 173, Issue 6, pp 2073–2085 | Cite as

Self-Constrained Euler Deconvolution Using Potential Field Data of Different Altitudes

Article
First Online:
Received:
Revised:
Accepted:

Abstract

Euler deconvolution has been developed as almost the most common tool in potential field data semi-automatic interpretation. The structural index (SI) is a main determining factor of the quality of depth estimation. In this paper, we first present an improved Euler deconvolution method to eliminate the influence of SI using potential field data of different altitudes. The different altitudes data can be obtained by the upward continuation or can be directly obtained by the airborne measurement realization. Euler deconvolution at different altitudes of a certain range has very similar calculation equation. Therefore, the ratio of Euler equations of two different altitudes can be calculated to discard the SI. Thus, the depth and location of geologic source can be directly calculated using the improved Euler deconvolution without any prior information. Particularly, the noise influence can be decreased using the upward continuation of different altitudes. The new method is called self-constrained Euler deconvolution (SED). Subsequently, based on the SED algorithm, we deduce the full tensor gradient (FTG) calculation form of the new improved method. As we all know, using multi-components data of FTG have added advantages in data interpretation. The FTG form is composed by x-, y- and z-directional components. Due to the using more components, the FTG form can get more accurate results and more information in detail. The proposed modification method is tested using different synthetic models, and the satisfactory results are obtained. Finally, we applied the new approach to Bishop model magnetic data and real gravity data. All the results demonstrate that the new approach is utility tool to interpret the potential field and full tensor gradient data.

Keywords

Euler deconvolution Gravity data Magnetic data Full tensor gradient 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgments

The authors would like to thank two anonymous reviewers for their valuable and constructive comments and suggestions that improved this work. This research was partly supported by the Fundamental Research Funds for the Central Universities (Grant Nos. lzujbky-2015-65 and lzujbky-2014-133) and the National Natural Science Foundation of China (Grant Nos. 41172163 and 41371033).

References

  1. Davis K., Li Y. (2009), Enhancement of depth estimation techniques with amplitude analysis, SEG Annual Meeting. Society of Exploration Geophysicists, 2009.Google Scholar
  2. Fairhead, J. D., Williams S. E., and Flanagan G. (2004), Testing magnetic local wavenumber depth estimation methods using a complex 3D test model, 74th Annual International Meeting, SEG Expanded Abstracts, 742–745.Google Scholar
  3. Fedi, M. (2007), DEXP: a fast method to determine the depth and the structural index of potential fields sources, Geophysics, 72, I1–I11.Google Scholar
  4. Fedi, M., Florio, G. & Quarta, T. (2009), Multriridge analysis of potential fields: geometrical method and reduced Euler deconvolution, Geophysics, &4, L53–L65.Google Scholar
  5. Fedi, M., Florio, G. & Cascone, L. (2012), Multiscale analysis of potential fields by a ridge consistency criterion: the reconstruction of the Bishop basement, Geophysical Journal International, 188(1), 103–114.Google Scholar
  6. Fedi, M., Florio, G. & Paoletto, V. (2015), MHODE: a local-homogeneity theory for improved source-parameter estimation of potential fields, Geophysical Journal International, 202, 887–900.Google Scholar
  7. FitzGerald D., Reid A., Milligan P., et al. (2006), Hybrid Euler magnetic basement depth estimation: Integration into 3D Geological Models, Australian Earth Sciences Convention 2006, Melbourne.Google Scholar
  8. Florio, G. & Fedi, M. (2014) Multiridge Euler deconvolution, Geophysical prospecting, 62(2), 333–351.Google Scholar
  9. Florio, G., Fedi, M., Pašteka, R. (2014) On the estimation of the structural index from low-pass filtered magnetic data. Geophysics, 79(6), J67–J80.Google Scholar
  10. Hsu, S. (2002), Imaging magnetic sources using Euler’s equation, Geophysical prospecting, 50(1), 15–25.Google Scholar
  11. Keating P.B. (1998), Weighted Euler deconvolution of gravity data. Geophysics, 63, 1595–1603.Google Scholar
  12. Klingele E.E., Marson I. and Kahle H-G. (1991), Automatic interpretation of gravity gradiometric data in two dimensions: vertical gradient. Geophysical Prospecting 39, 407–434.Google Scholar
  13. Mickus K.L., and Hinojosa J.H. (2001), The complete gravity gradient tensor derived from the vertical component of gravity: a Fourier transform technique. Journal of Applied Geophysics, 46, 159–174.Google Scholar
  14. Mushayandebvu M.F., Van Driel P., Reid A.B. and Fairhead J.D. (2001), Magnetic source parameters of two-dimensional structures using extended Euler deconvolution, Geophysics, 66, 814–823.Google Scholar
  15. Ravat D., Wang B., Wildermuth A. and Taylor P.T. (2002), Gradients in the interpretation of satellite-altitude magnetic data: an example from central Africa. Journal of Geodynamics 33, 131–142.Google Scholar
  16. Reid, A. B., Fitzgerald D., and Flanagan G. (2005), Hybrid Euler magnetic basement depth estimation: Bishop 3D tests, 75th Annual International Meeting, SEG, Expanded Abstracts, 671–673.Google Scholar
  17. Reid A.B., Ebbing J., & Webb S.J. (2012), Comment on ‘A crustal thickness map of Africa derived from a global gravity field model using Euler deconvolution’ by Getachew E. Tedla, M. van der Meijde, A. A. Nyblade and F. D. van der Meer. Geophysical Journal International, 189, 1217–1222.Google Scholar
  18. Reid A.B., Ebbing J. & Webb S.J. (2014), Avoidable Euler Errors - the use and abuse of Euler deconvolution applied to potential fields, Geophysical Prospecting, 62, 1162–1168.Google Scholar
  19. Reid A.B., Allsop J. M., Grancer H., Millet A. J., & Somerton I.W. (1990), Magnetic interpretation in three dimensions using Euler deconvolution, Geophysics, 55, 80–91.Google Scholar
  20. Reid A.B., & Thurston J.B. (2014), The structural index in gravity and magnetic interpretation: Errors, uses, and abuses, Geophysics 79, J61–J66.Google Scholar
  21. Salem A., Williams S., Fairhead D., Smith R., & Ravat D. (2008), Interpretation of magnetic data using tilt-angle derivatives, Geophysics, 73, L1–L10.Google Scholar
  22. Stavrev P.Y. (1997), Euler deconvolution using differential similarity transformations of gravity or magnetic anomalies, Geophysical Prospecting, 45, 207–246.Google Scholar
  23. Stavrev P., & Reid A. (2007), Degrees of homogeneity of potential fields and structural indices of Euler deconvolution, Geophysics, 72, L1–L12.Google Scholar
  24. Stavrev P., & Reid A. (2010), Euler deconvolution of gravity anomalies from thick contact/fault structurals with extended negative structural index, Geophysics, 75, I51–I58.Google Scholar
  25. Thompson D.T. (1982), EULDPH: A new technique for making computer assisted depth estimates from magnetic data, Geophysics, 47, 31–37.Google Scholar
  26. Williams, S. E., Fairhead J. D., and Flanagan G. (2002), Realistic models of basement topography for depth to magnetic basement testing, 72nd Annual International Meeting, SEG, Expanded Abstracts, 814–817.Google Scholar
  27. Williams, S. E., Fairhead J. D., and Flanagan G. (2005), Comparison of grid Euler deconvolution with and without 2D constraints using a realistic 3D magnetic basement model, Geophysics, 70, L13–L21.Google Scholar
  28. Yao C. L., Guan Z. N., Wu Q. B., Zhang Y. W., Liu H. J. (2004), An analysis of Euler deconvolution and its improvement, Geophysical & Geochemical exploration, 28, 150–155. (In Chinese with English abstract)Google Scholar
  29. Zhang C.Y., Mushayandebvu M.F., Reid A.B., Fairhead J.D., & Odegard M.E. (2000), Euler deconvolution of gravity tensor gradient data, Geophysics, 65, 512–520.Google Scholar
  30. Zhou W. N., Li J. Y. & Du X. J. (2014), Semiautomatic interpretation of microgravity data from subsurface cavities using curvature gradient tensor matrix, Near Surface Geophysics, 12, 579–586.Google Scholar

Copyright information

© Springer International Publishing 2016

Authors and Affiliations

  1. 1.School of Earth Sciences, Key Laboratory of Mineral Resources in Western China (Gansu Province)Lanzhou UniversityLanzhouChina
  2. 2.Exploration and Production Research Institute, SINOPECBeijingChina
  3. 3.College of Petrochemical TechnologyLanzhou University of TechnologyLanzhouChina

Personalised recommendations