1887
ASEG2012 - 22nd Geophysical Conference
  • ISSN: 2202-0586
  • E-ISSN:

Abstract

Summary

Multiple geophysical data collected over the same area but based on fundamentally different physics usually contain complementary information about the subsurface. Joint inversion combines the complementary information by integrating all the geophysical data into a single inversion scheme. Thus, models resulting from joint inversion are more likely to represent the subsurface better than models derived from a single type of data. In this study, we consider joint inversion of seismic traveltimes and gravity data, and present a new joint inversion algorithm that uses petrophysical information as constraints. Using a synthetic example, we show that this new method can effectively build the available petrophysical information into inversion and improve the definition of both structure and physical properties. We also show that this method can deal with the situation where only partial petrophysical information about the subsurface is available. An important component of our method is applying fuzzy c-means (FCM) clustering algorithm to the recovered physical property distribution to generate a lithology map that is consistent with both the observed geophysical data and the a priori petrophysical information.

Loading

Article metrics loading...

/content/journals/10.1071/ASEG2012ab179
2012-12-01
2026-01-15
Loading full text...

Full text loading...

References

  1. Bezdek. J. C., 1981, Pattern recognition with fuzzy objective function algorithms: Plenum Press.
  2. Duda, R. O., Hart, P.E. and Stork, D.G., 2000, Pattern classification, 2nd Edition: Wiley-Interscience.
  3. Gallardo, L. A., and Meju, M. A., 2003, Characterization of heterogeneous near-surface materials by joint 2D inversion of DC resistivity and seismic data: Geophysical Research Letters, 30(13), 1658, doi:10.1029/2003GL017370.
  4. Haber, E., and Oldenburg, D.W., 1997, Joint inversion: A structural approach: Inverse Problems, 13, 63-77.
  5. Hathaway, R. J., and Bezdek, J.C., 2001, Fuzzy c-means clustering of incomplete data: IEEE Transactions on systems, Man, and Cybernetics, Part B: Cybernetics, 31, 735-744.
  6. Lelievre, P.G., Farquharson, C.G. and Hurich, C.A., 2010, Joint inversion of seismic traveltimes and gravity data on unstructured grids with application to mineral exploration: SEG Expanded Abstracts, 1758-1762.
  7. Paasche, H., Tronicke, J., Holliger, K., Green, A.G. and Maurer, H., 2006, Integration of diverse physical-property models: Subsurface zonation and petrophysical parameter estimation based on fuzzy c-means cluster analyses: Geophysics, 71, H33-H44.
  8. Vozoff, K. and Jupp, D.L.B., 1975, Joint Inversion of Geophysical Data: Geophysical Journal International, 42, 977–991.
/content/journals/10.1071/ASEG2012ab179
Loading
  • Article Type: Research Article
Keyword(s): gravity; Joint inversion; lithology differentiation; petrophysics; seismic traveltime
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