3D gravity inversion plays an important l role in the quantitative interpretation of practical gravity data. One of the key issues with 3D inversion of gravity data is the multiplicity. Combining multiple geophysical data is an advantageous means of reducing multiplicity. However, establishing a petrophysical relationship between different physical data is a major difficulty. We propose a process for petrophysical modeling using machine learning and multiple geostatistics.Based on the Fuzzy c-means (FCM) and an adaptive cross- variogram function fitting(which make it possible to introduce the cross-variogram in multivariate geostatistics into the traditional objective function), we can better suggest the spatial correlation of petrophysical. Synthetic example demonstrated the feasibility and reliability of our method.


Article metrics loading...

Loading full text...

Full text loading...


  1. Tikhonov, A.N., and V. Y.Arsenin
    , 1977, Solutions of ill-posed problems: V. H.Winston & Sons (translated from the Russian, Preface by translation editor Fritz John, Scripta Series in Mathematics).
    [Google Scholar]
  2. Gallardo, L.A., and M. A.Meju
    , 2004, Joint two-dimensional DC resistivity and seismic travel time inversion with cross-gradients constraints: Journal of Geophysical Research, 109, B03311.
    [Google Scholar]
  3. Zhdanov, M.S., A. V.Gribenko, and G. A.Wilson
    , 2012, Generalized joint inversion of multimodal geophysical data using Gramian constraints: Geophysical Research Letters, 39, no. 9, L09301.
    [Google Scholar]
  4. Sun, J., and Y.Li
    , 2017, Joint inversion of multiple geophysical and petrophysical data using generalized fuzzy clustering algorithms: Geophysical Journal International, 208, 1201–1216.
    [Google Scholar]

Data & Media loading...

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