Joint Lithologic Inversion

  • Li-Yun Fu
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 80)


Ambiguous dependence of observed data related to lithologic parameters suggests that both deterministic mechanism and statistical behaviour characterize practical lithologic inversion problems. The Caianiello neural network method is presented in this paper, including neural wavelet estimation, input signal reconstruction, and nonlinear factor optimization. A joint inversion scheme for porosity and clay-content estimations is established based on the combination of the Caianiello neural network with some deterministic petrophysical models. First, inverse neural wavelets are extracted using known solutions, and then the inverse-operator-based inversion is used to estimate an initial parameter model. Second, forward neural wavelets are estimated likewise, and then the forward-operator-based reconstruction can improve the initial parameter model. The scheme has been applied in a complex continental deposit in western China and significantly improves the spatial description of reservoirs.


Clay Content Seismic Data Joint Inversion Seismic Attribute Reservoir Characterization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Li-Yun Fu
    • 1
  1. 1.Institute of Geophysics and Planetary PhysicsUniversity of California at Santa CruzUSA

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