Research on Multi-Attributes Neural Network Inversion in Coalmines’ 3D Seismic Exploration

Wen Ming Liu; Wan Jin Liu; Hong Wei Wang; Xin Zhang

doi:10.4028/www.scientific.net/AMM.556-562.5539

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 556-562Research on Multi-Attributes Neural Network...

Research on Multi-Attributes Neural Network Inversion in Coalmines’ 3D Seismic Exploration

Abstract:

The conventional inversion can only get P-impedance data, but neural network inversion can obtain various kinds of well-log attributes data by getting the nonlinear relationship through training the data of seismic attributes and well-log attributes, and then apply the nonlinear relationship to the whole seismic data volume. In this paper, we use multilayer feed forward neural network, probability neural network and radial basis function neural network to carry out the log-density inversion research, and obtain three pseudo density data volumes. We compare the effect of the inversion results and analysis the density distribution in spatial domain through section and slice data, at last we predict the stability of the coal seam’s floor in more reasonable way.

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Periodical:

Applied Mechanics and Materials (Volumes 556-562)

Pages:

5539-5543

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.556-562.5539

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Online since:

May 2014

Authors:

Wen Ming Liu*, Wan Jin Liu, Hong Wei Wang, Xin Zhang

Keywords:

Neural Network Inversion, Pseudo Density, Seismic Attribute, Well-Log Attributes

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* - Corresponding Author

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