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Information Journal Paper

Title

1D INVERSE MODELING OF GEOELECTRIC DATA USING ARTIFICIAL NEURAL NETWORKS

Pages

  83-95

Abstract

INVERSE MODELING of GEOELECTRICal data is an important method exploration and underground water studies. Intrinsic nonlinear nature of geophysical data is obstacle in modeling procedure. Here we describe a study of the applicability of neural networks to solving some geophysical inverse problems. In particular, we study the problem of obtaining formation resistivities and layer thicknesses from vertical electrical sounding (VES) data.

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    Cite

    APA: Copy

    KARIMI, ARASH, MORADZADEH, A., & KAMKAR ROUHANI, A.A.GH.. (2009). 1D INVERSE MODELING OF GEOELECTRIC DATA USING ARTIFICIAL NEURAL NETWORKS. JOURNAL OF EARTH AND RESOURCES, 1(3), 83-95. SID. https://sid.ir/paper/164286/en

    Vancouver: Copy

    KARIMI ARASH, MORADZADEH A., KAMKAR ROUHANI A.A.GH.. 1D INVERSE MODELING OF GEOELECTRIC DATA USING ARTIFICIAL NEURAL NETWORKS. JOURNAL OF EARTH AND RESOURCES[Internet]. 2009;1(3):83-95. Available from: https://sid.ir/paper/164286/en

    IEEE: Copy

    ARASH KARIMI, A. MORADZADEH, and A.A.GH. KAMKAR ROUHANI, “1D INVERSE MODELING OF GEOELECTRIC DATA USING ARTIFICIAL NEURAL NETWORKS,” JOURNAL OF EARTH AND RESOURCES, vol. 1, no. 3, pp. 83–95, 2009, [Online]. Available: https://sid.ir/paper/164286/en

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