Neural Network Solution of an Inverse Problem with Integration of Geophysical Methods on Recovered Data: Training with Noise Addition
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-50381-8_41
Reference12 articles.
1. Spichak, V., Fukuoka, K., et al.: ANN reconstruction of geoelectrical parameters of the Minou fault zone by scalar CSAMT data. J. Appl. Geophys. 49(1–2), 75–90 (2002). https://doi.org/10.1016/S0926-9851(01)00100-8
2. Montahaei, M., Oskooi, B.: Magnetotelluric inversion for azimuthally anisotropic resistivities employing artificial neural networks. Acta Geophys. 62(1), 12–43 (2014). https://doi.org/10.2478/s11600-013-0164-7
3. Isaev, I., Obornev, E., et al.: Neural network recognition of the type of parameterization scheme for magnetotelluric data. In: Studies in Computational Intelligence, vol. 799, pp. 176–183 (2018). https://doi.org/10.1007/978-3-030-01328-8_19
4. Roux, E., Moorkamp, M., et al.: Joint inversion of long-period magnetotelluric data and surface-wave dispersion curves for anisotropic structure: application to data from Central Germany. Geophys. Res. Lett. 38(5), L05304 (2011). https://doi.org/10.1029/2010GL046358
5. Akca, İ, Günther, T., et al.: Joint parameter estimation from magnetic resonance and vertical electric soundings using a multi-objective genetic algorithm. Geophys. Prospect. 62(2), 364–376 (2014). https://doi.org/10.1111/1365-2478.12082
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