Al-Garni, M.A., 2013, Inversion of residual gravity anomalies using neural network. Arab J. Geosci., 6, 1509–1516.
Al-Nuaimy, W., Huang, Y., Nakhkash, M. and Eriksen, A., 2000, Automatic detection of buried utilities and solid objects with GPR using neural networks and pattern recognition. Journal of Applied Geophysics, 43, 157-165.
Asfahani, J. and Tlas, M., 2008, An automatic method of direct interpretation of residual gravity anomaly profiles due to spheres and cylinders. Pure Appl. Geophys., 165, 981-994.
Ashida, Y., 1996, Data processing of reflection seismic data by use of neural network. Journal of Applied Geophysics, 35, 89-98.
Bishop, C.M. and Hinton, G., 1995, Neural Networks for Pattern Recognition. Clarendon Press, Oxford.
Brown, M.P. and Poulton, M.M., 1996, Locating buried objects for environmental site investigations using Neural Networks. JEEG, 1, 179-188.
Chakravarthi, V. and Sundararajan, N., 2007, Marquardt optimization of gravity anomalies of anticlinal and synclinal structures with prescribed depth dependent density. Geophysical Prospecting, 55, 571–587.
Chakravarthi, V. and Sundararajan, N., 2008, TODGINV—A code for optimization of gravity anomalies due to anticlinal and synclinal structures with parabolic density contrast. Computers & Geosciences, 34, 955–966
El-Kaliouby, H.M. and Al-Garni, M.A., 2009, Inversion of self-potential anomalies caused by 2D inclined sheets using neural networks. J. Geophys. Eng., 6, 29–34.
Eshaghzadeh, A. and Hajian, A.R., 2018, 2D inverse modeling of residual gravity anomalies from Simple geometric shapes using Modular Feed-forward Neural Network. Annals of Geophysics, 61, 1, SE115.
Eslam, E., Salem, A. and Ushijima, K., 2001, Detection of cavities and tunnels from gravity data using a neural network. Explor. Geophys., 32, 204-208.
Hagan, M.T., Demuth, H.B. and Beale, M.H., 1996, Neural Network Design. PWS Publishing Company, Boston, Massachusetts.
Heiland, C.A., 1968, Geophysical Exploration. 2nd ed. Hafner Publishing Co., New York.
Huang, Z., Shimeld, J., Williamson, M. and Katsube, J., 1996, Permeability prediction with artificial neural network modeling in the venture gas field, offshore eastern Canada. Geophysics, 61, 422-436.
Macias, C., Sen M.K. and Stoffa P.L., 2000, Artificial neural networks for parameter estmation in geophysics. Geophysical Prospeting, 48, 21-47.
Osman, O., Muhittin, A.A. and Ucan O.N., 2006, A new approach for residual gravity anomaly profile interpretations: Forced Neural Network (FNN). Ann. Geofis., 49, 6.
Osman, O., Albora, A.M. and Ucan, O.U., 2007, Forward modeling with forced Neural networks for gravity anomaly profile. Math. Geol., 39, 593-605.
Pearson, W., Wiener, J. and Moll, R., 1990, Aeromagnetic structural interpretation using neural networks” A case study from the northern Denver-Julesberg Basin”, Ann International Meeting, Soc. Expl.Geophysics, Expanded abstract., 587-590.
Rao, K.G.C. and Avasthi, D.N., 1973, Analysis of the Fourier spectrum of the gravity effect due to two-dimensional triangular prism. Geophysical Prospecting, 21, 526–542.
Rao, B.S.R. and Murty, I.V.R., 1978, Gravity and Magnetic Methods of Prospecting. Arnold-Heinemann Publishers, New Delhi, India.
Rojas, R., 1996, Neural Networks: A Systematic Introduction. Springer-Verlag, Berlin.
Rummelhart, D.E., Hinton, G.E. and Williams, R.J., 1986, Learning internal representation by back propagating errors. Nature, 332, 533–536.
Salem, A., Elawadi, E., Abdelaziz, A. and Ushijima, K., 2001, Imaging subsurface cavities from microgravity data using Hopfield neural network, Proceeding of the 5th SEGJ International Symposium, Totyo, 199-205.