___

• Abdelrahman, E.M., Bayoumi, A.I., ve El-Araby, H.M., 1991. A least-squares minimizati- on approach to invert gravity data, Ge- ophysics, 56, 1, 115-118.
• Basu, A., ve Frazer, L.N., 1990. Rapid determi- nation of critical temperature in simula- ted annealing inversion, Science, 249, 1409-1412.
• Başokur, A.T., 2002. Doğrusal ve Doğrusal Ol- mayan Problemlerin Ters-Çözümü. Je- ofizik Mühendisleri Odası Eğitim Yayın- ları, Ankara.
• Beiki, M., ve Pedersen, L.B., 2010. Eigenvector analysis of gravity gradient tensor to lo- cate geologic bodies, Geophysics, 75, 6, I37-I49.
• Butler, D.K., 1984. Microgravimetric and gravity gradient techniques for detection of subsurface cavities, Geophysics, 49, 7, 1084-1096.
• Calderón-Macías, C., Sen, M.K., ve Stoffa, P.L.,
• Hopfield neural networks, and
• mean field annealing for seismic de
• convolution and multiple attenuation,
• Geophysics, 62, 3, 992-1002.
• Calderón-Macías, C., Sen, M.K. ve Stoffa, P.L.,
• Automatic NMO correction and
• velocity estimation by a feedforward
• neural network, Geophysics, 63, 5, 1696-1707.
• Chamoli, A., Srivastava, R.P. ve Dimri, V.P., 2006. Source depth characterization of poten- tial field data of Bay of Bengal by conti- nuous wavelet transform, Indian Journal of Marine Sciences, 35, 3, 195-204.
• Dosso, S.E., ve Oldenburg, D.W., 1991. Mag- netotelluric appraisal using simulated annealing, Geophysical Journal Inter- national, 106, 370-85.
• Elawadi, E., Salem, A., ve Ushijima, K., 2001. Detection of cavities and tunnels from gravity data using a neural network, Exploration Geophysics, 32, 4, 204-208.
• Gupta, O.P., 1983. A least-squares approach to depth determination from gravity data, Geophysics, 48, 3, 357-360.
• Günaydın, K., ve Günaydın, A., 2008. Peak gro- und acceleration prediction by artificial neural networks for Northwestern Tur- key, Mathematical Problems in Engine- ering, 2008.
• Hajian, A., 2004. Depth Estimation of Gravity Anomalies by Neural Network, M.Sc.
• Thesis, Tehran University, Iran (in Per- sian).
• Hajian, A., Styles, P. ve Zomorrodian, H., 2011. Depth estimation of cavities from mic- rogravity data through multi adaptive neuro fuzzy interference system. In:
• “Near Surface” 2011, Proc. 17th Euro
• pean Meeting of Environmental and En
• gineering Geophysics, 12-14 Septem
• ber 2011, Leicester, UK.
• Hajian, A., Zomorrodian, H., ve Styles, P., 2012. Simultaneous Estimation of Shape Fac- tor and Depth of Subsurface Cavities from Residual Gravity Anomalies using Feed-Forward Back-Propagation Ne- ural Networks, Acta Geophysica, 60, 1043-1075.
• Haykin, S., 1999. Neural Networks: A Compre- hensive Foundation, 2nd ed., Prentice- Hall Inc., Englewood Cliffs.
• Ho, T. L., 2009. 3-D inversion of borehole-to- surface electrical data using a back- propagation neural network, Journal of Applied Geophysics, 68, 4, 489-499.
• Holland, J., 1975. Adaptation in Natural and Ar- tificial Systems, University of Michigan Press.
• Huang, Z., Shimeld, J., Williamson, M., ve Kat- sube, J., 1996. Permeability prediction with artificial neural network modeling in the Ventura gas field, offshore eas- tern Canada, Geophysics, 61, 2, 422- 436.
• Landa, E., Beydoun, W., ve Tarantola, A., 1989. Reference velocity model estimation from prestack waveforms: coherency optimization by simulated annealing, Geophysics, 54, 984-990.
• Langer, H., Nunnari G., ve Occhipinti L., 1996. Estimation of seismic waveform gover- ning parameters with neural networks, Journal of Geophysical Research, 101, B9, 20109-20118.
• Lee, K. Y., ve Mohamed, P. S., 2002. A real-co- ded genetic algorithm involving a hybrid crossover method for power plant control system design, in Proceedings of the 2002 Congress on Evolutionary Computation, pp. 1069-1074.
• Levenberg, K., 1944. A Method for the solution of certain nonlinear problems in least squares. The Quarterly of Applied Mat- hematics, 2, 164-168.
• Lines, L.R. ve Treitel, S., 1984. A review of least- squares inversion and its application to geophysical problems, Geophysical Prospecting, 32, 2, 159-186.
• Luke, S., 2009. Essentials of metaheuristics. Lulu, Retrieved January 20th, 2012, from http://cs.gmu.edu/~sean/book/ metaheuristics/.
• Marquardt, D.W., 1963. An algorithm for le- ast-squares estimation of nonlinear parameters, Journal of the Society for Industrial and Applied Mathematics, 11(2): 431-441.
• McCulloch, W.S., ve Pitts, W., 1943. A logical calculus of the ideas immanent in ner- vous activity, Bulletin of Mathematical Biology, 5, 4, 115-133.
• Oruç, B., 2010. Depth estimation of simple ca- usative sources from gravity gradient tensor invariants and vertical compo- nent, Pure and Applied Geophysics, 167, 10, 1259-1272.
• Oruç, B., 2012. Teori ve Örneklerle Jeofizikte Modelleme. Umuttepe Yayınları, Koca- eli.
• Osman, O., Albora, A.M., ve Ucan O.N., 2007. Forward modeling with forced neural networks for gravity anomaly profile, Mathematical Geology, 39, 6, 593-605.
• Poulton, M.M., Sternberg, B.K., ve Glass, C.E.,
• Location of subsurface targets
• in geophysical data using neural net
• works, Geophysics, 57, 12, 1534- 1544.
• Reid, A.B., Allsop, J.M., Granser, H., Millett, A.J., ve Somerton, I.W., 1990. Magnetic in- terpretation in three dimensions using Euler deconvolution, Geophysics, 55, 1, 80-91.
• Roest, W. R., Verhoef, J., ve Pilkington, M.,
• Magnetic interpretation using 3-D
• analytic signal: Geophysics, 57, 116– 125.
• Roth, G., ve Tarantola, A., 1994. Neural net- works and inversion of seismic data,
• Journal of Geophysical Research, 99, B4, 6753-6768.
• Roy, A., 1962. Ambiguity in geophysical interp- retation, Geophysics, 27, 1, 90-99.
• Roy, L., Agarwal, N.P., ve Shaw, R.K., 2000. A new concept in Euler deconvolution of isolated gravity anomalies, Geophysi- cal Prospecting, 48, 3, 559-575.
• Rumelhart, D.E., Hinton, G.E., ve Williams, R.J.,
• Learning internal representati
• on by error back propagation. In: D.E.
• Rumelhart and J.L. Mc Clelland (eds.),
• Parallel Distributed Processing: Exp
• lorations in the Microstructure of Cog
• nition. Vol. 1. Foundations, MIT Press,
• Cambridge, USA, 318-362.
• Salem, A., Elawadi, E., ve Ushijima, K., 2003. Short note: Depth determination from residual gravity anomaly data using a simple formula, Computer and Geosci- ence, 29, 6, 801-804.
• Sen, M. K., ve Stoffa, P. L., 1991. Nonlinear mul- tiparameter optimization using genetic algorithms: Inversion of plane wave seismograms, Geophysics, 56, 1794- 1810.
• Sen, M.K., ve Stoffa, P. L., 1992. Seismic wa- veform inversion using global optimiza- tion. Journal of Seismic Exploration, 1, 9-27.
• Szu, H., ve Hartley, R., 1987. Fast Simulated Annealing, Physical Letters A, 122, No. 3, 157-162.
• Thompson, D.T., 1982. EULDPH: A new tech- nique for making computer-assisted depth estimates from magnetic data, Geophysics, 47, 1, 31-37.
• Vestergaard, P. D., ve Mosegaard, K. 1991. Inversion of post-stack seismic data using simulated annealing, Geophysi- cal Prospecting, 39, 613-624.
• Wang, L.X., ve Mendel, J.M., 1992. Adaptive minimum prediction-error deconvoluti- on and source wavelet estimation using Hopfield neural networks, Geophysics, 57, 5, 670-679.
• Zhang, Y., ve Paulson K.V., 1997. Magnetotellu- ric inversion using regularized Hopfield neural networks, Geophysical Prospec- ting, 45, 5, 725-743.