تخمین شکل و عمق گنبدهای نمکی با استفاده از تفسیر داده‌های گرانی‌سنجی به روش شبکه‌های عصبی مصنوعی چندلایه

- حاجیان، ع.ابراهیم زاده اردستانی،و.، لوکس،ک.، سقاییان نژاد، م.،1388، اکتشاف قنات‌های زیرزمینی مدفون از طریق شبکه‌های عصبی مصنوعی و با استفاده از داده‌های میکروگرانی‌سنجی، فیزیک زمین و فضا 35(1)، 9-15.

- حاجیان، ع.، ابراهیم زاده اردستانی،و.، ضیایی، ز.،1383، تخمین عمق بی‌هنجاری‌های گرانی با استفاده از شبکه‌های عصبی مصنوعی، کنفرانس مهندسی معدن ایران، دانشگاه تربیت مدرس.

- منهاج، م.، مبانی شبکه‌های عصبی، انتشارات دانشگاه صنعتی امیرکبیر(پلی تکنیک)، چاپ سوم 1384.

-Aghajani, H., Moradzadeh, A., and Zeng, H.,2009, Normalizd full gradient of gravity

anomaly method and its application to the Mobrunsulfide body, Canada. World Applied

Science Journal 6(3), 392-400.

-Aghajani, H. Moradzadeh, A. and Zeng, H., 2009 “Estimation of Depth to Anomalous Body from Normalized Full Gradient of Gravity Anomaly” Journal of Earth Science, 20(6),1012–1016.

- Albora A.M., Uçan O.M., Özmen A., Özkan T., 2001,Separation of Bouguer Anomaly Map Using Cellular Neural Network, Journal of Applied Geophysics,46,129-142.

- Burr, D. J., 1987, experiments with a connectionist text reader, in proceedings of a first international conference on neural networks, San Diego, CA., 4, 717-724.

- Cottrel G. W., Munro, p., and Zipser, D., 1987, image compression by backpropagation, an example of extensional programing. Advances in cognitive science, 3, 78-89.

 

- Gret, A. A., Klingele, E. E., 1998, Application of Artificial Neural Networks for Gravity Interpretation in Two Dimension, Report No.279, Institute of Geodesy and Photogrammetery, Swiss Federal Institute of Technology, Zurich.

- HajianA., Ardestani V.E., Lucas C. 2011, Depth estimation of gravity anomalies using Hopfield Neural Networks, journal of the earth & space physics,37(2),1-9.

- McCuloch, W., and Pitts, W., 1943, A logical calculus of the ideas immanent in nervous activity, Bulletin of Mathematical Biophysics., 5, 115-133.

- Menhaj, M.B., 1999, Application of computational intelligence in control, first edition, professor Hesabi publishers, 236, (in Persian).

-Nabighian, M. N..GrauchV. J. S, HansenR. O. , LafehrT. R. , Li1,Y. PeirceJ. W. ,

PhillipsJ. D., and RuderM. E., 2005, The historical development of the magnetic method in exploration, Geophysics, 70(6), 33–61.

- Osman O., 2006, A new approach for residual gravity anomaly profile interpretations: Forced Neural Networks (FNN), Annals of Geophysics, 49(6).

- Osman O., AlboraA. M., UcanO. N.,2007, Forward Modelling with Forced Neural Networks for Gravity Anomaly Profile, Journal of Mathematical Geology, 39,593-605.

- Parker, R. L., 1977, Linear inference and under parameterized models, ReviewGeophysics, 15, 446-456.

- Parker, D. B., 1982, Learning logic: invention report, office of technology licensing, Stanford University, 1, 64-81.

- Parker, D. B., 1987, Second order back propagation. Implementing an optimal O(n) approgsimation to newton's method as an artificial neural network: MIT Press, 1, 318-362.

- Rumelhart, D. E., Hinton, G. E., and Williams, R. J., 1986, Learning internal representations by error propagations: Parallel distributed processing, MIT Press, 1, 318-362.

- Salem, A., and Ushijima, K., 2001, Detection of cavities and tunnels from gravity data using a neural network, exploration geophysics,32, 204-208.

- Sejnovski, T. J., and Rosenberg, C. R., 1987, Parallel networks that learn to pronounce English text: Complex systems 3., 145-168.

- Salem A., 2011, Multi-deconvolution analysis of potential field data, Journal of Applied Geophysics, vol. 74, p. 151-156.

- Salem, A. and Elawadi, E., and K. Ushijima2003, Short note: Depth determination from residual gravity anomaly using a simple formula; Computer and Geosciences, 29, 801-804.

- Werbos, P. J., 1974, Beyond regression: New tools for prediction and analysis in the behavioural sciences, Master thesis, Harvard University.