2-D Anticlinal Structure Modeling Using Feed-Forward Neural Network (FNN) Inversion of Profile Gravity Data: A Case Study from Iran

Document Type : Research Article


1 Ph.D. Student, Department of Geology, Faculty of Sciences, University of Isfahan, Isfahan, Iran

2 Instructor, Department of civil Engineering, University College of Nabi Akram, Tabriz, Iran

3 M.Sc. Graduated, Department of Earth Physics, Institute of Geophysics, University of Tehran, Tehran, Iran


The Anticlines are the main hydrocarbon traps on land or at sea. This structure is considered as the target of the many projects of gravity exploration all over the world. Artificial neural networks (ANNs) are used in order to solve prediction, estimation, and optimization problems. In this paper, the feed-forward neural network (FNN) is applied for modeling the anticlinal structure using gravity anomaly profile and the back propagation algorithm is used for artificial neural network training. Moreover, the scalene triangle model is employed to describe the geometry of anticlinal structure in analyzing gravity anomalies. In terms of neural network training, eight features among the synthetic gravity field variations curves along 22500 profiles are defined. These gravity profiles are computed based on different values of the scalene triangle parameters consisting of the top depth, bottom depth, limb angles and density contrast. The defined neural network contain three layers, eight neurons (the number of features) in the input layer, 30 neurons in the hidden layer and six neurons (the number of scalene triangle parameters) in the output layer. In order to evaluate the performance of the trained neural network, the specified features related to a synthetic model, with and without random noise, are applied as the input data to train neural network. The parameters estimation error by FNN is negligible. The proposed method is illustrated with a real gravity data set from Korand region, Iran. The inferred anticlinal structures are compared with the interpreted map of the seismic data.


Main Subjects

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.