(Danesh Tadbir Zima Institute, Chaloos, Iran)
(Roshdiyeh Higher Education Institute, Tabriz, Iran)
(Islamic Azad University, Science and Research Branch, Tehran, Iran)
Yıl: 2020Cilt: 0Sayı: 161ISSN: 0026-4563 / 2651-3048Sayfa Aralığı: 33 - 47İngilizce

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2D inverse modeling of the gravity field due to a chromite deposit using the Marquardt’s algorithm and forced neural network
In this paper, two modeling method are employed. First, a method based on the Marquardt’s algorithm is presented to invert the gravity anomaly due to a finite vertical cylinder source. The inversion outputs are the depth to top and bottom, and radius parameters. Second, Forced Neural Networks (FNN) for interpreting the gravity field as try to fit the computed gravity in accordance with the estimated subsurface density distribution to the observed gravity. To evaluate the ability of the methods, those are employed for analyzing the gravity anomalies from assumed models with different initial parameters as the satisfactory results were achieved. We have also applied these approaches for inverse modeling the gravity anomaly due to a Chromite deposit mass, situated east of Sabzevar, Iran. The interpretation of the real gravity data using both methods yielded almost the same results.
DergiAraştırma MakalesiErişime Açık
  • Abdelrahman, E.M. 1990. Discussion on “A least-squares approach to depth determination from gravity data” by O. P. Gupta. Geophysics 55, 376-378.
  • Abdelrahman, E.M., El-Araby, T.M. 1993a. A least-squares minimization approach to depth determination from moving average residual gravity anomalies. Geophysics 58,1779–1784.
  • Abdelrahman, E.M., El-Araby, H.M. 1993b. Shape and depth solutions from gravity using correlation factors between successive least-squares residuals. Geophysics 59, 1785–1791.
  • Abdelrahman, E.M., Essa, K.S. 2015. A new method for depth and shape determinations from magnetic data. Pure and Applied Geophysics 172, 439–460.
  • Abdelrahman, E.M., Bayoumi, A.I., Abdelhady, Y.E., Gobashy, M.M., El-Araby, H.M. 1989. Gravity interpretation using correlation factors between successive least-squares residual anomalies. Geophysics 54, 1614-1621.
  • Abdelrahman, E.M., Bayoumi, A.I., El-Araby, H.M. 1991. A least-squares minimization approach to invert gravity data. Geophysics 56, 115-l 18.
  • Abdelrahman, E.M., El-Araby, T.M., Essa, K.S. 2003. A least-squares minimisation approach to depth, index parameter, and amplitude coefficient determination from magnetic anomalies due to thin dykes. Exploration Geophysics 34, 241–248.
  • Abdelrahman, E.M., Essa, K.S., El-Araby, T.M., Abo-Ezz, E.R. 2015. Depth and shape solutions from second moving average residual magnetic anomalies. Exploration Geophysics, 47/1, 58-66.
  • Abedi, M., Afshar, A., Ardestani, V.E., Norouzi, G.H., Lucas, C. 2009. Application of various methods for 2D inverse modeling of residual gravity anomalies. Acta Geophysica 58/2, 331-336.
  • Albora, A.M., Uçan, O.N., Özmen, A. 2001a. Residual separation of magnetic fields using a cellular neural network approach. Pure Appl Geophys,158,1797–1818.
  • Albora, A.M., Uçan, O.N., Özmen, A., Özkan, T. 2001b. Evaluation of Sivas-Divriği region Akdağ iron ore deposits using cellular neural network. J Appl Geophys 46,129–142.
  • Al-Garni, M.A. 2013. Inversion of residual gravity anomalies using neural network. Arab J Geosci 6, 1509–1516.
  • Asfahani, J., Tlas, M. 2008. An automatic method of direct interpretation of residual gravity anomaly profiles due to spheres and cylinders. Pure and Applied Geophysics 165/5, 981–994.
  • Bhattacharyya, B.K. 1964. Magnetic anomalies due to prism-shaped bodies with arbitrary polarization. Geophysics 29, 517–531.
  • Bichsel, M. 2005. Image processing with optimum neural networks. IEEE International Conference on Artificial Neural Networks, 513, IEEE, London, 374–377.
  • Bowin C., Scheer E., Smith W. 1986. Depth estimates from ratios of gravity, geoid, and gravity gradient anomalies. Geophysics 51, 123-136.
  • Chakravarthi, V., Sundararajan, N. 2004. Ridge regression algorithm for gravity inversion of fault structures with variable density. Geophysics 69, 1394–1404.
  • Chakravarthi, V., Sundararajan, N. 2005. Gravity modeling of 21/2-D sedimentary basins-a case of variable density contrast. Computers and Geosciences 31, 820–827.
  • Chakravarthi, V., Sundararajan, N. 2006. Gravity anomalies of multiple prismatic structures with depthdependent density – A Marquardt inversion. Pure and Applied Geophysics 163, 229–242.
  • Chakravarthi, V., Sundararajan, N. 2007. Marquardt optimization of gravity anomalies of anticlinal and synclinal structures with prescribed depthdependent density. Geophysical Prospecting 55, 571–587.
  • Chakravarthi, V., 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.
  • Chua, L.O., Yang, L. 1988. Cellular neural networks. Theory. IEEE Trans Circuits Syst 35,1257–1272.
  • Eshaghzadeh, A., Kalantari, R.A. 2015. Anticlinal Structure Modeling with Feed Forward Neural Networks for Residual Gravity Anomaly Profile, 8th congress of the Balkan Geophysical Society DOI: 10.3997/2214-4609.201414210.
  • Eshaghzadeh, A., Hajian, A. 2018. 2-D 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., Ushijima, K. 2001. Detection of cavities and tunnels from gravity data using a neural network. Explor. Geophys 32, 204–208.
  • Essa, K.S. 2007. A simple formula for shape and depth determination from residual gravity anomalies. Acta Geophysica 55/2, 182–190.
  • Gerkens, A.J.C. 1989. Foundation of exploration Geophysics Elsevier.
  • Gupta, O.P. 1983. A least-squares approach to depth determination from gravity data. Geophysics 48, 357-360.
  • Hajian, A.R. 2004. Depth estimation of gravity data by neural network, M. Sc. thesis, Tehran University, Iran (unpublished).
  • Hammer. S. 1974. Graticule spacing versus depth discrimination in gravity interpretation. Geophysics 42, 60-65.
  • Kaftan, I., Salk, M., Şenol, Y. 2011. Evaluation of gravity data by using artificial neural networks case study: Seferihisar geothermal area (Western Turkey). Journal of Applied Geophysics 75, 711-718.
  • Last, B. J., Kubik, K. 1983. Compact gravity inversion. Geophysics 48, 713-721.
  • Lines, L.R., Treitel, S. 1984. A review of least-squares inversion and its application to geophysical problems. Geophys. Prosp 32, 159-186.
  • Marquardt, D.W. 1963. An algorithm for least-squares estimation of nonlinear parameters. Journal of the Society of Indian Applied Mathematics 11, 431–441.
  • Mohan, N.L., Anandababu, L., Rao, S. 1986. Gravity interpretation using the Melin transform, Geophysics 51, 114-122.
  • Nagy, D. 1966. Gravitational attraction of a right rectangular prism. Geophysics 31, 362–371.
  • Odegard, M.E., Berg, J.W. 1965. Gravity interpretation using the Fourier integral. Geophysics 30, 424- 438.
  • Osman, O., Muhittin, A.A., Uçan, O.N. 2006. A new approach for residual gravity anomaly profile interpretations: Forced Neural Network (FNN). Ann. Geofis 49, 6.
  • Osman, O., Muhittin, A.A., Uçan, O.N. 2007. Forward modeling with Forced Neural Networks for gravity anomaly profile. Math. Geol 39, 593-605.
  • Plouff, D. 1976. Gravity and magnetic fields of polygonal prisms and application to magnetic terrain corrections. Geophysics 41, 727–741.
  • Salem, A., Ravat, D., Johnson, R., Ushijima, K. 2001. Detection of buried steel drums from magnetic anomaly data using a supervised neural network. J. Environ. Eng. Geophys 6,115-122.
  • Saxov, S., Nygaard, K. 1953. Residual anomalies and depth estimation. Geophysics 18, 913-928.
  • Sharma, B., Geldart, L.P. 1968. Analysis of gravity anomalies of two-dimensional faults using Fourier transforms. Geophys. Prosp 77-93.
  • Shaw, R.K., Agarwal, N.P. 1990. The application of Walsh transforms to interpret gravity anomalies due to some simple geometrically shaped causative sources: A feasibility study. Geophysics 55, 843- 850.
  • Talwani, M. 1965. Computation with the help of a digital computer of magnetic anomalies caused by bodies of arbitrary shape. Geophysics 30,797–817.
  • Talwani, M., Ewing, M. 1960. Rapid computation of gravitational attraction of 3D bodies of arbitrary shape. Geophysics 25, 203–225.

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