• الصفحة الرئيسية
  • Inverse modeling of gravity field data due to finite vertical cylinder using modular neural network and least-squares standard deviation method

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عنوان URL للمقالة


رقم المقالة : IJES-1801-1175 (R5) زيارة : 125 الصفحة: 267 - 276

10.30495/ijes.2019.669401

نوع المخطوط: ابحاث

Inverse modeling of gravity field data due to finite vertical cylinder using modular neural network and least-squares standard deviation method

الموضوعات :

Ata Eshaghzadeh 1 , Sanaz Seyedi Sahebari 2 , Roghayeh Alsadat Kalantari 3

1 - Postgraduate of geophysics, Zima company, Chaloos, Iran
2 - Roshdiyeh Higher Education Institute, Tabriz, Iran
3 - Postgraduate of geophysics, Zima company, Chaloos, Iran

تاريخ الإرسال : 18 الأحد , جمادى الأولى, 1439 تاريخ التأكيد : 12 الجمعة , رمضان, 1440 تاريخ الإصدار : 02 الثلاثاء , صفر, 1441

الکلمات المفتاحية: Least-Squares Standard Deviation, Finite Vertical Cylinder, Modular Neural Network (MNN), Salt Dome,

ملخص المقالة :

In this paper, modular neural network (MNN) inversion has been applied for the parameters approximation of the gravity anomaly causative target. The trained neural network is used for estimating the amplitude coefficient and depths to the top and bottom of a finite vertical cylinder source. The results of the applied neural network method are compared with the results of the least-squares standard deviation method. The inverse modeling has been tested first on synthetic gravity data. The synthetic data are infected with random noise to evaluate the effect of noise on performance of the methods. Both methods show satisfactory results, with and without random noise. The MNN and least squares standard deviation approaches have been applied to two real gravity data due to two salt domes from Iran and USA, where the results comparison shows good agreement with each other. The computed standard errors indicate the generated gravity response of the estimated parameters from MNN has better conformity with the observed gravity anomaly than the generated gravity response from the least squares method. The results of the MNN inversion show the top and bottom depths of the salt dome situated in Iran are about 24.5 m and 63.8 m and for the salt dome situated in USA are about 1451 m and 9263 m, respectively.

المصادر:


  1. Abdelrahman EM, El-Araby TM (1996) Shape and depth solutions from moving average residual gravity anomalies. Applied Geophysics 36:89-95.
    2.
  2. Abdelrahman EM, El-Araby TM, El-Araby HM, Abo-Ezz ER (2001) A new method for shape and depth determinations from gravity data. Geophysics 66:1774-1780.
    3.
  3. Abdelrahman EM, Abo-Ezz ER, Essa KS, El-Araby TM, Soliman KS (2006) A Least-squares variance analysis method for shape and depth estimation from gravity data. Journal of Geophysics and Engineering 3:143-153.
    4.
  4. Abdelrahman EM, Abo-Ezz ER (2008) A Least-Squares Standard Deviation Method to Interpret Gravity Data due to Finite Vertical Cylinders and Sheets. Pure and Applied Geophysics 165:947–965.
    5.
  5. Abedi M, Afshar A, Ardestani VE, Norouzi GH, Lucas C (2009) Application of Various Methods for 2D Inverse Modeling of Residual Gravity Anomalies. Acta Geophysica 58(2):317-336.
    6.
  6. Albora AM, Ucan ON, Özmen A, Ozkan T (2001a) Separation of Bouquer anomaly map using cellular neural network. Journal of Applied Geophysics 46:129-142.
    7.
  7. Albora AM, Özmen A, Ucan ON (2001b) Residual separation of magnetic fields using a cellular neural network approach. Pure and Applied Geophysics 158:9-10.
    8.
  8. Al-Garni MA (2013) Inversion of residual gravity anomalies using neural network. Arabian Journal of Geosciences 6:1509-1516.
    9.
  9. 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.
    10.
  10. Bichsel M (2005) Image processing with optimum neural networks. IEEE International Conference on Artificial Neural Networks, 513, IEEE, London, 374-377.
    11.
  11. Biswas A (2015) Interpretation of residual gravity anomaly caused by simple shaped bodies using very fast simulated annealing global optimization, Geoscience Frontiers 6:875-893
    12.
  12. BotezatuIM, Visarion F, Scutu G, Cucu G (1971) Approximation of the gravitational attraction of geological bodies. Geophysical Prospecting 19(2):218-227.
    13.
  13. Cordell L, Henderson RG (1968) Iterative three-dimensional solution of gravity anomaly data using a digital computer. Geophysics 33:596-601.
    14.
  14. Chua LO, Yang L (1988) Cellular neural networks: Theory. IEEE Trans. Circuits Systems 35(10):1257-1272.
    15.
  15. Eshaghzadeh A, Kalantari RA (2015) Anticlinal Structure Modeling with Feed Forward Neural Networks for Residual Gravity Anomaly Profile. 8th congress of the Balkan Geophysical Society, European Association of Geoscientists and Engineers.
    16.
  16. Eshaghzadeh A, Hajian AR (2018) 2D inverse modeling of residual gravity anomalies from Simple geometric shapes using Modular Feed-forward Neural Network, Annals of Geophysics 61(1):1-9.
    17.
  17. Eslam E, Salem A, Ushijima K (2001) Detection of cavities and tunnels from gravity data using a neural network. Explor. Geophysics 32:204-208.
    18.
  18. Essa KS (2007) Gravity data interpretation using the S-curves method. Journal of Geophysics and Engineering 4:204–213.
    19.
  19. Hajian AR (2004) Depth estimation of gravity data by neural network. M. Sc. thesis, Tehran University, Iran (In Persian).
    20.
  20. Hammer S (1974) Approximation in gravity interpretation calculation. Geophysics 39: 205–222.
    21.
  21. Haykin S (1994) Neural networks: a comprehensive foundation. Macmillan, New York.
    22.
  22. Masket AVH, Macklin R, Schmitt HWG (1956) Tables of Solid Angles and Activations: 1. Solid Angle Subtended by a Circular disc; 2. Solid Angle Subtended by a Cylinder; 3. Activation of a Cylinder by a Point Source, ORNL-2170, Unclassif. Tech. Inf. Serv. Extens., Oak Ridge, Tenn.
    23.
  23. Mehanee SA (2014) Accurate and efficient regularised inversion approach for the interpretation of isolated gravity anomalies. Pure and Applied Geophysics 171: 1897-1937.
    24.
  24. Nettleton LL (1976) Gravity and Magnetics in Oil Prospecting. McGraw-Hill Book Co.
    25.
  25. Osman O, Muhittin AA, Ucan ON (2006) A new approach for residual gravity anomaly profile interpretations: Forced Neural Network (FNN), Annals of Geophysics 49(6).
    26.
  26. Osman O, Muhittin AA, Ucan ON (2007) Forward modeling with Forced Neural Networks for gravity anomaly profile, Mathematical Geology 39:593-605.
    27.
  27. Pick M, Picha J, Vyskosil V (1973) Theory of the Earth’s Gravity Field. (Academic publishing house of the Czechoslovakia), Academy of Sciences, Prague.
    28.
  28. Roy L, Agarwal BNP, Shaw RK (2000) A new concept in Euler deconvolution of isolated gravity anomalies, Geophysical Prospecting 48:559-575.
    29.
  29. Salem A, Ravat D, Johnson R, Ushijima K (2001) Detection of buried steel drums from magnetic anomaly data using a supervised neural network, Journal of Environmental and Engineering Geophysics 6:115-122.
    30.
  30. Skeels DC (1963) An approximate solution of the problem of maximum depth in gravity interpretation, Geophysics 28:724-735.
    31.
  31. Talwani M, Ewing M (1960) Rapid computation of gravitational attraction of three-dimensional bodies of arbitrary shape, Geophysics 25:203-225.
    32.
  32. Tanner JG (1967) An automated method of gravity interpretation, Geophysical Journal International 13:339-347.
    33.
  33. Zhang L, Poulton MM, Wang T (2002) Borehole electrical resistivity modeling using neural networks, Geophysics 67:1790-1797.
    34.

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