Abdelrahman, E. M., El-Araby, T. M. and Abo-Ezz, E. R., 2001, Three least-squares minimization approach to depth, shape, and amplitude coefficient determination from gravity data, Geophysics, 66, 1105-1109.
Arzi, A. A., 1975, Microgravity for engineering applications, Geophysical Prospecting, 23, 408-425.
, A. M., Özmen
, A. and Uçan
, O. N., 2001, Residual separation of magnetic fields using a cellular neural network approach, Pure and Applied Geophysics
), 1797-1818, doi: 10.1007/PL00001244.
Boulanger, O. and Chouteau, M., 2001, Constraints in 3D gravity inversion, Geophysical Prospecting 49, 265-280.
Butler, D. K., 1980, Microgravimetric techniques for geotechnical applications, Miscellaneous Paper GL-80-13, U.S. Army Engineer, Water-ways Experiment station, Vicksburg, Miss.
Bescoby, D. J., Cawley, G. C. and Chroston, P. N., 2004, Enhanced interpretation of magnetic survey data using artificial neural networks: a case study from Butrint, southern Albania, Archaeological Prospection, 11(4), 189-199.
Colley, G. C., 1963, The detection of caves by gravity measurements, Geophysical Prospecting, 11, 1-9.
Debeglia, N. and Dupont, F., 2002, Some critical factors for engineering and environmental microgravity investigations, Journal of Applied Geophysics, 50, 435-454.
Elawadi, E., Salem, A. and Ushijima, K., 2001, Detection of cavities and tunnels from gravity data using a neural network, Exploration Geophysics, 32, 75-79.
Fajklewicz, Z., 1986, Origin of the anomalies of gravity and its vertical gradient over cavities in brittle rock, Geophysical Prospecting, 4(8), 1233-1254.
Grêt, A., Klingelé, E. E. and Kahle, H. G., 2000, Application of artificial neural networks for gravity interpretation in two dimensions: a test study, Bollettino Geofisica Teorica ed Applicata, 41(1), 1-20.
Gupta, O. P., 1983, A least-squares approach to depth determination from gravity data, Geophysics, 48, 357-360
Hajian, A., Ardestani, V. E., Lucas, C. and Hajian, M., 2006a, Detection of Hazardous Downlifting Regions by neural network through microgravity data, 1st Conference on GIS Technology and Natural Hazard Management, Tehran, May, 8-10.
Hajian, A., Ardestani, V. E. and Lucas, C., 2006b, Depth Estimation of Subsurface Cavities via multi-layer perceptron neural network from microgravity data, 6th International conference: Problems of Geocosmos, Saint Petersburg, Russia, May, 23-28.
Hajian, A., 2008, Depth estimation of gravity anomalies by Hopfield network, Proceeding of 5th Annual Meeting, AOGS: Asia Oceania Geosciences Society, Busan, Korea, 16-20, Jun, 424-438.
Hajian, A., 2010a, Intelligent interpretation of gravity data via a fuzzy approach for detecting subsurface cavities, proceeding of 7th Annual Meeting, AOGS: Asia Oceania Geosciences Society, Hyderabad, International Convention Center, India, 5-9, July.
Hajian, A., 2010b, Detection of subsurface Qanats using gravity data via multi-layer perceptrons, Journal of Advances in Geosciences, Solid Earth, 20, 247-256.
Hajian, A., Styles, P. and Zomorrodian, H., 2011, Depth estimation of cavities from microgravity data through multi adaptive neuro fuzzy interference System, 17th European Meeting of Environmental and Engineering Geophysics, Leicester, UK, 12-14 September.
Hajian, A., Zomorrodian, H., Styles, P., Greco, F. and Lucas, C., 2012, Depth estimation of cavities from microgravity data using a new approach: the local linear model tree (LOLIMOT), Near Surface Geophysics, 10, 221-234, doi:10.3997/1873-0604.2011039.
Li, Y. and Oldenburg, D.W., 1998, 3-D inversion of gravity data, Geophysics, 63,109-119.
Loganathan, C. and Girijia, K.V., 2013, Hybrid learning for adaptive neuro fuzzy interference system, International Journal of Engineering and Science, 2(11), 6-13.
Mohan, N. L., Anandadabu, L. and Roa, S., 1986, Gravity interpretation using Mellin transform, Geophysics, 52, 114-122.
Neumann, R., 1967, Lav gravimetrie de haute précision, application aux recherches de cavités, Geophysical Prospecting, 15, 116-134.
Odegard, M. E. and Berg, J. W., 1965, Gravity interpretation using the Fourier integral, Geophysics, 30, 424-438.
Osman, O., Albora, A. M. and. Ucan, O. N., 2006, A new approach for residual gravity anomaly profile interpretations: Forced Neural Network (FNN), Annals of Geophysics, 9, 65-78.
Osman, O., Albora, A. M. and Ucan, O. N., 2007, Forward mmodeling with Forced Neural Networks for gravity anomaly profile, Mathematical Geology, 39, 593-605, doi: 10.1007/s11004-007-9114-8.
Reid, A. B., Allsop, J. M., Granser, H., Millet, A. J. and Somerton, I. W., 1990, Magnetic interpretation in three dimensions using Euler Deconvolution, Geophysics, 55, 80-91.
Sharma, B. and Geldrat, L. P., 1968, Analysis of gravity anomalies of two-dimensional faults using Fourier transforms, Geophysical Prospecting, 16, 77-93.
Shaw, R. K. and Agarwal, P., 1990, The application of Walsh transforms to interpret gravity anomalies due to some simple geometrical shaped causative sources: a feasibility study, Geophysics, 55,843-850.
Smith, R. A., 1959, Some depth formulate for local magnetic and gravity anomalies,
Geophysical Prospecting, 7, 55-63.
Styles, P., McGrath, R., Thomas, E. and Cassidy, N. J., 2005, The use of microgravity for cavity characterization in Karstic terrains, Quarterly Journal of Engineering and Hydrogeology, 38,155-169.
Styles, P., Miller, S., Thomas, E. and Toon, S. M., 1999, Microgravity survey freeport container terminal phase II Grand Bahama, Report No.98073, Microsearch UK.
Thompson, D. T., 1982, EULDPH-A new technique for making computer-assisted depth estimations from magnetic data, Geophysics, 47, 31-37.