Improving the inversion of helicopter-borne frequency-domain electromagnetic data with depth constrained

Author

MA.Student_Shahrood University

Abstract

Generally, the measured secondary field data is inverted into resistivity using two principal models; the homogeneous half-space model and the layered half-space model. While the homogeneous half-space inversion uses single frequency data, the inversion is done individually for each of the frequencies used, the multi-layer 1D inversion is able to take the data of all frequencies available into account. The resulting parameter of the half-space inversion is the apparent resistivity which is the inverse of the apparent conductivity. It's possible that using the fast method to calculate the apparent resistivity, if the distance between the HEM sensor and the top of the half-space is known. Unfortunately, the dependency of the secondary field on the half-space resistivity is highly non-linear. Thus, the inversion is not straightforward and the apparent resistivities have to be derived by the use of look-up tables, curve fitting or iterative inversion procedures (Fraser, 1978; Siemon, 1997; Siemon, 2001).The usual technique for inversion of airborne electromagnetic data frequency domain (HEM) data is a 1D single site inversion, because of the 2D and 3D inversion of HEM data wants very powerful computer hardware. Some inversion method for electromagnetic data inversion suggested. Usually this method updated for ground electromagnetic methods. One of the methods employed in the inversion of airborne electromagnetic data frequency domain (HEM), Levenberg-Marquardt method inversion (MLI) is looking for smoothing fitted to the data in the inversion algorithm; this inversion method based on least squares criteria, seeking a modelby minimizing the residuals of an objective function. Marquardt’s inversion only pursuits the largest fitting of simulation data to original measurements, and has the characteristics of simple algorithm and fast calculation. In this procedure usually HEM data smoothed and then used in the inversion procedure, but any variation in data change results. For stability of inversion procedure, it is suggested that stitched-together 1-D models along the profile that each sounding inverted by constrained neighbor sounding and each layer of each sounding inverted by depth constrained neighbor layers. In addition used smoothing constrained in inversion procedure instead of smoothing a data like Marquardt–Levenberg inversion.
In this paper, Starting model determined for apparent resistivity with Mundry technique and for centroied depth with Weidelt technique. To using this method, the auto inversion cod written in MATLAB software environment that inputs are real and imaginary part of data with sensor altitude and output is inverted model with misfit. In the following this algorithm tested on standard synthetic data, the model chosen for the generation of synthetic data represents a layered earth structure having an inhomogeneous top layer in order to study the influence of shallow resistivity variations on the appearance of deep horizontal conductors in one-dimensional inversion results. The inversion of synthetic data results shown this technique for inversion HEM data improved the results and is much more accurate than Marquardt–Levenberg inversion. Finally the inversion algorithm used to invert a set of real DIGHEM field data from Mirgah Naqshineh area in Saqqez of Kurdistanand interpretation of results according to geology information of area.

Keywords

Main Subjects

References

Arabamiri, A. R., Moradzadeh, A., Fathianpour, N. and Siemon, B., 2010, Inverse modeling of HEM data using a new inversion algorithm, Mining and Environment. J., 1, 9-20.
Aster, R. C., Borchers, B. and Thurber C. H., 2013, Parameter estimation and inverse problems, Academic Press is an imprint of Elsevier.
Auken, E. and Christiansen, A. V., 2004, Layered and laterally constrained 2D inversion of resistivity data, Geophys. J. Int., 69, 752-761.
Auken, E., Christiansen, A. V., Jacobsen, B. H. and Foged, N., 2005, Piecewise 1D laterally constrained inversion of resistivity data, Geophys. Prospect, 53, 497-506.
Baker, K., 2005, Singular value decomposition tutorial, Ohio State University.
Beard, L. P. and Nyquist, J. E., 1998, Simultaneous inversion of airborne electromagnetic for resistivity and magnetic permeability, Geophys. J. Int., 63, 1556-1564.
Davis, A., 2007, Quantitative characterisation of airborne electromagnetic systems, PhD Thesis, RMIT University.
Farquharson, C. G. and Oldenburg, D. W., 1998, Non-linear inversion using general measures of data misfit and model structure, J Appl. Geophys., 134, 213-227.
Fitterman, D. V. and Deszcz-Pan, M., 1998, Helicopter EM mapping of saltwater intrusion in Everglades National Park, Florida, Exploration Geophysics, 29, 240-243.
Fluche, B. and Sengpiel, K. P., 1997, Grundlagen und Anwendungen der Hubschrauber-Geophysik In, Beblo, M. (Ed.), Umweltgeophysik, Ernst und Sohn, Berlin, 363-393.
Fraser, D. C., 1978, Resistivity mapping with an airborne multi coil electromagnetic system, Geophys. J. Int., 43, 144-172.
Guptasarma, D. and Singh, B., 1997, New digital linear filters for Hankel J0 and J1 transforms, Geophys. Prospect, 45(5), 745-762.
Huang, H. and Fraser, D. C., 1996, The differential parameter method for multi frequency airborne resistivity mapping, Geophys. J. Int., 61(1), 100-109.
Huang, H. and Fraser, D. C., 2003, Inversion of helicopter electromagnetic data to a magnetic conductive layered earth, Geophys. J. Int., 68(4), 1211-1223.
Jackson, D. D., 1979, The use of a priori data to resolve non-uniqueness in linear inversion, Geophysical Journal of the Royal Astronomical Society., 57, 137-157.
Kirsch, R., Sengpiel, K. P. and Voss, W., 2003, The use of electrical conductivity mapping in the definition of an aquifer vulnerability index, Near Surface Geophysics, 1, 13-19.
Marquart, D., 1963, An algorithm for least squares estimation of nonlinear parameters, SIAM, J Appl. Mathematics, 11 ,441-443.
Menke, W., 1989, Geophysical data analysis discrete inverse theory, Academic Press, Inc.
Mundry, E., 1984, On the interpretation of airborne electromagnetic data for the two-layer case, Geophys. Prospect, 32, 336-346.
Nabighian, M. N., 1996, Electromagnetic methods in applied geophysics, Application/Parts A and B, SEG Books.
Palacky, G. J. and West, G. F., 1991, Airborne electromagnetic methods. In M. N. Nabighian, ed, electromagnetic methods in applied geophysics, SEG, pp. 811-880.
Sengpiel, K. P., 1988, Approximate inversion of airborne EM data from a multi-layered ground, Geophys. Prospect, 36, 446-459.
Sengpiel, K. P. and Siemon, B., 1998, Examples of 1D inversion of multi frequency HEM data from 3D resistivity distributions, Exploration Geophysics., 29(2), 133-141.
Sengpiel, K. P. and Siemon, B., 2000, Advanced inversion methods for airborne electromagnetic exploration, Geophys. J. Int., 65, 1983-1992.
Siemon, B., 2001, Improved and new resistivity

depth profiles for helicopter electromagnetic data, J. Appl. Geophys., 46, 65-76.
Siemon, B., Stuntebeck, C., Sengpiel, K. P., Röttger, B., Rehli, H. J. and Eberle, D. G., 2002, Investigation of hazardous waste sites and their environment using the BGR helicopter-borne geophysical system, Journal of Environmental & Engineering Geophysics, 7, 169-181
Siemon, B., Eberle, D. G. and Binot, F., 2004, Helicopter-borne electromagnetic investigation of coastal aquifers in North-West Germany, Z. f. Geophys., 32, 385-395.
Siemon, B., Auken, E. and Christiansen, A. V., 2009, Laterally constrained inversion of helicopter borne frequency-domain electromagnetic data, J. Appl .Geophys., 67, 259-268.
Tarantola, A. and Valette, B., 1982, Generalized non-linear inverse problems solved using the least squares criterion, Reviews of Geophysics and Space Physics., 20(2), 219-232.
Wait, J. R., 1982, Geo-electromagnetism, Academic Press, New York.
Weidelt, P., 1972, The inverse problem of geomagnetic induction, Z. f. Geophys., 38, 257-289.

Files

Share

How to cite

Statistics