2.5D regularized inversion for the interpretation of residual gravity data by a dipping thin sheet: numerical examples and case studies with an insight on sensitivity and non-uniqueness

Abstract

A new two-and-a-half dimensional (2.5D) regularized inversion scheme has been developed for the interpretation of residual gravity data by a dipping thin-sheet model. This scheme solves for the characteristic inverse parameters (depth to top z, dip angle θ , extension in depth L, strike length 2 Y, and amplitude coefficient A) of a model in the space of logarithms of these parameters (log(z), log(θ ), log(L), log(Y), and log(|A|)). The developed method has been successfully verified on synthetic examples without noise. The method is found stable and can estimate the inverse parameters of the buried target with acceptable accuracy when applied to data contaminated with various noise levels. However, some of the inverse parameters encountered some inaccuracy when the method was applied to synthetic data distorted by significant neighboring gravity effects/interferences. The validity of this method for practical applications has been successfully illustrated on two field examples with diverse geologic settings from mineral exploration. The estimated inverse parameters of the real data investigated are found to generally conform well with those yielded from drilling. The method is shown to be highly applicable for mineral prospecting and reconnaissance studies. It is capable of extracting the various characteristic inverse parameters that are of geologic and economic significance, and is of particular value in cases where the residual gravity data set is due to an isolated thinsheet type buried target. The sensitivity analysis carried out on the Jacobian matrices of the field examples investigated here has shown that the parameter that can be determined with the superior accuracy is θ (as confirmed from drilling information). The parameters z, L, Y, and A can be estimated with acceptable accuracy, especially the parameters z and A. This inverse problem is non-unique. The non-uniqueness analysis and the tabulated inverse results presented here have shown that the parameters most affected by the non-uniqueness are L and Y. It has also been shown that the new scheme developed here is advantageous in terms of computational efficiency, stability and convergence than the existing gravity data inversion schemes that solve for the characteristic inverse parameters of a sheet/dike.

Introductory Chapter: Mineral Exploration from the Point of View of Geophysicists

“…Gravity method has wide-ranging uses in mineral, hydrocarbon, cave, geothermal, and archeological investigations [5][6][7]. The target of gravity interpretation is to locate and characterize the buried mineral source parameters, in particular, the density contrast, depth, and shape [8][9][10]. Several methodologies are used to interpret gravity data [11][12][13][14].…”

Quantitative Interpretation of Gravity and Magnetic Anomalies in West of Tikrit City and Surroundings, Iraq

“…The selection of the best matching between the residual and calculated anomalies is highly subjective and likely to vary with interpreter, amount of geological and geophysical subsurface control and objective [1]. 2.5D modeling was employed to model the residual gravity and magnetic anomalies in many studies [2,[20][21][22][23][24]. In the current study, a 2.5D modeling softwares grav2dc and mag2dc [25,26] were used for forward modeling along the selected profiles.…”

Global nonlinear optimization for the interpretation of source parameters from total gradient of gravity and magnetic anomalies caused by thin dyke

“…Abdelrahman, E. M., Essa, K. S., (2015). A New Method for Depth and Shape Determinations from Magnetic Data.…”

“…2001], Fourier transform [Odegard and Berg 1965, Bhattacharyya 1965, Sharma and Geldart 1968, Euler deconvolution [Thompson 1982], Mellin transform [Mohan et al 1986], Hilbert transforms [Mohan et al 1982], least squares minimization approaches [Gupta 1983, Silva 1989, McGrath and Hood 1973, Lines and Treitel 1984, Abdelrahman 1990, Abdelrahman et al 1991, Abdelrahman and El-Araby 1993, Abdelrahman and Sharafeldin 1995a, Werner deconvolution [Hartmann et al 1971, Jain 1976, Kilty 1983; Walsh Transformation [Shaw and Agarwal 1990], Continual least-squares methods [Abdelrahman and Sharafeldin 1995b, Abdelrahman et al 2001a, b, Essa 2012, Euler deconvolution method [Salem and Ravat 2003], Fair function minimization procedure andAsfahani 2011a, Asfahani andTlas 2012], DEXP method [Fedi 2007], deconvolution technique [Tlas and Asfahani 2011b]; Regularised inversion [Mehanee 2014, Mehanee andEssa 2015]; Simplex algorithm [Tlas and Asfahani 2015], simulated annealing methods [Gokturkler and Balkaya 2012], Very fast simulated annealing Acharya 2016, Biswas andSharma 2016a, b;Biswas 2015, b, Sharma and Biswas 2013a, particle swarm optimization [Singh and Biswas 2016] and Differential Evolution ] have been used to solve similar kind of no...…”