A. Nejati Kalate; V. Ebrahimzadeh Ardestani; E. Shahin; S. H. Motavalli Anbaran; Sh. Ghomi; E. Javan
Abstract
Determination of the geometry of bedrock, by nonlinear inverse modeling of gravity data, is the aim of this paper. In this method, reliable geological structures can be obtained by minimum geology priori information. The usual practice of inverting gravity anomalies of two-dimensional bodies replaced ...
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Determination of the geometry of bedrock, by nonlinear inverse modeling of gravity data, is the aim of this paper. In this method, reliable geological structures can be obtained by minimum geology priori information. The usual practice of inverting gravity anomalies of two-dimensional bodies replaced by n-sides polygon for determining location of the vertical that best explain the observed anomalies. In this method, the geometry of the bedrock is replaced by a series of juxtaposing prisms. Finally the length of each prism is the depth of the bedrock at that point.
The algorithm uses a nonlinear iterative procedure for simulation of bedrock geometry. At the first step, the nonlinear problem changes to a linear problem by a proper approximation and standard method. The second step is the parameterization of the model. Finally, an initial model is suggested on the basis of geological and geophysical assumption and using the numerical analysis, Jacobean matrix is calculated. In each iteration the inversion will improve the initial model, considering the differences between observed and calculated gravity anomalies, based on Levenberg-Marquardt's method.
The practical effectiveness of this method is demonstrated by inversion of synthetic (free noise and noise contaminated data) and real examples. The real data is acquired over the Moghan area and the results compared with the geological information.
A. Nejati Kalateh; M. Mirzaei; N. Gouya; E. Shahin
Abstract
In this paper we used orthogonal basis functions and expansion coefficients for inverse modeling of magnetic data. The basis functions chosen are normalized eigenvectors of second derivation of the objective function (Hessian matrix) calculate for an initial model. Limited number of basis vectors obtained ...
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In this paper we used orthogonal basis functions and expansion coefficients for inverse modeling of magnetic data. The basis functions chosen are normalized eigenvectors of second derivation of the objective function (Hessian matrix) calculate for an initial model. Limited number of basis vectors obtained in this way defines a new subspace in model parameters space. A new objective function is defined in term of these new parameters and minimized in subspace of original space. As in geophysical inverse problems we need to inverse matrixes that are functions data and geometry of data and model parameters. The matrix inversion in new subspace of the original space will be better conditions due to less dimensionality in the inversion. Since the most significant eigenvectors corresponding the largest eigen values in Singular Value Decomposition ( SVD) of matrixes. Others eigenvectors have less influence in fitting data or lead inversion procedures to local minima. With apply subspace method inversion will be fast and stable against the noise. The efficiency of the method is tested with synthetic and real magnetic data (acquired from Moghan area, north-west of Iran). The results proved fast convergence and stability of inversion against the noise.
