M. Moghadasi; A. Nejati Kalateh; M. Rezaie
Abstract
Gravity data inversion is one of the important steps in the interpretation of practical gravity data. The inversion result can be obtained by minimization of the Tikhonov objective function. The determination of an optimal regularization parameter is highly important in the gravity data inversion. In ...
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Gravity data inversion is one of the important steps in the interpretation of practical gravity data. The inversion result can be obtained by minimization of the Tikhonov objective function. The determination of an optimal regularization parameter is highly important in the gravity data inversion. In this work, an attempt was made to use the active constrain balancing (ACB) method to select the best regularization parameter for a 3D inversion of the gravity data using the Lanczos bidiagonalization (LSQR) algorithm. In order to achieve this goal, an algorithm was developed to estimate this parameter. The validity of the proposed algorithm was evaluated by the gravity data acquired from a synthetic model. The results of the synthetic data confirmed the correct performance of the proposed algorithm. The results of the 3D gravity data inversion from this chromite deposit from Cuba showed that the LSQR algorithm could provide an adequate estimate of the density and geometry of sub-surface structures of mineral deposits. A comparison of the inversion results with the geologic information clearly indicated that the proposed algorithm could be used for the 3D gravity data inversion to estimate precisely the density and geometry of ore bodies. All the programs used in this work were provided in the MATLAB software environment.
M. Rezaie; A. Moradzadeh; A. Nejati Kalate
Abstract
One of the most remarkable basis of the gravity data inversion is the recognition of sharp boundaries between an ore body and its host rocks during the interpretation step. Therefore, in this work, it is attempted to develop an inversion approach to determine a 3D density distribution that produces a ...
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One of the most remarkable basis of the gravity data inversion is the recognition of sharp boundaries between an ore body and its host rocks during the interpretation step. Therefore, in this work, it is attempted to develop an inversion approach to determine a 3D density distribution that produces a given gravity anomaly. The subsurface model consists of a 3D rectangular prisms of known sizes and positions and unknown density contrasts that are required to be estimated. The proposed inversion scheme incorporates the Cauchy norm as a model norm that imposes sparseness and the depth weighting of the solution. A physical-bound constraint is enforced using a generic transformation of the model parameters. The inverse problem is posed in the data space, leading to a smaller dimensional linear system of equations to be solvedand a reduction in the computation time. For more efficiency, the low-dimensional linear system of equations is solved using a fast iterative method such as Lanczos Bidiagonalization. The tests carried out on the synthetic data show that the sparse data-space inversion produces blocky and focused solutions. The results obtained for the 3D inversion of the field gravity data (Mobrun gravity data) indicate that the sparse data-space inversion could produce the density models consistent with the true structures.
Ali Nejati Kalateh; Amin Roshandel kahoo
Abstract
We inverse the surface gravity data to recover subsurface 3D density distribution with two strategy. In the first strategy, we assumed wide density model bound for inverting gravity data and In the second strategy, the inversion procedure have been carried out by limited bound density. Wediscretize the ...
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We inverse the surface gravity data to recover subsurface 3D density distribution with two strategy. In the first strategy, we assumed wide density model bound for inverting gravity data and In the second strategy, the inversion procedure have been carried out by limited bound density. Wediscretize the earth model into rectangular cells of constant andunidentified density. The number of cells is often greater than the number of observation points thus we have an underdetermined inverse problem. The densities are estimated by minimizing a cost function subject to fitting the observed data. The synthetic results show that the recovered model from the first strategy is characterized by broad density distribution around the true model, butthat of the second strategy is closer to true models.We carry out inversion of gravity data taken over chromite deposit located at Hormozgan providence of Iran for estimating of subsurface density distribution. The recovered model obtained from second strategy has appropriate agreement with previous study.
