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.
Maysam Abedi; Kiomars Mosazadeh; Hamid Dehghani; Ahmad MadanchiZare
Abstract
This paper describes an efficient edge-preserved regularization algorithm for downward continuation of magnetic data in detection of unexploded ordnance (UXO). The magnetic anomalies arising from multi-source UXO can overlap at a height over the ground surface, while causative sources may not be readily ...
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This paper describes an efficient edge-preserved regularization algorithm for downward continuation of magnetic data in detection of unexploded ordnance (UXO). The magnetic anomalies arising from multi-source UXO can overlap at a height over the ground surface, while causative sources may not be readily separated due to low level of signal-to-noise ratio of the observed data. To effectively the magnetic method work in the cleanup stage of contaminated area with UXO, the magnetic anomalies of UXO sources should be enhanced in order to separate the locations of different sources. The stable downward continuation of magnetic data can increase the signal-to-noise ratio which subsequently causes the separation of UXO sources by enhancing the signals. We formulate the downward continuation as a linear ill-posed deconvolution problem in this study. To obtain a reasonable downward continued field, it is stabilized in a Fourier domain to regularize the downward solution using the edge-preserved (or total-variation) algorithm. The L-curve method is used to choose the optimum value of the regularization parameter which is a trade-off between the misfit and the solution norms in the cost function of optimization problem. A synthetic magnetic field is constructed from isolated multi-source UXO anomalies, whose results show that the data can be stably downward continued by the proposed method. Likewise, a field data set has been provided to demonstrate the capability of the applied method in UXO detection.
