The main problems in three-dimensional gravity inversion are the non-uniqueness of the solutions and the high computational cost of large data sets. To minimize the high computational cost, we propose a new sorting me...The main problems in three-dimensional gravity inversion are the non-uniqueness of the solutions and the high computational cost of large data sets. To minimize the high computational cost, we propose a new sorting method to reduce fluctuations and the high frequency of the sensitivity matrix prior to applying the wavelet transform. Consequently, the sparsity and compression ratio of the sensitivity matrix are improved as well as the accuracy of the forward modeling. Furthermore, memory storage requirements are reduced and the forward modeling is accelerated compared with uncompressed forward modeling. The forward modeling results suggest that the compression ratio of the sensitivity matrix can be more than 300. Furthermore, multiscale inversion based on the wavelet transform is applied to gravity inversion. By decomposing the gravity inversion into subproblems of different scales, the non-uniqueness and stability of the gravity inversion are improved as multiscale data are considered. Finally, we applied conventional focusing inversion and multiscale inversion on simulated and measured data to demonstrate the effectiveness of the proposed gravity inversion method.展开更多
Joint inversion based on a correlation constraint utilizes a linear correlation function as a structural constraint.The linear correlation function contains a denominator,which may result in a singularity as the objec...Joint inversion based on a correlation constraint utilizes a linear correlation function as a structural constraint.The linear correlation function contains a denominator,which may result in a singularity as the objective function is optimized,leading to an unstable inversion calculation.To improve the robustness of this calculation,this paper proposes a new method in which a sinusoidal correlation function is employed as the structural constraint for joint inversion instead of the conventional linear correlation function.This structural constraint does not contain a denominator,thereby preventing a singularity.Compared with the joint inversion method based on a cross-gradient constraint,the joint inversion method based on a sinusoidal correlation constraint exhibits good performance.An application to actual data demonstrates that this method can process real data.展开更多
基金This work was supported by the Key National Research Project of China （Nos. 2017YFC0601900 and 2016YFC0303100） and the Key Program of National Natural Science Foundation of China （Nos. 41530320 and 41774125）.
文摘The main problems in three-dimensional gravity inversion are the non-uniqueness of the solutions and the high computational cost of large data sets. To minimize the high computational cost, we propose a new sorting method to reduce fluctuations and the high frequency of the sensitivity matrix prior to applying the wavelet transform. Consequently, the sparsity and compression ratio of the sensitivity matrix are improved as well as the accuracy of the forward modeling. Furthermore, memory storage requirements are reduced and the forward modeling is accelerated compared with uncompressed forward modeling. The forward modeling results suggest that the compression ratio of the sensitivity matrix can be more than 300. Furthermore, multiscale inversion based on the wavelet transform is applied to gravity inversion. By decomposing the gravity inversion into subproblems of different scales, the non-uniqueness and stability of the gravity inversion are improved as multiscale data are considered. Finally, we applied conventional focusing inversion and multiscale inversion on simulated and measured data to demonstrate the effectiveness of the proposed gravity inversion method.
基金supported by the National Key Research and Development Project of China(No:2017YFC0602201)
文摘Joint inversion based on a correlation constraint utilizes a linear correlation function as a structural constraint.The linear correlation function contains a denominator,which may result in a singularity as the objective function is optimized,leading to an unstable inversion calculation.To improve the robustness of this calculation,this paper proposes a new method in which a sinusoidal correlation function is employed as the structural constraint for joint inversion instead of the conventional linear correlation function.This structural constraint does not contain a denominator,thereby preventing a singularity.Compared with the joint inversion method based on a cross-gradient constraint,the joint inversion method based on a sinusoidal correlation constraint exhibits good performance.An application to actual data demonstrates that this method can process real data.
