摘要
The structure-coupled joint inversion method of gravity and magnetic data is a powerful tool for?developing improved physical property models with high resolution and compatible features;?however, the conventional procedure is inefficient due to the truncated singular values decomposition?(SVD) process at each iteration. To improve the algorithm, a technique using damped leastsquares?is adopted to calculate the structural term of model updates, instead of the truncated SVD. This?produces structural coupled density and magnetization images with high efficiency. A so-called?coupling factor is introduced to regulate the tuning of the desired final structural similarity level.?Synthetic examples show that the joint inversion results are internally consistent and achieve?higher?resolution than separated. The acceptable runtime performance of the damped least squares?technique used in joint inversion indicates that it is more suitable for practical use than the truncated SVD method.
The structure-coupled joint inversion method of gravity and magnetic data is a powerful tool for?developing improved physical property models with high resolution and compatible features;?however, the conventional procedure is inefficient due to the truncated singular values decomposition?(SVD) process at each iteration. To improve the algorithm, a technique using damped leastsquares?is adopted to calculate the structural term of model updates, instead of the truncated SVD. This?produces structural coupled density and magnetization images with high efficiency. A so-called?coupling factor is introduced to regulate the tuning of the desired final structural similarity level.?Synthetic examples show that the joint inversion results are internally consistent and achieve?higher?resolution than separated. The acceptable runtime performance of the damped least squares?technique used in joint inversion indicates that it is more suitable for practical use than the truncated SVD method.
