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 pro...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.展开更多
Incorporating structural-coupling constraint, known as the cross-gradients criterion, helps to improve the focussing trend in cross-plot of multiple physical properties. Based on this feature, a?post-processing techni...Incorporating structural-coupling constraint, known as the cross-gradients criterion, helps to improve the focussing trend in cross-plot of multiple physical properties. Based on this feature, a?post-processing technique is studied to characterize the lithological types of subsurface geological materials after joint inversion. A simple domain transform, which converts two kinds of participant physical properties into an artificial complex array, is adopted to extract anomalies manually from homogenous host rock. A synthetic example shows that structure-coupled joint inverted results tend to concentrate on the feature trends in the cross-plot, and the main geological targets are recovered well by a radius-azimuth plot. In a field data example, the lithological characterization?reveals that the main rock types interpreted in the study area agree with the geological information, thus demonstrating the feasibility of this technique.展开更多
文摘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.
文摘Incorporating structural-coupling constraint, known as the cross-gradients criterion, helps to improve the focussing trend in cross-plot of multiple physical properties. Based on this feature, a?post-processing technique is studied to characterize the lithological types of subsurface geological materials after joint inversion. A simple domain transform, which converts two kinds of participant physical properties into an artificial complex array, is adopted to extract anomalies manually from homogenous host rock. A synthetic example shows that structure-coupled joint inverted results tend to concentrate on the feature trends in the cross-plot, and the main geological targets are recovered well by a radius-azimuth plot. In a field data example, the lithological characterization?reveals that the main rock types interpreted in the study area agree with the geological information, thus demonstrating the feasibility of this technique.
