This talk is about an improvement to the method described in "Robust 3D gravity gradient inversion by planting anomalous densities". See the expanded abstract for details.
I have added an open-source implementation of the method to the Python
library Fatiando a Terra. In version
the code is in module
We present an improvement to the method of 3D gravity gradient inversion by planting anomalous densities. This method estimates a density-contrast distribution defined on a grid of right-rectangular prisms. Instead of solving large equation systems, the method uses a systematic search algorithm to grow the solution, one prism at a time, around user-specified prisms called "seeds". These seeds have known density contrasts and the solution is constrained to be concentrated around the seeds as well as have their density contrasts. Thus, prior geologic and geophysical information are incorporated into the inverse problem through the seeds. However, this leads to a strong dependence of the solution on the correct location, density contrast, and number of seeds used. Our improvement to this method consists of using the "shape-of-anomaly" data-misfit function in conjunction with the l2-norm data-misfit function. The shape-of-anomaly function measures the different in shape between the observed and predicted data and is insensitive to differences in amplitude. Tests on synthetic and real data show that the improved method not only has an increased robustness with respect to the number of seeds and their locations, but also provides a better fit of the observed data.
Uieda, L., and V. C. F. Barbosa (2012), Use of the "shape-of-anomaly" data misfit in 3D inversion by planting anomalous densities, SEG Technical Program Expanded Abstracts, pp. 1-6, doi:10.1190/segam2012-0383.1