This talk and expanded abstract are a branch of my Masters degree research. It presents an adaptation of the gravity-gradient inversion to gravity data.
I have added an open-source implementation of the method to the Python
library Fatiando a Terra. In version
the code is in module
This paper presents a novel gravity inversion method for estimating a 3D density-contrast distribution defined on a grid of prisms. Our method consists of an iterative algorithm that does not require the solution of a large equation system. Instead, the solution grows systematically around user-specified prismatic elements called "seeds". Each seed can have a different density contrast, allowing the interpretation of multiple bodies with different density contrasts and interfering gravitational effects. The compactness of the solution around the seeds is imposed by means of a regularizing function. The solution grows by the accretion of neighboring prisms of the current solution. The prisms for the accretion are chosen by systematically searching the set of current neighboring prisms. Therefore, this approach allows that the columns of the Jacobian matrix be calculated on demand. This is a known technique from computer science called "lazy evaluation", which greatly reduces the demand of computer memory and processing time. Test on synthetic data and on real data collected over the ultramafic Cana Brava complex, central Brazil, confirmed the ability of our method in detecting sharp and compact bodies.
Uieda, L., and V. C. F. Barbosa (2011), 3D gravity inversion by planting anomalous densities, SBGf 2011 Expanded Abstracts, pp. 1–5, doi:10.1190/sbgf2011-179