Joint inversion of magnetic and gravity data using group lasso regularization
摘要
Magnetic and gravity inversion has long attracted attention and research. A recent topic in such inversion studies is the magnetic and gravity joint inversion with the constraint that the derived magnetization and density models are correlated. The purpose of this approach is to reduce the non-uniqueness of the individual models, which is an inherent problem of potential-field data inversion, by using the constraints of multiple data. Another point of interest is the introduction of sparse regularization in the magnetic and gravity inversion. If the conventional smoothness-promoting inversion is used, the derived model is likely to be blurred. The aim of introducing sparse regularization is to reduce the blurred feature and improve the resolution of the derived model. In this paper, we proposed and developed a new magnetic and gravity joint inversion method by introducing the group lasso regularization. The group lasso is a kind of sparseness-promoting regularization method, an extension of the