A self-constrained inversion of magnetic data based on correlation method

• Correlation method is used to extract self-constraints for 3D inversion.
• Two specific strategies are proposed to obtain spatial weighting functions.
• The sources with clear boundaries and concentrated physical property can be obtained.

Geologically-constrained inversion is a powerful method for producing geologically reasonable solutions in geophysical exploration problems. But in many cases, except the observed geophysical data to be inverted, the geological information is insufficiently available for improving reliability of recovered models. To deal with these situations, self-constraints extracted from preprocessing observed data have been applied to constrain the inversion. In this paper, we present a self-constrained inversion method based on correlation method. In our approach the correlation results are first obtained by calculating the cross-correlation between theoretical data and horizontal gradients of the observed data. Subsequently, we propose two specific strategies to extract the spatial variation from the correlation results and then translate them into spatial weighting functions. Incorporating the spatial weighting functions into the model objective function, we obtain self-constrained solutions with higher reliability. We presented two synthetic and one field magnetic data example to test the validity. All results demonstrate that the solution from our self-constrained inversion can delineate the geological bodies with clearer boundaries and much more concentrated physical property.