The priority of microgravity focusing inversion in 3D modeling of subsurface voids

Abstract

Geophysical methods can be used to detect voids, cavities, and tunnels. Inversion of microgravity data provides the distribution of the subsurface density. It can be effective to reconstruct concealed cavities and other underground openings, as they are often at different depths that have a low density. We have smooth and focusing inversion methods that apply the minimum norm and minimum support stabilizers. In this study, we used smooth and focusing inversion methods by reweighted regularized conjugate gradient (RRCG) method for inverting microgravity data. Here, these methods are utilized to inversion of microgravity data from two synthetic models and a microgravity data set over a tunnel located in Yucca Flat, Nevada. The results demonstrated smooth inversion cannot detect sharp boundaries and overestimates the bottom depths of the underground openings, but the focusing inversion method recovers models with sharp boundaries and does not have the problems of the previous method. The results indicate the priority of the focusing inversion method for the detection of voids by microgravity data.