Multi-parameter inversion using radio-magnetotelluric and direct current resistivity data

The simultaneous inversion of multiple parameters is beneficial to improving the inversion imaging accuracy and interpretation. We carried out a multi-parameter joint inversion using the data of shallow subsurface electromagnetic methods. The parameters recovered from inversion include resistivity, permittivity and permeability. And the observed data we used in inversion is composed by direct current resistivity (DCR) and radio-magnetotelluric (RMT). DCR method contains apparent resistivity and RMT contains apparent resistivity and tipper data. To realize the mutual constraint between different inverted properties, a fuzzy C-means clustering algorithm is used for the iteration of multi-parameter joint inversion. The synthetic examples indicate that, multi-parameter inversion based on FCM clustering recovers the properties more accurately in comparison with the general minimum structure regularization inversion. And the inversion using RMT and DCR data simultaneously recovers the properties more accurately over the single observed data.