Dual-parameter inversion using two-dimensional tipper data of Radio-magnetotelluric based on clustering algorithm

In this paper, a resistivity and permittivity joint inversion using two-dimensional tipper data of radio-magnetotelluric (RMT) is studied. The finite element modeling of tipper using unstructured grids is carried out. Based on the analysis of the influence of displacement current on tipper data. a simultaneous inversion of resistivity and permittivity is carried out. The fuzzy C-means (FCM) clustering is used as mutual constraint to strengthen the physical coupling between the inversion parameters, and a progressive refining strategy for grid is applied to improve the stability of inversion. Synthetic examples show that the tipper data of RMT has great lateral resolution for resistivity, but poor capability for vertical imaging. Due to the lack of absolute information about the physical properties of the medium the inverted models are greatly affected by the initial model. Moreover, the simultaneous inversion of resistivity and permittivity using tipper data of RMT is feasible. The tipper data has great lateral resolution of permittivity, and the recovered resistivity and permittivity models can be effectively improved by FCM clustering constraint.