Joint Inversion of Two-Dimensional Direct Current Resistivity and Radio- Magnetotelluric Data Based on Fuzzy C-Means Clustering Model Constraint

Abstract:

In combination with the advantages of the direct current resistivity （DCR） and radio-magnetotelluric （RMT） methods， a joint inversion of two-dimensional DCR and RMT data was performed in this paper. A balancing operator was proposed to adjust the weightings of the two types of data in the joint inversion. To obtain a reliable inverted resistivity model， a fuzzy C-means （FCM） clustering was added to the regularization objective function based on classical minimum structure constraint. In the inversion， a strategy of increasing the weight of the FCM part with the inversion iteration based on root-mean-square error calculation was applied. The single inversion and joint inversion of the DCR and RMT data were compared， and the advantage of joint inversion was summarized. The synthetic tests show that the recovered resistivity model with joint inversion is closer to the true subsurface structure than that with single inversion. Moreover， the FCM clustering constraint improves the accuracy of the inverted model.

Key words:direct current resistivity;radio-magnetotelluric;fuzzy C-means clustering;joint inversion