Two-dimensional DC resistivity regularization inversion based on FCM model constraint
This article presents a study on Fuzzy C-Means(FCM)constrained inversion of 2D DC resistivity.Based on the minimum structure model constraint regularized inversion,we compared the L,-norm with L2-norm model constraints.It is observed that the L1-norm inversion yields clearer boundaries and more pronounced block-like distribution of physical properties compared to the L2-norm inversion.Furthermore,by incorporating FCM model constraints,accurate rock property measurements are utilized as reference cluster centers to guide the inversion towards those centers.The application of FCM clustering constraints further enhances the inversion results.Both synthetic and field data inversions demonstrate that the combination of FCM model constraints guided by accurate rock property measurements and L1-norm minimum structure model enables high-precision DC resistivity inversion.
L,normRegularization inversionFuzzy C-Means(FCM)clustering constraintDirect current resistivity
NETL
NSTL
万方数据
