Evaluation of inverse algorithms in the analysis of magnetic flux leakage data

We evaluate the use of linear and nonlinear inverse algorithms (maximum entropy method, low resolution electromagnetic tomography, L ₁ and L₂ norm methods) in the analysis of magnetic flux leakage (MFL) measurements commonly used for the detection of flaws and irregularities in gas and oil pipelines. We employed MFL data from a pipe with well-defined artificial surface breaking flaws at the internal and external wall. Except for the low-resolution electromagnetic tomography, all algorithms show, on average, similar accuracy in the flaw extent estimation. Maximum entropy and the L₁ norm have a tendency to yield better results for smaller flaws, while the L₂ norm performs slightly better for larger flaws. The errors of the flaw location estimation are comparable for the maximum entropy and the L₂ norm algorithm. The L₁ norm performs worse for those flaws situated on the internal pipe wall. Linear methods (L₂ norm) are easier to implement and require less computation time than nonlinear methods (maximum entropy method, L₁ norm). In conclusion, inverse algorithms potentially provide a powerful means for the detection and characterization of flaws in MFL data