Nonlinear Electromagnetic Inversion of Damaged Experimental Data by a Receiver Approximation Machine Learning Method

In practical applications, electromagnetic measurements often contain damaged data due to malfunctioning receivers, which can severely influence the inversion performance. Thus, in this letter, a new receiver approximation machine learning (RAML) method is proposed to repair the data from the damaged receivers and the repaired data are the input to the dual-module nonlinear mapping module-image enhancing module machine learning scheme for the 2-D inverse scattering problem. The RAML method has been tested for the inversion of both measured transverse electric and transverse magnetic datasets provided by Institute Fresnel. Excellent inversion performance has been obtained when 10 out of 241 (4.15%) receivers are damaged. Furthermore, in an extreme situation, the RAML method can also have good inversion performance even though half of the receivers are damaged.