It was pointed out in all publications about AGCM for experimental **data** [8,6,20,24] that there is a huge misfit between measured **data** and computationally simulated data, in the case of waves propagation in a non-attenuating medium. This misfit is evident from a simple visual comparison of simulated and experimental curves, see, e.g. Figures 3 and 4. This discrepancy is the main difficulty for the numerical treatment of experimental **data** by any computational method. In other words, the experimental **data** are lying far away from the range of the operator, which needs to be inverted to solve the CIP. It is well known that this is a compact operator. Hence, its range is very narrow and the inversion problem is unstable. Therefore, regularization is necessary. Still, the regularization theory gives recipes only in the case when the right hand side of the operator equation is not far from the range of that operator. However, in the case of those experimental data, the right hand side is actually far away from that range. Therefore, the crucial step of all above cited publications about experimental data, was the **data** pre-processing procedure. This procedure has extracted such a piece of the **data** from the whole data, which looked somewhat similar with the computationally simulated data. Still, it was impossible to estimate the distance between the extracted **data** and the range of that compact operator. Furthermore, those **data** pre-processing procedures cannot be rigorously justified neither from the Physics nor from the Mathematics standpoint. They were based on the intuition only. The single criterion of their success was the accuracy of resulting solutions of corresponding CIPs. Since accurate results were obtained for the blind real **data** in [6,20,24], then those **data** pre-processing procedures were unbiased. We describe below the **data** pre-processing procedure. It consists of two steps: extracting a piece of **data** from the whole **data** and multiplying this piece by a calibration factor. This factor is unknown and needs to be determined numerically.