The purpose of this thesis is to present an automated picking and inversion procedure, which is designed to accurately and objectively identify the main reflections within Ground Penetrating Radar (GPR) data sets; to characterize them in terms of their arrival times, peak amplitudes, and polarities; and to recover from these and other quantities the internal stratigraphy and EM properties of the subsurface. In this text the main features and formulas of the developed algorithms are presented, while also highlighting both the advantages and limitations of the proposed auto-picking and inversion procedure with respect to other commonly used methods. In particular, the algorithms are tested on a synthetic GPR profile and their performance is assessed by comparing the inversion results with the initial model. The main uncertainty factors of the procedure are also analyzed, with a particular focus on sampling-related signal distortions, leading to the definition of a recommended minimum threshold for the sampling rate selected during data acquisition. The procedure is also applied to a glaciological 3-D GPR data set, in order to study the internal stratigraphy, density distribution, total volume, and water content of an alpine glacier.
