Inversion of Surface Wave data suffers from solution non uniqueness and is hence strongly biased by the initial model. A priori geological information can be used to produce a reliable initial model: these information, however, are rarely available along all the survey line since they are mainly punctual information. Moreover, when we perform a laterally constrained inversion we have to be aware that bad quality data, though localized in a limited region of the entire dataset, can bias the whole result. In this work we present a procedure to estimate the quality of the Surface Wave dataset before the inversion and to produce a consistent initial model for the LCI. We prepared some tools to make the quality control of dataset semi-automatic: besides, we arranged a method to extend a priori punctual information to the whole survey line, in order to generate a pseudo 2D initial model able to make the inversion process more reliable. This method is based on a sensitivity analysis and on the application of scale properties of Surface Waves. Our procedure ensures a better model parameters estimation, makes the inversion process faster and allows a proper tuning of the strength of lateral constraints in LCI.


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