We present here a joint inversion method to build P- and S-wave velocity models from surface-wave and P-wave refraction data, specifically designed to deal with laterally varying layered environments which can present strong velocity contrasts with depth. In this case, a smooth minimum-structure inversion produces smooth models even for geological models, which are overall layered. The proposed algorithm is also able to incorporate a-priori information available over the site and any physical law to link model parameters. This method presents advantages with respect to individual surface wave analysis and refraction tomography since it imposes internal consistency for all the model parameters, reducing the required apriori assumptions and the ill-ness of the two methods. We describe the algorithm and we show its application to synthetic and field datasets.

Joint Inversion of Surface-wave Dispersion and P-wave Refraction Data for Laterally Varying Layered Models / Boiero, Daniele; Calzoni, Corrado; Socco, Laura. - ELETTRONICO. - (2011), pp. 1-5. (Intervento presentato al convegno 73rd EAGE Conference & Exhibition tenutosi a Vienna, Austria nel 23 - 26 Maggio 2011).

Joint Inversion of Surface-wave Dispersion and P-wave Refraction Data for Laterally Varying Layered Models

BOIERO, DANIELE;CALZONI, CORRADO;SOCCO, LAURA
2011

Abstract

We present here a joint inversion method to build P- and S-wave velocity models from surface-wave and P-wave refraction data, specifically designed to deal with laterally varying layered environments which can present strong velocity contrasts with depth. In this case, a smooth minimum-structure inversion produces smooth models even for geological models, which are overall layered. The proposed algorithm is also able to incorporate a-priori information available over the site and any physical law to link model parameters. This method presents advantages with respect to individual surface wave analysis and refraction tomography since it imposes internal consistency for all the model parameters, reducing the required apriori assumptions and the ill-ness of the two methods. We describe the algorithm and we show its application to synthetic and field datasets.
2011
9789073834125
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2489098
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