Ground roll and mud roll in seismic reflection data can be analysed to infer a near surface S-wave velocity model from which converted-wave static correction can be computed. A workflow that exploits processing tools available in industrial processing codes has been implemented to extract dispersion curves and related uncertainties along a seismic line. The dispersion curves are then inverted with a laterally constrained inversion algorithm based on an initial model previously determined through a Monte Carlo inversion. The final S-wave velocity models are finally used for static computation. The application of the method to synthetic data supplied static correction values with error lower than 5%.
Surface wave dispersion analysis is widely used to build near surface S-wave velocity models in the filed of geotechnical engineering and engineering seismology. In recent years, following the idea proposed by Mari (1984) this method started also to be applied to near surface issues concerning the processing of hydrocarbon exploration seismic data (Strobbia et al., 2009). In particular, few authors showed that ground roll and mud roll in seismic reflection data can be exploited to supply a near surface velocity model to be used for static computation (Ernst, 2007) . In this context we developed an innovative processing workflow aimed at handling big seismic datasets and perform static computation from surface wave analysis in a quasi-automatic way. Eni workflow is based on seismic processing tools available in industrial processing codes for the extraction of a set of surface wave dispersion curves which are then inverted with a laterally constrained inversion algorithm (Socco et al., 2009). The retrieved VS models are used to compute S-static corrections along the whole seismic line. We here present a schematic outline of the workflow and some results obtained on a synthetic dataset containing smooth lateral variations. The error on static computation with respect to the true one was below 5%.
The first three steps can be performed in an industrial seismic processing code, exploiting different editing and spectral analysis tools which are normally used in seismic reflection processing. Some preliminary tests are performed to select optimum processing window length and maximum offset to be used in the extraction of dispersion curves. The dispersion curve is extracted by each panel using the fk- Music transform and performing an automatic picking of the spectral maxima. These dispersion curves are called pre-stack dispersion curves and are used to compute the uncertainties. The fk-Music spectra which have the central spatial coordinate falling in a given spatial range are then stacked and used for the estimation of the post-stack dispersion curves which are then exported in a convenient format and inverted. This procedure allows large datasets to be handled by exploiting the noticeable computational efficiency of seismic processing tools. Once the dispersion curves have been extracted, they are evaluated through a quality control procedure that permits to discard erroneous data points and dispersion curves. Finally they are inverted through an on purpose implemented inversion tool.