Near surface models from refraction inversion contain several types of errors,which are partially compensated later in the data ﬂow by reﬂection residual statics. In this work we modify the dataﬂow to automatically include feedback information from surface consistent reﬂection statics from stack-power maximization. We modify GLI by adding model and data weights computed from the long wavelength components of surface consistent residual statics. By using an iterative inversion, these weights allow us to update the near surface velocity model and to reject ﬁrst arrival picks that do not ﬁt the updated model. In this non-linear optimization workﬂow the refraction model is derived from maximizing the coherence of the reﬂection energy and minimizing the misﬁt between model arrival times and the recorded ﬁrst arrival times. This approach can alleviate inherent limitations in shallow refraction data by using coherent reﬂection data.
View full article as PDF (9.98 Mb)