Incorporating reflection data into refraction statics solution

Bernard K. Law, Daniel Trad


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.

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