Statics are primarily derived from seismic refractions or first arrivals, and strongly rely on their quality of gathers. At the initial stage of seismic processing, refractions are the single data input. In common practise, inversions of first breaks are based on user’s interpretation of early arrivals and, therefore, they are subject to systematic errors, especially if picking is automated as for large 3D data sets. In such circumstances, it is desirable to avoid overfitting of observations in inversion. An active criteria counter to systematic mistakes is presented here, based on statistical benchmarking against independent non-seismic measures. The approach is based on Simultanous Joint Inversion for measure integration. While rock physics relations are normally unstable at the near surface, the qualitative concept of localized anomaly can be transported between various geophysical domains, as is normally done in prospect play evaluation. Anomaly distribution consistency between domains is here used as a discriminant of input data through a-posteriori inversion result benchmarking. A static solver can, therefore, weight more the first break data, which are experimentally confirmed by several independent measurements, rather than contradicting data. Concordance is based on concordant anomaly generation in the a-posteriori inverted model.


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