While the smoothness of the summed time shifts are often justifiable in non-faulted areas, the time shifts can change abruptly across the faults. In these situations, we desire an inversion technique that yields smooth time shifts in non-faulted areas while preserving sharp time shifts across the faults. In addition, no pre-defined fault indicator should be supplied.
In this paper, we present an automatic edge-preserving method for flattening faulted data without requiring an input fault model. The method uses iterative re-weighted least-squares (IRLS). A Geman-McClure weight function (fault indicator) of data residuals is computed at each non-linear iteration to allow outliers in the data residuals.
The only requirements are that part of the fault tip-lines are encased in the data and that the faults are oriented vertically. The resulting weight generated by this IRLS method is a fault indicator cube that best flattens the data. This is an important difference from many traditional automatic fault detectors that are defined by local discontinuities.