Here we demonstrate a case study of depth imaging applied to legacy data (shot in 70s and 80s) from Central Poland with a strong overprint of salt tectonics. We use a novel, curvelet-based approach to condition the low-fold gathers in order to improve the performance of the autopicker and subsequent tomographic model updates. Superior results are obtained when a proper conditioning of the gathers is done before running autopicker for tomography. Our 2D Discrete Curvelet Transform based conditioning algorithm run in a two-step mode on the common offset sections and on the depth-slices seems to improve the performance of the autopicker and thus provide more reliable input to grid tomography. Additionally, in case of legacy data, such conditioning acts as a trace regularization.


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