| dc.description.abstract | Land data acquisition often suffers from rough top-surface topography and complicated near-surface conditions. The resulting poor data quality makes conventional data processing very difficult. Under such circumstances, where simple model assumptions may fail, it is of particular importance to extract as much information as possible directly from the measured data. Fortunately, the ongoing increase in available computing power makes advanced data-driven imaging approaches feasible; thus, these have increasingly gained in relevance during the past few years. The common-reflection-surface (CRS) stack, a generalized high-density velocity analysis and stacking process, is one of these promising methods. It is applied in a non-interactive manner and provides, besides an improved zero-offset simulation, an entire set of physically interpretable stacking parameters that include and complement the conventional stacking velocity. For every zero-offset sample, these so-called kinematic wavefield attributes are obtained as a by-product of the data-driven stacking process. As will be shown, they can be applied both to improve the stack itself and to support subsequent processing steps... | |
