Seismic data are usually contaminated by both random and coherent noise, even when the data have been migrated reasonably well and are multiple-free. Seismic attributes are particularly effective at extracting subtle features from relatively noise-free data. Certain types of noise can be addressed by the interpreter through careful structure-oriented filtering or post migration footprint suppression. However, if the data are contaminated by multiples or are poorly focused and imaged due to inaccurate velocities, the data need to go back to the processing team to alleviate those problems. Another common problem with seismic data is their relatively low bandwidth. Significant efforts are made during processing to enhance the frequency content of the data as much as possible to provide a spectral response that is consistent with the acquisition parameters. The interpreters will have a better understanding of the geology, the play concept, access to any well data, and therefore be better able to keep or reject alternative filter products that are consistent or inconsistent with the interpretation hypothesis. We begin our discussion by reviewing alternative means of suppressing random noise on our migrated seismic images, with the most promising methods being various implementations of structure-oriented filtering. Next, we address acquisition footprint, which may appear to be random in the temporal domain but is highly correlated to the acquisition geometry in the spatial domain. After running the data through the cleaning phase, we evaluate alternative methods for frequency enhancement of the input seismic data. We conclude with a summary on the choice of the frequency-enhancement methods on the basis of the examples generated with different workflows.


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