Although generally very powerful, noise and multiple attenuation techniques can often leave<br>remnants in seismic data. For example, noise from extraneous sources such as rigs and other<br>boats can be hard to model and fully remove using standard methods. Similarly, multiple<br>remnants are often present after multiple attenuation when multiples are generated by<br>relatively complex geology, such as rugose water bottoms or salt, and as such do not conform<br>to the assumptions of most multiple attenuation algorithms. These remnants can cause<br>problems in later processing, for example through the generation of migration noise and<br>contamination of AVO analysis etc. and therefore often need to be further attenuated in the<br>processing sequence. As these remnants are often localized and may have high amplitudes<br>compared to the underlying data, they can be relatively easy to identify and can be targeted in<br>a different number of domains. The application of a wavelet transform (wavelet<br>decomposition) on pre-stack data can be used to separate signal from coherent noise in both<br>frequency and time. The noise can then be removed from the data by a variety of noise<br>attenuation methods in the wavelet domain. We show two examples to illustrate the<br>effectiveness of this method: the attenuation of residual multiples and attenuation of boat<br>noise.


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