Spatially random noise
The spatially random noise uncorrelated from trace to trace is largely attenuated by CMP stacking (basic data processing sequence). Any remaining noise on stacked data can be attenuated by spatial prediction filters. Figure 6.0-41 shows a stacked section before and after noise attenuation. The process of noise attenuation is meant to achieve attenuation of noise so as to enhance coherent events such as reflections on a stacked section. It is not meant to create any coherent events that do not exist in the data.
The spatial prediction filters also can be applied to common-offset data prior to stacking. They are conveniently designed and applied in the frequency-space domain. What is predictable by a prediction filter for a given frequency component is a signal in the lateral direction and what is not predictable is considered spatially random noise. This is just the opposite of what is intended by statistical deconvolution (deconvolution). Specifically, the predictive deconvolution operator is a prediction error filter, and the output from predictive deconvolution is the unpredictable part of the input — the white reflectivity series. What is predictable by predictive deconvolution is multiples contained in a one-dimensional (1-D) seismogram associated with vertical incidence.
In linear uncorrelated noise attenuation, a frequency-space prediction filter for attenuation of spatially random noise is presented accompanied by a [[Multichannel filtering techniques for noise and multiple attenuation#F.4 Free-surface multiple attenuation|mathematical discussion.
- Coherent linear noise
- Treatment of coherent linear noise by conventional processing
- Reverberations and multiples
- Treatment of reverberations and multiples by conventional processing