In the following sections, I will use the EMD based dip filter to decompose the synthetic and field data examples into 5 dip components. Note that the number of dip components is not limited to 5. As one can see, the EMD based dip filter can act as a data-driven dip components separator, without any parameters to be defined except for the separation number .
Other structural filtering approaches that require a precise slope estimation will perform better when the dip estimation is applied on dip-separated profiles than the traditional implementation. The slope estimation after dip separation will be obviously superior than that without dip separation. One can use the slope estimation shown in Figure 8 as an example. The slope estimations in the separated dip components are more precise than that of the original profile in the dip conflicting area. The separated slope is smoother while the original slope is highly non-stationary when the events cross each other. The dip estimation error will transform into large filtering damages in any structural filtering approaches. Other structural filtering approaches that do not rely on the slope estimation will also benefit a lot because a reduced number of slope components will result in a much easier parameterization, such as choosing the prediction length in the prediction filtering (Chen and Ma, 2014) and choosing the minimum rank in the multichannel singular spectrum analysis (MSSA) (Oropeza and Sacchi, 2011).