The first one has been used by Brown and Clapp (2000) and Guitton (2001) and consists of deconvolving the data pef by the noise pef. It works pretty well but the deconvolution might be unstable when dealing with non-stationary filters Rickett (1999). If and are the data and noise pef respectively, the estimated signal pef becomes
The last method consists of estimating the signal with the standard adaptive subtraction scheme. A pef is then estimated from the signal and used inside the hybrid scheme. You might be tempted to repeat this process iteratively in order to improve your signal pef. In my experience, I find that an iterative process might diverge, especially when non-stationary filters are involved.
In the next two sections, I show synthetic and field data examples. They prove that the hybrid adaptive filtering gives a better estimate of the noise when primaries and multiples interfere.