sfslpf estimates a non-stationary filter using shaping regularization.

The method is described in the reproducible paper Adaptive multiple subtraction using regularized nonstationary regressio

The following example from tccs/lpf/plut shows a common-offset section from the Pluto synthetic dataset before and after adaptive multiple subtraction with the help of **sflpf**.

Given target data $m(\mathbf{x})$ (specified with **match=** parameter) and a collection of fitting functions $s_k(\mathbf{x})$ (specified in the standard input), **sflpf** finds the fitting coefficients $b_k(\mathbf{x})$ by minimizing the error

$m(\mathbf{x}) – \displaystyle \sum_{k=1}^{N} b_k(\mathbf{x})\,s_k(\mathbf{x})$

while constraining the coefficients to be smooth. The smoothness is controlled by **rect#=** parameters, as in sfsmooth.

Shaping regularization is carried out iteratively, **niter=** controls the number of iterations.

The mean coefficient from the example above is shown in the figure below.

Optionally, a prediction-error filter can be applied to whiten the residual. The filter is specified with the help of **pef=** and **lag=** parameters, with a multidimensional helical filter specified as in sfhelicon.

The complex version of the same program is sfclpf.

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