pylops.Smoothing2D

pylops.Smoothing2D(nsmooth, dims, nodir=None, dtype='float64')

2D Smoothing.

Apply smoothing to model (and data) along two directions of a multi-dimensional array depending on the choice of nodir.

Parameters:

nsmooth : tuple or list

Lenght of smoothing operatorin 1st and 2nd dimensions (must be odd)

dims : tuple

Number of samples for each dimension

nodir : int, optional

Direction along which smoothing is NOT applied (set to None for 2d arrays)

dtype : str, optional

Type of elements in input array.

See also

lops.signalprocessing.Convolve2D

2D convolution

Notes

The 2D Smoothing operator is a special type of convolutional operator that convolves the input model (or data) with a constant 2d filter of size \(n_{smooth, 1} \quad x \quad n_{smooth, 2}\):

Its application to a two dimensional input signal is:

\[y[i,j] = 1/(n_{smooth, 1}*n_{smooth, 2}) \sum_{l=-(n_{smooth,1}-1)/2}^{(n_{smooth,1}-1)/2} \sum_{m=-(n_{smooth,2}-1)/2}^{(n_{smooth,2}-1)/2} x[l,m]\]

Note that since the filter is symmetrical, the Smoothing2D operator is self-adjoint.

Attributes:

shape : tuple

Operator shape

explicit : bool

Operator contains a matrix that can be solved explicitly (True) or not (False)

Examples using pylops.Smoothing2D

2D Smoothing

Causal Integration