Equation (3) shows an example where the first output signal is the ordinary one and the second output signal used a filter interlaced with zeros. We prepare subroutines that allow for more output signals, each with its own filter interlace parameter given in the table jump(ns). Each entry in the jump table corresponds to a particular scaling of a filter axis. The number of output signals is ns and the number of zeros interlaced between filter points for the j-th signal is jump(j)-1.
The multiscale helix filter is defined in module
mshelix , analogous to the
single-scale module helix
.
A new function
onescale
extracts our usual helix filter
of one particular scale
from the multiscale filter.
mshelixmultiscale helix filter definition
We create a multscale helix with module
createmshelixmod
.
An expanded scale helix filter is like an ordinary helix filter
except that the lags are scaled according to a jump.
createmshelixmodcreate multiscale helix
First we examine code for estimating a prediction-error filter
that is applicable at many scales.
We simply invoke the usual filter operator
hconest
for each scale.
mshconestmultiscale convolution, adjoint is the filter
filter ! multiscale prediction-error
The multiscale prediction-error filter finding subroutine
is nearly identical to the usual subroutine
find_pef() .
(That routine cleverly ignores missing data while estimating a PEF.)
To easily extend pef to multiscale filters
we replace its call to the ordinary helix
filter module
hconest
by a call to mshconest.
mspefmultiscale PEF
The purpose of pack(dd,.true.)
is to produce the one-dimensional array expected by
our solver routines.
Similar code applies to
the operator in
(4)
which is needed for missing data problems.
This is like
mshconest
except the adjoint is not the filter but the input.
msheliconmultiscale convolution, adjoint is the input
The multiscale missing-data module msmis2
is just like the usual missing-data module
mis2
except that
the filtering is done with the multiscale filter
mshelicon
.
msmis2multiscale missing data