SYNOPSIS
PERL PROGRAM NAME:
AUTHOR: Juan Lorenzo (Perl module only)
DATE:
DESCRIPTION:
Version:
USE
NOTES
Examples
SYNOPSIS
SEISMIC UNIX NOTES SUFXDECON - random noise attenuation by FX-DECONvolution
sufxdecon <stdin >stdout [...]
Required Parameters:
Optional Parameters:
taper=.1 length of taper
fmin=6. minimum frequency to process in Hz (accord to twlen)
fmax=.6/(2*dt) maximum frequency to process in Hz
twlen=entire trace time window length (minimum .3 for lower freqs)
ntrw=10 number of traces in window
ntrf=4 number of traces for filter (smaller than ntrw)
verbose=0 =1 for diagnostic print
tmpdir= if non-empty, use the value as a directory path prefix
for storing temporary files; else, if the CWP_TMPDIR
environment variable is set, use its value for the path;
else use tmpfile()
Notes: Each trace is transformed to the frequency domain.
For each frequency, Wiener filtering, with unity prediction in
space, is used to predict the next sample.
At the end of the process, data is mapped back to t-x domain. ",
Credits:
CWP: Carlos E. Theodoro (10/07/97)
References:
Canales(1984):'Random noise reduction' 54th. SEGM
Gulunay(1986):'FXDECON and complex Wiener Predicition
filter' 56th. SEGM
Galbraith(1991):'Random noise attenuation by F-X
prediction: a tutorial' 61th. SEGM
Algorithm:
- read data
- loop over time windows
- select data
- FFT (t -> f)
- loop over space windows
- select data
- loop over frequencies
- autocorelation
- matrix problem
- construct filter
- filter data
- loop along space window
- FFT (f -> t)
- reconstruct data
- output data
Trace header fields accessed: ns, dt, d1
Trace header fields modified: User's notes (Juan Lorenzo) untested
CHANGES and their DATES
Import packages
instantiation of packages
Encapsulated hash of private variables
sub Step
collects switches and assembles bash instructions by adding the program name
sub note
collects switches and assembles bash instructions by adding the program name
sub clear
sub fmax
sub fmin
sub ntrf
sub ntrw
sub taper
sub tmpdir
sub twlen
sub verbose
sub get_max_index
max index = number of input variables -1