ccp stacking

Common Conversion Point (CCP) Stacking

CCP stacking runs off an RF dataset with quality metric labels applied. Such input file is produced by the Compute RFs workflow. For producing CCP stacking, additional information is supplied to script plot_ccp_batch.py as shown below.

• Transects file containing a network code on the first line, followed by a series of station code pairs which indicate directed seismic profiles to be plotted. For example, for network 7X a text file containing a set of profile lines looks like this:

  7X
  MA51,MA11
  MA62,MA12
  MA63,MA13
  MA64,MA14
  MA65,MB11
  MB62,MB12
  MB63,MB13
  MB64,MB34
  MA11,MB13
  MA21,MB22
  MA31,MB34
  MA41,MB44
  MA51,MB54
  MA62,MB64
  

  The actual choice of profile lines is up to the user.

• Path to the definition file for construction FederatedASDFDataSet. This is required for converting network and station codes into actual latitude/longitude coordinates.

The workflow schematic is shown here:

The following command line options are the common ones recommended. See plot_ccp_batch.py --help for more details.

• Name of input RF file with quality labels, i.e. output produced by rf_quality_filter.py.
• Name of FederatedASDFDataSet initialization file, use to convert station code to earth location.
• Name of transects definition file. This consists of a network code on the first line, followed by a series of one station code pair per line
• Filtering options: --apply-amplitude-filter is recommended. This is the same option filtering function used in RF stacking for report generation.
• Plotting options:
  • Amplitude scale: e.g. --stack-scale 0.3. Usable range is expected to be roughly 0.2 to 0.4.
  • Depth of transect in km: e.g. --depth 100.0
  • Vertical spacing of sample points in depth direction: e.g. --spacing 1.0
• Output folder name. Since this script generates one output file per transect, the script requires an output folder name in which the files are stored.

Be aware that for small input data volume, the CCP stack plots may be very sparse.