MB-System

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MB-System

  • MB-System is Free software for the processing and display of swath and sidescan sonar data. It can handle both multibeam bathymetry and sidescan sonar image data.

Import into GRASS

See the GRASS and GMT wiki help page for more information.
  • Ungridded data points may be piped directly from mblist to GRASS's v.in.ascii module.
d.vect's zcolor= option can be used to color by depth value.
See the v.colors addon script for colorizing point data in GRASS (v.colors may be unsuitable for massive datasets).
  • ".fnv" navigation files can be imported with the v.in.mbsys_fnv addon module in a number of different ways:
  1. track: ship's track
  2. port_trk: port-side outward track
  3. stbd_trk: starboard-side outward track
  4. scanlines: lines perpendicular to direction of travel
  5. swath: coverage area
  6. track_pts: ship's track as points
  7. all_pts: ship's track, port, and stbd track points
I think the swath area coverage is particularly neat.


Examples

  • Export Lat/Lon + depth data from XTF datafile into a GRASS Lat/Lon location
mblist -I 074.XTF -OXYz | v.in.ascii out=track074 x=1 y=2 fs=tab
  • Export Lat/Lon from the XTF datafile, reproject into the current GRASS location's projection, and import into GRASS with v.in.ascii
mblist -I 074.XTF -OXY | m.proj -i | cut -f1 -d' ' | \
  v.in.ascii out=track074 x=1 y=2 fs=tab

Idea: write a v.in.cdl script that will parse a NetCDF/CDL file and automatically set v.in.ascii's column= option with column names and types.


  • Read a series of .fbt pre-processed bathymetry files, reproject into the current projection, and grid into raster maps.

First create .fbt .fnv and .inf summary files

 find . | grep '.[xX][tT][fF]$' > tmplist
 mbdatalist -F-1 -I tmplist > datalist-1 
 mbdatalist -F-1 -I datalist-1 -N

Next create quick GMT plot of the nav lines to check coverage

 # scan multi-dir for bounds
 for lltype in Longitude Latitude ; do
   for tb in head tail ; do
     grep $lltype `find | grep '\.inf$'` | \
       awk '{print $3 "\n" $6}' | sort -n | $tb -n1
   done
 done
 EXTENT=167.68/169.09/40.48/40.82
 
 mbdatalist -F-1 -I datalist-1 -R$EXTENT > survey-datalist
 mbm_plot -F-1 -I survey-datalist -N
 ./survey-datalist.cmd

Based on the above PostScript file set GRASS region by eye:

#LL: n=40:48N s=40:37N w=168:40E e=169:06E

Convert to local projection with m.proj:

echo "168d40E 40d37N
169d06E 40d48N" | m.proj -i

Plug those numbers into g.region:

g.region s=4616845.02 n=4636836.33 w=520872.47 e=524862.14

Set the grid cell size to 5m, and align to whole numbers, and check that the rows x columns is reasonable (smaller than 40000x40000):

g.region res=5 -a -p

Adjust resolution to smaller grid size if needed (1000x1000 is fine for a summary image).

g.region res=10 -a -p

Scan, read, and reproject all .fbt data into a single x,y,z text file:

note that this file can get very large, perhaps too large for a 32bit OS/filesystem. In these cases you can pipe directly into r.in.xyz so no file is written to disk.
note that -Rw/e/s/n is used to skip over out-of-region data. MB-System and GMT will accept DDD:MM:SS.SSSh format as well as decimal degrees, just like GRASS.
The following assumes the output is projected, for import into WGS84 lat/lon skip the cs2cs command or at least change "%.3f" to "%.8f".
OUTPROJ="`g.proj -jf`"
( 
  for FILE in `find . | grep '\.fbt$'` ; do
    echo "Reading <$FILE> ..." 1>&2
    mblist -I$FILE -D3 -R168:40E/169:06E/40:37N/40:48N
  done 
) | cs2cs -f '%.3f' +init=epsg:4326 +to $OUTPROJ | \
   tr ' ' '\t' > fbt_UTM58_zoom.dat

Based on scan of the .inf files and personal knowledge set some reasonable bounds for the z-data:

# scan multi-dir for depth bounds
for tb in head tail ; do
   grep Depth `find | grep '\.inf$'` | \
      awk '{print $3 "\n" $6}' | sort -n | $tb -n1
done
#-2.7339
#200.6072

ZRANGE="-250,-2"

Run r.in.xyz to make aggregate raster maps. (Running 3 of them in the background to take full advantage of a quad-core CPU)

r.in.xyz fs=tab x=1 x=2 z=3 zrange=$ZRANGE \
  in=zoom_fbt.dat out=zoom_fbt.mean method=mean --q &
r.in.xyz fs=tab x=1 x=2 z=3 zrange=$ZRANGE \
  in=zoom_fbt.dat out=zoom_fbt.median method=median --q &
r.in.xyz fs=tab x=1 x=2 z=3 zrange=$ZRANGE \
  in=zoom_fbt.dat out=zoom_fbt.trim20 method=trimmean trim=20 --q &
r.in.xyz fs=tab x=1 x=2 z=3 zrange=$ZRANGE \
  in=zoom_fbt.dat out=zoom_fbt.n method=n

In my sample data, median and trim-mean 20% look the best.

Analyze "n" map for number of pings per square-meter.

no-data cells not randomly distributed so we set n=0 to NULL so we aren't biasing the data as much
r.null zoom_fbt.n setnull=0
r.univar zoom_fbt.n

"mean:" is number of pings per square-meter for cells which have data.

Now analyze bathymetry data:

r.univar zoom_fbt.median

and set some nice colors:

r.colors zoom_fbt.median color=bcyr

Finally display it:

d.mon x0
d.rast zoom_fbt.median
d.legend zoom_fbt.median
d.barscale at=5,5