MB-System

• go to the Marine Science wiki page
• go to the GRASS and GMT wiki page

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

• Gridded data can be loaded into GRASS either as a GMT NetCDF grid via the r.in.gdal module (see GDAL supported formats), old-style GMT grid using r.in.bin -h, or as an Arc ASCII grid via the r.in.arc module.

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

Import x,y,z bathymetry into GRASS

Into a vector points map

• 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 attribute table column names and types.

Into a gridded raster map

• 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:

A datalist given to mblist then piped through 'm.proj -i' then piped into r.in.xyz to form a gridded elevation map

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.

r.in.xyz fs=tab x=1 x=2 z=3 zrange=$ZRANGE \
  in=zoom_fbt.dat out=zoom_fbt.n method=n

r.in.xyz fs=tab x=1 x=2 z=3 zrange=$ZRANGE \
  in=zoom_fbt.dat out=zoom_fbt.mean method=mean

r.in.xyz fs=tab x=1 x=2 z=3 zrange=$ZRANGE percent=25 \
  in=zoom_fbt.dat out=zoom_fbt.median method=median

r.in.xyz fs=tab x=1 x=2 z=3 zrange=$ZRANGE percent=25 \
  in=zoom_fbt.dat out=zoom_fbt.trim20 method=trimmean trim=20

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

Create vector maps from .fnv nav data

Add bounding box

MAP=swath_MBM04_007_0501_XTF

v.db.addcol $MAP column="north DOUBLE PRECISION, \
  south DOUBLE PRECISION, east DOUBLE PRECISION, west DOUBLE PRECISION"

eval `v.info -g $MAP`

echo "UPDATE $MAP SET north = $north, south = $south, \
  east = $east, west = $west" | db.execute

Import loop and patching

change DB to sqlite

db.connect driver=sqlite database='$GISDBASE/$LOCATION_NAME/$MAPSET/sqlite.db'

Import all files in the datalist using a loop:

for TYPE in track swath ; do
   for TRACK in `cut -f1 -d' ' datalist-1` ; do
      OUTNAME="${TYPE}_$( echo "`basename $TRACK .fnv`" | sed -e 's/[- .]/_/g' )"
      echo "Importing <$OUTNAME> ..."
 
      CAT=`echo "$OUTNAME" | cut -f3,4 -d'_' | tr -d '_'`
 
      v.in.mbsys_fnv in="${TRACK}.fnv" out="$OUTNAME" type=$TYPE cat=$CAT
   done
done

Patch together all maps with a loop:

for TYPE in track swath ; do

   # create an empty vector
   v.in.ascii -e out="datalist_${TYPE}s" --quiet

   # copy table structure
   BASE_MAP=`g.mlist vect pattern="${TYPE}_*" | head -n 1`
   exist_cols=`db.describe $BASE_MAP -c | grep '^Column' | cut -f2- -d: | \
     sed -e 's/:/ /' -e 's/CHARACTER:\(.*\)/VARCHAR(\1)/' | \
     cut -f1 -d: | tr '\n' ',' | sed -e 's/,$//' -e 's/^ //'`
   v.db.addtable "datalist_${TYPE}s" columns="$exist_cols"

   # run the patch loop
   for MAP in `g.mlist vect pattern="${TYPE}_*"` ; do
      echo "Patching <$MAP> ..."

      v.patch -a -e in="$MAP" out=datalist_${TYPE}s --overwrite --quiet

   done
done

• trac #859: v.patch is failing to preserve our carefully selected category numbers.

View and interactive query:

d.mon start=x0
d.vect datalist_swaths type=area color=150:170:200 fcolor=200:220:250
d.vect datalist_tracks width=2

d.zoom

d.what.vect datalist_tracks -f