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:
- track: ship's track
- port_trk: port-side outward track
- stbd_trk: starboard-side outward track
- scanlines: lines perpendicular to direction of travel
- swath: coverage area
- track_pts: ship's track as points
- 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