Multibeam bottom detection algorithm

The multibeam bottom detection algorithm can be finely tuned using the settings on the Multibeam Bottom Detection Properties dialog box. These settings are available:

		Units	Symbol used in algorithm
Identification of candidate samples
	Detection start depth	m	Dstart
	Detection stop depth	m	Dstop
	Minimum threshold factor	%	Tpercent
	Minimum number of samples between candidates	-	Nskip
	Number of candidates to choose per beam	-	Nsamples
Joining of candidate samples
	Number of beams used for seeding	-	Nbeams
	Number of samples to join by the depth criterion	-	Ndepth
	Maximum change in range allowed between neighboring samples	%	DRmax
	Maximum number of samples rejected before stopping	-	Ngap
Quality control
	Maximum range of edge samples (%)	%	Rmax

The multibeam bottom detection algorithm uses these settings as follows:

Identify candidate bottom samples (maximum of Nsamples) in each beam for each ping as follows:
1. Calculate a threshold value
  T = Min + (Max-Min) x Tpercent / 100
  where: Min and Max are the minimum and maximum sample values in this beam between Dstart and Dstop.
2. Scan the beam from Dstart to Dstop as follows:
  1. look for the next sample with a value that exceeds the threshold T.
  2. look for the next sample with a value that is below the threshold T.
  3. from within this range of samples, pick the sample S with the highest value.
  4. add sample S to a list of candidate bottom samples.
  5. if the list contains no more than Nsamples samples, discard the sample with the lowest value (the weakest sample).
  6. skip Nskip samples and repeat.
  Result: each beam in each ping now has a list of candidate bottom samples associated with it containing up to Nsamples of the strongest samples found.
Identify candidate bottom detections in each ping as follows:
1. Create a list of detection seeds.
  The seeds are all the candidate bottom samples from the Nbeams most vertical beams in the ping. There will be maximally Nsamples x Nbeams detection seeds and each seed is the first sample in a bottom detection candidate. Each candidate seed is assessed against the previous one. If the vertical distance is greater than DRMax, it is marked as 'bottom not detected' and discarded.
  
  Specifically, a start beam B is chosen which is either the most vertical or, if there is no most vertical beam then the middle beam (i.e. if beams are numbered a to b, then the beam numbered [a+b]/2 is chosen). Further beams are then chosen such that the following two criteria are met:
  1. Nbeams beams are chosen (highest priority)
  2. the chosen beams are equally distributed about the beam B (lowest priority) - an asymmetry may occur when beam B is near the edge of the sector plot or if Nbeams is even.
  Result: There are now up to Nbeams x Nsamples seeds chosen. Each of these will become a bottom detection candidate.
  
  Note: An internal beam number is used for choosing B and the neighbouring beams. This may not correspond with the beam number reported on the Echoview status bar which respects the beam numbering of the original source instrument.
2. Extend each seed into a complete bottom detection candidate.
  Each of the seeds becomes a bottom detection candidate by adding to the most suitable candidate bottom sample from each neighboring ping until completion, first in one direction then the other. Any seed that has a value greater than RMax is discarded.
  1. if the beam is within Ndepth/2 beams of beam B then choose the candidate bottom sample which is nearest in depth to the next sample nearest to B, otherwise
  2. choose the candidate bottom sample which is nearest a line L which is created by linear regression on all samples already chosen (in the space defined by depth=f(beam number)
If this is a bottom detection preview, then display each multibeam bottom detection candidate on the visible echogram, otherwise create a bottom surface as follows:
1. Note the depth of each bottom detection candidate
2. Project all the bottom detection candidates onto a horizontal flat plane
3. Connect the projected points with a triangulated irregular network in which the projected bottom detection candidates are the vertices of triangles.
4. Give each of the vertices in the triangulated irregular network the depth of the bottom detection candidate associated with that vertex (as noted in a.)
  
  Note: For multibeam pings with either a high elevation or low tilt, noise in the bottom echo trace may cause exaggerated unevenness or spikiness in the resulting surface. Improving the quality of the bottom detection using the settings may help to reduce this.

Multibeam bottom detection algorithm

See also