70 
Fishery Bulletin 111(1) 
56 10°N 
56 05°N 
56.00°N 
55 95“N 
55 90 N 
1 54. 2° W 154 1“W 154 0°W 153 9°W 1538°W 153 7°W 1536°W 153 5°W 
Figure 2 
The study area at Snakehead Bank in the Gulf of Alaska, south of Kodiak Island. Bathymetric 
contours are drawn at 50-m intervals. The locations where data were collected in 2009 with a Sim- 
rad ME70 multibeam echo sounder from the large-scale trackline and during focused surveys are 
shown in red (classified as untrawlable) and blue (classified as trawlable). Camera data collected 
in 2009 and 2010 with a stereo drop camera and a remotely operated vehicle are shown as green 
squares (untrawlable) and cyan circles (trawlable). 
target angle direction; the occurrence of grating lobes 
is specific to the design of the transducer array that 
generates beams). A pulse duration of 1.5 ms was used 
for each beam. During transmission and reception, the 
beam-pointing directions were compensated for pitch 
and roll of the ship with a GPS-aided inertial motion 
unit (IMU). The IMU was also used to georeference 
the data collected with the MBES. The standard target 
method was used to calibrate the combined transmit- 
receive sensitivity of each beam (Foote et al., 1987). 
In comparison with the Simrad ME70, most hydro- 
graphic MBES are capable of generating an order of 
magnitude more beams with beam opening angles of a 
fraction of a degree and, therefore, produce a relatively 
high density of bathymetric soundings and measure- 
ments of seafloor backscatter. To achieve a similarly 
high density of data with fewer beams, we processed 
the Simrad ME70 data with a hybrid multibeam and 
phase-differencing technique (Lurton, 2010) that pro- 
vided hundreds of independent seafloor soundings 
(each of which was associated with a measure of S b ) 
over a swath that nominally covered ±60°. At beam 
angles away from normal incidence, the insonified por- 
tion of the seafloor (the area on the seafloor defined 
by the intersection of the sonar pulse within the beam 
pattern of the transducer array) acts as a discrete tar- 
get; therefore, each beam was processed as if it were 
a phase-measuring bathymetric sonar (Lurton, 2010, 
section 8.2.3). Because this approach is more accu- 
rate at higher incidence angles (Jin and Tang, 1996), a 
weighted mean amplitude detection (Lurton, 2010, sec- 
tion 8.3.3) was used for beams with incidence angles 
of only a few degrees. For our data, the transition be- 
tween these 2 bottom detection approaches correspond- 
ed to an incidence angle of approximately 15°. The raw 
soundings were then merged with vessel position and 
attitude data and corrected for refraction through the 
water column. The georeferenced soundings were used 
to extract the rugosity in a grid of 25-m squares, or 
cells, by computing the ratio of the observed surface 
area within each grid cell to the area of a plane fitted 
to the same data. 
A measure of the acoustic power was associated 
with each bottom detection and was converted to S b 
by accounting for system gains and calibration offsets, 
spherical spreading and absorption in the water col- 
umn, and area insonified. Area insonified was estimat- 
ed with the assumption that the seafloor was flat and 
with the method described by Lurton (2010, section 
3.4.3). Applications of these radiometric corrections 
provided a realization of the angle-dependent seafloor 
backscatter, which was used to help characterize the 
seafloor, on each ping. Figure 1 shows predictions of 
the angle-dependent S b for different substrate types 
that range from very fine silt to rough rock, on the 
basis of a scattering model that includes estimates for 
acoustic impedance, seafloor roughness, and sediment 
volume scattering strength (APL, 1994). In general, it 
can be difficult to disambiguate between the different 
factors that underlie these scattering curves (Fonseca 
and Mayer, 2007), but they do offer some separation 
between different substrate types. On the basis of an 
