-lammond, S.ific Spreadingurn from Sea

'. Geol. Congr.

8, Automatedp-Sea Bottom

~ractal?, 1989,

rization of the

iphy, Geophys.

.ng of Seafloornd-Order Sta-

fše Processing,

J Morphologyi Rate, 1. Geo-

Wiley & Sons,

rerm Behavior

'. and Stauffer,Survey Profes-

1986, Applica-'requency Bot-10-1422,ships Between

je Deposits in

l5-505,pisodicity and

:theast Pacifc

Geophys. Res.

: Crest Conver-t, 700-712.I Longitudinal'. Res. Let!. 6,

Nature, W H.

age Processing

, SeaMARC II7469-7490.:ion of Statisti-

i Its Slope Dis-

ds for Describ-14(10), 1061-

Lphy: A Record

0), 1541-1544.

: Method and

Computers and

gi, R. Y, Neal,tion òfKilaueane 8, 1984, in

ds.), Volcanism71-508.

Processing and Analysis of Simrad Multibeam Sonar Data

NEIL C. MITCHELL

Present address: Department of Geological Sciences, University of Durham, Science Laboratories, South Road, Durham DHl 3LE England; andDepartment of Geodesy and Geomatics Engineering, University of New Brunswick, Fredericton Canada

(Accepted 7 March 1996)

Key words: Bathymetry, mapping, multi beam echosounder, data

processing

Abstract. The common approach to analysing data collected withmultibeam and sidescan sonars is to visually interpret charts ofcontoured bathymetry and mosaics of seabed images. However, someof the information content is lost by processing the data into chartsbecause this involves some averaging; the analysis might uncovermore information if done on the data at an earlier stage in theprocessing. Motivated by this potential, I have created a software

system which can be used to analyse data collected with Simrad

EMIOOO (shallow water) and EMl2 (deep water) multibeam sonars,as well as to generate bathymetry contour charts and backscattermosaics. The system includes data preprocessing, such as navigation

filtering, depth filtering (removal of outlying values), and amplitudemapping using the multi beam bathymetry to correctly position im-age pixels across the swath. The data attributes that can be analysedinclude the orientation and slope of the seafloor, and the mean signal

strength for each sounding. To determine bathymetry attributes suchas slope, the soundings across a number of beams and across aseries of pings are grouped and a least-squares plane fitted to them.Bathymetric curvature is obtained by detrending the grouped datausing the least-squares plane and fitting a paraboloid to the residuals.

The magnitudes and signs of the paraboloid's coeffcients revealdepressions and hils and their orientations. Furthermore, the sea-

floor geology can be classified using a simple combination of theseattributes. For example, flat-lying sediments can be classified wherethe backscatter, slope and curvature fall below specified values.

Introduction,"

Multiple-beam echo sounders were originally designedto measure the topography of the seafloor (e.g., Klein-rock, 1992). However, these sonars are becoming moreversatile, with the newest generation also capable ofproducing seafloor images from the backscatter, whichare similar to those produced by sidescan sonars. Fur-ther developments include the introduction of mul-tiple-frequency sonars and multiple narrow-beamprofilers. With this present rapid evolution of the tech-nology, a re-evaluation of the methods we use to pro-cess and analyse these data could be fruitful if we areto make the most of the diverse and large datasets thatare to follow.

Marine Geophysical Researches 18: 729~739, 1996.

¡g 1996 Kluwer Academic Publishers. Printed in the Netherlands.

The longer-term aim of this software effort is tomake the interpretation more objective. At first glance,this might seem an impossible task, given its potentialcomplexity. For example, a human interpreter wil as-sess the seafloor's geology by recognising the shapesof geological features in contour maps, by recognisingtypical textures and shapes in shaded-relief maps, gra-dient maps and sonar images, by assessing differencesof tone in sonar images related to different seafloormaterials, and by recognising angle of incidence effectsin sonar images which give the appearance of a seaflooriluminated by an artificial sun. (The term 'sonar im-ages' is used here for backscatter images created fromeither multibeam or sidescan sonar data.) However, anumber of these characteristics can be calculated fromthe data, and this can potentially make the interpreta-tion at least partly objective, as wil be shown.

This paper describes how various attributes of Sim-rad EM 1000 and EM 12 soundings can by studied toexplore the seafloor's geology. (The term 'attribute'here means any information that can be attributed toa sounding, such as the depth, local slope, curvatureor echo strength (Table I).) The sounding attributescan be plotted in profile to study geographic variations,or they can be plotted together to . find correlationsbetween them. Alternatively, they can be gridded andstudied in map form (I use the GMT system to displaythe grids (Wessel and Smith, 1991)), or they can becombined to produce simple seafloor classifications.There are further potential advantages of calculatingattributes from the raw data, rather than from DTMsor backscatter mosaics. For example, seafloor gradi-ents or shaded-relief images may show detail closer tothe resolution limit of the sonar if computed from rawsounding data; surface statistics wil be more represen-tative if computed from raw data; attributes such asbeam quality factors and depth variability can be usedto exclude noisy data; and the direct mapping ofshaded-relief imagery and seafloor gradients reducesartefacts at overlapping swaths due to navigation,

sound velocity, roll, pitch or draught errors. If shaded-

!l

730

Processing

Bathymetry

Backscatter

Further

N. C. MITCHELL

Attribute

Multibeam data attributes

TABLE I

The three orthogonal Cartesian components of the surface normal vector".Angle ß between the surface normal vector (directionn of surface tilt)".Azimuth of the surface normal vector (direction of surface tilt)'.Angle of incidence ø (degrees)'.Standard deviation of depths about the surface (m)'.Three coeffcients a, b, c (m -I) of the paraboloid, and the depth standard deviation about the paraboloid surface (m)b.The two coeffcients a' and b' and rotation angle e for the paraboloid surface in the rotated coordinate frameb.

Mean of signal strengths for each sounding (dB).Mean of signal strengths for each sounding with the manufacturer's Lambert Law correction removed (dB).Standard deviations of signal strengths for each sounding (dB).Raw amplitude values (dB). I.e. list full trace for each specified sounding.

Latitude of the sounding.

Longitude of the sounding.Time (in seconds from the start of the year 1900).Beam number from I to 60 (EMIOOO) or 81 (EMI2).Ping number (counted from start of file or as provided by the sonar).Sounding quality factors, which also indicate bottom detection method.Heading (gyro heading of the vessel or towfsh).Roll (in degrees at the time of transmission).

Number of amplitude samples for each sounding.

" From plane fitted to groups of soundings.b From paraboloid fitted to groups of soundings.

relief images are required in near realtime, this kindof approach may be desirable because it avoids theneed for gridding and other processing. In addition,the mapping may be more effcient where the dataconsist of sparse transit lines because the software neednot search through empty sections of grids. This kindof approach may also be desirable for AutonomousUnderwater Vehicles (Stewart, 1988), where a com-bination of the amplitude and bathymetry attributesdescribed herè,could be used to classify the vehicles'environment.

The Simrad EMIOOO and EMU MultibeamSonars

These systems operate by ensonifying a narrow stripof seafloor across track, and detect the bottom echowith narrow (across-track) listening beams, similar toolder multibeam designs (e.g., de Moustier, 1988).However, unlike with the older designs in which thebottom echo is detected within single beams, the Sim-rad systems use a split array to form pairs of parallelbeams (Hammerstad et aI., 1991). The echo arrival

angle is detected by the slight time delay of echoes

between the two beams, where the time delay is mea-sured as a difference of signal phase. Within the centralbeams, the phase difference is small so the systems

resort to the older method of determining the centre ofmass of the echo (which is called 'amplitude detection'below). The sonars are therefore hybrids, combiningthe older multibeam design with phase-difference tech-niques that have previously been employed with

towfish sonars (Blackinton et aI., 1983). Data collectedwith these sonars show fewer artefacts than previoussystems (de Moustier and Kleinrock, 1986), except fora 'w' -shaped dragging of con tours parallel to the shiptrack in EM12 data which is caused by a depth biasin amplitude-detected beams at ~ 10° off vertical (these

also have greater noise). The artefact is probably

caused by the sonar's time-varied amplifier gain (TVG)which may be too rapidly varying and distorts thebottom echo (E. Hammerstad, pers. commun., 1993),causing the bottom-detection algorithm to locate thebottom echo too late. It occurs at the transition be"tween the phase-difference and amplitude detectionmethods, where the phase difference detections appear'to be generally more reliable. This problem is currently

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PROCESSING AND ANALYSIS OF SIMRAD MULTIBEAM SONAR DATA

being addressed by the manufacturer, though a re-design of the sonar may not completely remove thisartefact because the TVG must compensate for thevarying backscatter with angle of incidence, and this

variation varies between different bottom types (e.g.,de Moustier and Matsumoto, 1993).

The beam forming software can optionally providebeams spaced almost uniformly across track, ratherthan at equally spaced angles, and this gives improvedresults for some bathymetry attributes described below.The swath width to water depth ratio is much greater(7 times water depth or more) than with previous multi-beam sonars, and this results in improved sonar im-agery (Hughes Clarke, 1993).

Backscatter amplitudes are logged by the Simradsystems as seafloor backscatter strengths (see Urick(1983) for definition), which are computed using cali-bration values for the electronics and transducers de-termined at the time of manufacture. The backscatterstrength values include a further partial correction for

the varying angle of incidence, using Lambert's lawfor the variation with angle and assuming a flat seafloor

(the correction can be removed by the software de-scribed here (Table I)). Note that, unless there is a

further calibration at the time of the survey, the back-scatter strength values may be inaccurate and theremay be a d.c. bias in the backscatter values (the dataused in this paper were uncalibrated). However, therelative signal level is still useful and differences be-tween materials are clearly visible in the data.

Initial Processing

The raw Simrad data are translated into a flexible datastructure which later forms the basis for the analysisof data attributes. Depths, amplitudes and raw phase-difference data are bundled together from separate datapackets, and combined with position information bylinearly interpolating the processed navigation. Thedata structure then includes, for each transmission

cycle, a header, ship or fish positions and expandableareas for bathymetry, raw amplitude and phase differ-ence data, and the seafloor imagery. Further areas

could be included for additional frequencies or sub-bottom profiler data. The structure is relatively verboseso that as much information as possible is retainedthroughout the processing sequence. The software'sstructure is modular and uses UNIX~ 'pipes' wherepossible to pass data between processes. The processedSounding data can then be imported to the GMT sys-tem (Wessel and Smith, 1991) to produce traditionalbathymetry contour maps or images.

731

NavigationWithin a small moving window of navigation points,the software computes the median longitude (X) andmedian latitude (Y), and a standard deviation of Xand Yabout the medians. Position spikes are rejectedwhere either the latitude or longitude exceed more thana specified number of standard deviations away fromthe median. Filtered navigation is given by computingthe mean X and Y within the window and withGaussian weights about the central position. Whilethis method is unsophisticated, it is simple and thefilter variables are easily understood by the user.

Depth FilteringSome preliminary despiking of the depths can be doneby comparing each beam with its across-track neigh-bours and flagging soundings where the slopes on bothsides of the beam are greater than a specified threshold.For a more robust error rejection, the despiking canbe achieved by computing the standard deviation ofdepths about their median average (computed withingroups of beams), and flagging depths which aregreater than a specified number of standard deviationsfrom the average. This can be done during the initialformat conversion or later by comparing depths acrossa series of pings, and the results can be checked visuallyusing an interactive multi beam display program(Hughes Clarke et aI., 1993).

The above mentioned error that causes the W-shaped dragging of contours in EM12 data is morediffcult to correct because the magnitude of the deptherror is unpredictable. For the moment, the only solu-tion seems to be to remove the beams containing thisartefact altogether, so an algorithm is used whichsearches for the transition between phase and ampli-tude detections and removes two amplitude-detectedsoundings on' either side.

Projection of Beam Amplitudes to Form Seafloor ImagesAmplitudes supplied by multi beam sonars consist ofamplitude traces representing the echo level with timefor each beam (de Moustier, 1986). To provide seafloorimages, the amplitudes must be translated from thetim~ ordinate to across-track distances. Unlike withsidescan sonars, which commonly require a flat bottomassumption to achieve this, the bathymetry informa-tion can be used to correctly position the amplitudesand reduce layover and foreshortening effects (Mitch-ell, 1991; Reed and Hussong, 1989). The translationcan be done by determining the travel time to pointsalong a curve fitted through each profile of soundings,for example onto a cubic spline (Talukdar et at., 1992;Talukdar and Tyce, 1991). However, the sounding pre-

I:

¡

f.i!

,'f.

732 N. C. MITCHELL

Projection line

f ago

ŠiŠlI

go

Š..l- ~l-Fig. I. The geometry for projecting the amplitude data for a single beam onto the across-track dimension. The illustration in the upper rightshows the general beam geometry. For greater software effciency, the amplitude trace for each beam is projected onto a straight line (f-g),which has a slope given by the adjacent beams. I.e. the data are projected onto a line that passes through beam k and lies parallel with aline drawn through beams k+ i and k- i (the soundings are shown by the solid rectangles). In areas of rugged topography, illustrated by

the thick grey line, the projection error with this flat bottom assumption is the difference between f and l and g and g'.

cision probably does not justify this level of sophistica-tion so a more computationally effcient method isused here by projecting the amplitudes onto a straightline with an aèross-track slope determined from adja-cent soundings (Figure 1). Given that the angularsweep across each beam is small and if the angle ( ø - ex)is not too small, the following simple expression can

be used to compute the across-track distance from thenadir:

Figure 1 shows the distortion that would occur ifthe seafloor profile were significantly curved (thickline); the echo trace is projected onto f-g, whereas itshould be more correctly projected onto f'-g'. In prac-tice this error is small because the true seafloor curva-ture is usually less than the sounding precision.

The amplitude samples are binned into an across-track array, averaging coincident samples and (option-ally) interpolating across any gaps. Since the swath

width varies with changing water depth, the array sizeis kept constant but the across-track spacing of arrayelements is varied so that the image resolution is aconstant proportion of the swath width. This is prefer-able to maintaining a constant spacing because the

along-track dimension of a resolution cell (the sonar'footprint' for a narrow beam sonar (1.7° for EMI2and 3° for EM 1000) is defined by the beam width in

/d=do+(i-io)11 cos ex/sin(ø-ex), (1)where do is the across-track distance to the beam's

centre, i is the amplitude sample number, io is thesample number corresponding to the centre of thebeam, 11, is the sampling interval in slant range (m),ø is the beam inclination angle and ex is the across-track slope.

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PROCESSING AND ANALYSIS OF SIMRA MULTIBEAM SONAR DATA 733

1-.

radians times the slant range, and therefore the actualimage resolution is proportional to water depth. Somefurther processing may be desirable at this stage, forexample removing beam pattern artefacts and angle-of-incidence effects, where the processing could use thecoregistered depth data. It is then a relatively easy

matter to reformat the data in a form readable by

standard sidescan sonar mosaicking software so thatmaps can be produced (e.g., Chavez, 1986; Malinvernoet al., 1990).

:~-

~:5

Calculation of Sounding Attributes

The attributes of the soundings which presently canbe listed by the software are given in Table 1. Besidesthe bathymetry and backscatter attributes, which areexplained below, the software can list various system-related data, which can be useful to diagnose faultswith the system. For example, the roll values listedwith depths from an outer beam can be used to checkfor a roll-meter error, which would produce a correla-tion between roll and the bathymetry. The ping andbeam numbers can be listed with the depths to locateand isolate erroneous data. The number of amplitudesamples are needed, for example, when computing thestandard errors of the mean signal strengths.

Seafloor Orientation, Gradient and Incidence AnglesNormal vectors to the seafloor are determined as ilus-trated in Figure 2. Soundings from adjacent beamsand adjacent pings are grouped together and a least-squares plane is computed for them (e.g., Miler andFreund, 1965). The number of beams and pings canbe adjusted to vary the lengthscale of the slope mea-surements. Data from the corners of the group are

masked out to keep the data set approximately ellipticaland prevent edge bias. Two vectors in the plane of thesurface are forrÎe,4 using the coeffcients of the surface,

and the cross product of these two vectors gives thesurface normal vector, which is normalised to unitlength to give n.

The slope of the seafloor P is given by the scalarproduct of the surface normal n with the unit verticalvector k and taking the arccosine of the product. Theorientation (slope facing diréction) is computed fromthe two horizontal Cartesian components of the sur-face normaL. The angle of incidence (J is given by thescalar product of the surface normal with a vectorrepresenting the inclination of the beam at the centreof each group ('beam axis' in Figure 2) and taking thearccosine of the product. The sounding variability,which is potentially a measure of bathymetric rough-

k.

a: normal vector

k.: vertcal vector

Fig. 2. Computing a least-squares plane to groups of soundingsand determining the surface normal vector n. The software groupssoundings from a series of transmission cycles and across a numberof beams, which surround the central beam labelled 'beam axis',and fits a least-squares plane to the depths. The small vertical barsconnect the data (dots) with the surface; the sum of the squares ofthese is minimised to determine the 'best fitting' plane. The slopeis then given by the angle ß between the surface normal vector andthe vertical, and the incidence angle ø is the angle between the

surface normal and the axis of the central beam of the group.

ness or sounding noise, is given by the standard devi~ation of the soundings about the least-squares surface.

There are further uses of these attributes besidessimple mapping and interpretation. To assist the inter-pretation of sonar images, incidence angles could be

used to generate synthetic sonar images from the ba-thymetry assuming a Lambert law or other angulardependence for the backscatter. The standard devi-ation of the soundings could be used to exclude areasof poor data (for example, to exclude data from the

cross-over region between amplitude and phase detec-tion methods), or could be used as a general diagnosticof data quality.

l1athy~etric ~urvatureWhile the orientation and slope of the seafloor areuseful for interpretation, they do not provide a measureof shape that could be used to objectively map outgeological features (for example, as would be neededfor an automated mapping routine). Two previousstudies ilustrate how bathymetric shape can be charac-terised using simple geometric functions. Shaw (1992)and Shaw and Lin (1993) located fault scarps in grid-ded Sea Beam data over the Mid-Atlantic Ridge byfitting a quadratic surface to the bathymetry and flag-ging areas where the coeffcients of the quadratic wereless than a threshold value chosen to highlight the

734

least-squares plane surface and centred so that x andyare the positions of the soundings in local coordinates(metres north and east from the central beam of thegroup).

N. C. MITCHELL

(a)

i;:

r~ !

r

(b)

z

Fig. 3. Examples of paraboloids. The paraboloid is fitted to thesoundings after they have been detrended using the plane in Figure2 and centred, and the coeffcients a' and b' of the paraboloid fitindicate the shape of the seafloor's topography. (a) Upward-curvedelliptic paraboloid (a' and b' positive); (b) Hyperbolic paraboloid(a' positive and b' negative). A cross-section drawn through theparaboloid in (a) at constant z is an ellipse with semi-minor axis q

and semi-major axis r (where qlr=-l(b'la').

faults. Malinverno (1990) measured the width anddepth of mid-ocean ridge median valleys by fitting aninverted Gaussian to single beam profiles.

A paraboloid is used here as a measure of curvatureand shape because it provides a general descriptionof surface curvature in two dimensions, and becauseparaboloids resemble the bathymetric shape of manygeological features and therefore may be useful forcharacterising them. For example, an upward-curvedelliptic paraboloid with various aspect ratios (Figure3(a)) may represent iceberg scours, pock marks,

troughs between abyssal hills or the change in slopeat the edge of basement outcrops; a downward-curvedelliptic paraboloid may represent abyssal hills, sea-mounts or other mounds; and a hyperbolic paraboloid(Figure 3(b)) may represent the saddle points foundalong abyssal hills or between adjacent seamounts.The following function is fitted to circular groups ofsoundings after they have been detrended using the

It

i',~~:

z = ax2 + by2 + cxy. ~:

(2)

PROCESSING AND ANALYSIS OF SIMRAD MULTIBEAM SONAR DATA

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PROCESSING AND ANALYSIS OF SIMRAD MULTIBEAM SONAR DATA 737

would be e). Saddle points can occur, for example, at

a small depression along an abyssal hil (a' small andhi large), between two adjacent seamounts (a' and b'large), or at a talus fan around the edge of a sedimentpond (a' large and b' small or large depending on theshape of the talus fan).

of'

Characteristics of Sonar Image DataThe image interpretation is slightly different from thatof sidescan sonar images, and therefore attributes ofthe images, displayed separately, may be helpful forinterpretation. Multibeam sonar images are collectedwith a smaller swath width to altitude ratio resultingin more of the image at high incidence angles andtherefore with unfamiliar relief effects; due to the pro-cessing time between pings, the scan rate is usually

lower resulting in a pixelated image; and the hull-mounting commonly results in a noisier image. Toaverage out the effects of noise, this software can listthe mean signal strength for each beam as an attribute(Table I), which can be easier to interpret than theraw data. Figure 4(a) shows this attribute gridded anddisplayed as an image.

The standard deviations of signal strength for eachbeam (computed in decibels) can potentially highlightareas of heterogeneous seafloor. This is because thedata variability that is due to Rayleigh speckle (Good-man, 1976) is constant on a logarithmic scale, andtherefore an anomalously high standard deviation mayindicate more than one bottom type, acousticshadowing or angle of incidence effects across the sea-floor covered by the beam (e.g., Mitchell, 1995). Forthe small number of samples in each beam of Simradsonars, however, this statistic is relatively noisy so itis probably better computed over several beams. Todetermine the importance of angle-of-incidence effectson the image (image variation due to seafloor relief)and beam-pattern effects, amplitudes can be listed withincidence angles,"and the rate of change of backscatterwith angle (e.g., Keeton and Searle, 1996) could poten-tially be displayed as a further attribute.

'i,

.:'"

-~,

,f

l- F'ig. 5. Along track profies of attributes computed for beam 17!. (White line in Figure 4(c)). The data were collected travelling from

Southwest to northeast in Figure 4(c). (a) Bathymetry; (b) slope

given by the angle between the surface normal vector and the ver-tical; (c) standard deviation of depths about a local least-squaresplane (30 m lengthscale); (d) coeffcients a' and b' of the least -squaresparaboloid (200 m lengthscale). The continuous line is a' and thedotted line is b'; (e) mean backscatter strength, uncalibrated and

with the Simrad Lambert law correction not reversed.

,,'

Gridding of AttributesMultibeam soundings are more regularly spaced thanother types of marine geophysical data so iterativesurface-fitting algorithms (Briggs, 1974; Smith andWessel, 1990; Swain, 1975) are unnecessary for deter-mining values over a grid, while weighted mean

methods are more effcient (e.g., Slootweg, 1978). Amethod similar to that of Ware et al. (1991) is usedhere, in which a stream of longitude, latitude, value(and optionally, weights) is read, and each value dis-tributed over a circular region of the grid centred onits geographic location. The values are distributed witha Gaussian weight field rather than with the conic usedby Ware et al. (1991) because grids produced withGaussian weights wil be smoother and can potentiallybe analysed with Fourier methods. The images shownin Figures 4(b) and 4( c) were produced by gridding thedespiked soundings and computing artificial shadingcoeffcients for this grid (Mitchell, 1991). Figure 4(a)

was produced by gridding the mean amplitudes foreach beam.

Simrad EMIOOO Data from the Nova Scotian Shelf

The data in Figure 4 from the Nova Scotian shelfinclude a variable geology of sediments and rocky out-crops which ilustrates the potential for interpretationusing a number of attributes in combination. The datawere collected with a Simrad EMlOOO system in 100 mof water in the approaches to Halifax and have a typicalswath width of 400-500 m here. The geology is de-scribed in detail elsewhere (Loncarevic et al., 1994;

Mitchell and Hughes Clarke, 1994) but essentially in-cludes Cambro-Ordovician sandstone and shale bedsthat have been folded during the Devonian period andlater faulted. These were differentially eroded by gla-ciers or wave action during the last ice age and manytopographic depressions are now filled with silts re-vealed by the low backscattering areas in Figure 4a.A valley formed by erosion along a fault can be seenrunning east-west along the top-right of Figure 4 near44°20.5' N, 63°23' E. Up-turned sedimentary bedsstrike approximately parallel with the swath in thecentre of Figure 4b, for example near 44°19.7' N,

63°25' E.Figure 5 shows profiles for the highlighted beam

running from southwest to northeast in Figure 4(c),including along-track bathymetry (Figure 5(a)), sea-floor slope (Figure 5(b)) computed from groups of 54soundings (a group size of ~30 m), and (Figure 5(c))the standard deviation of depths about the least-squares plane used to compute Figure 5(b). Figure

j

738 N. C. MITCHELL

5(d) shows the coeffcients of the paraboloid surfacefit to groups of 1500 soundings (a group size of

~ 200 m). The continuous line is the a' coeffcient anddotted line the b' coeffcient. Figure 5(e) shows themean of the amplitude samples for each transmissioncycle for this beam, where the mean was computed indB and given as backscatter strengths.

The bathymetry shows sediment ponds at 6-7 and12-14 minutes survey time, with a number of peaksand troughs to either side of them. The sediment pondsproduce regions of low slope in Figure 5(b), low depthstandard deviation in Figure 5(c), low bathymetric cur-vature in Figure 5( d), and low backscatter strength inFigure 5(e), forming a contrast of 15-20 dB with thesurrounding materials. The edges of the ponds, in par-ticular at 12-14 minutes survey time, show an increaseof slope and higher curvature. The backscatter strengthappears to rise by ~ 5 dB earlier than the slope andcurvature (e.g., at 13 minutes), suggesting that theedges of the sediment ponds are rippled or contain amore highly backscattering material such as gravel.The major peaks and troughs are revealed by the curva-ture coeffcients at the start and end of the profile,for example, a major trough at 17.5 minutes, which

produces a large value for a' and small b'. Much ofthe smaller variations in curvature, such as those at16 minutes, indicate saddle points rather than ridges

or troughs becau~e the coeffcients have similar magni-tudes but opposite signs.

These attributes can be combined to produce asimple geological classification. Figure 4(c) shows theseafloor along the beam classified into sediment ponds

(white circles), ridges (dark arrows) and troughs (lightarrows). Sediment ponds were defined as areas wherethe magnitudes of both the paraboloid coeffcients areless than 0.0002 m-i, where the seafloor slope is lessthan 3°, and where the backscatter strength is less than- 30 dB. The ridges were defined as areas where the

coeffcient b:)s less than -0.0009 m-i, the elongationvalue 1(1a'1-lb'I)1 is greater than 0.006 m-i and theslope is less than 8°. The direction of the vector showsthe orientation of the x'-axis computed from e of Equa-tion 3 and its length is proportional to the coeffcientb' (representing curvature perpendicular to the vectoror the degree of 'sharpness' of the ridge). The troughswere defined as areas whéí-e the coeffcient a' is greaterthan 0.001 m-i, b' is greater than -0.001 m-I, theelongation value is greater than 0.00066 m - i and the

slope is less than 10°. The direction of the vector showsthe orientation of the y' -axis and its length is propor-tional to the coeffcient a'.

The ridge and trough annotation in Figure 4(c) pro-vides information that is complementary to the

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shaded-relief image because the spatial scale of theparaboloid was chosen to locate relatively large fea-tures, which are not well revealed in the image. Furtherspatial scales could be analysed by computing theparaboloid over more or less soundings. However, theinformation is not easily displayed; imagine the Com-

plexity of Figure 4(c) if the ridge and trough vectorswere computed for each of the beams and for differentlength scales. A solution may be to develop algorithmsthat can map out the ridges and troughs by linkingtheir locations on adjacent swaths, and present theirlocations and curvatures in a form that is similar tomaps produced in structural geology of faults andfolds.

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Previous authors have shown that the seafloor can be"classified according to its topographic roughness (e.g., \i'Fox and Hayes, 1985; Goff and Jordan, 1988; Malin- -

verno, 1989), and according to various properties of ;tsidescan sonar imagery (e.g., Pace and Dyer, 1979; ~.Pace and Gao, 1988; Reed and Hussong, 1989; Reut .~et al., 1985; Tamsett, 1993). However, there has beenlittle effort to develop numerical classification algo-

~rithms based on attributes of both topography and¥:sonar imagery. The preliminary results presented here .~suggest that simple geological structures can be classi-fied using a combination of attributes. For example,

areas can be classified as sediment ponds where theyhave a combination of low amplitude, low slope andlow curvature, where curvature is determined by fittinga second order surface (a paraboloid) to groups ofsoundings. The coeffcients of the second order surface

can also be used to locate ridges and troughs in thetopography, and to measure their elongation and ori-entation.

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Acknowledgements

This software was developed at the Universities ofNew Brunswick and Durham with the support of theNatural Sciences and Engineering Research Council

of Canada and the Natural Environment Research

Council (UK). I am grateful to the Department ofGeodesy and Geomatics Engineering at the Universityof New Brunswick and the Department of GeologicalSciences in Durham for the use of facilities, and toPaul Wessel and Walter Smith for the use of the GMTsystem. I am also grateful for discussions on multi beamsonar and processing with John Hughes Clarke, Larry

:cale of the'y large fea-

ige. Furtheriputing the

:owever, theie the Com-

ugh vectorsfor differenti algorithms

; by linkingresent theirs similar to

faults andl:;t

loor can be

~hness (e.g.,

988; Malin- -roperties of

Dyer, 1979;

1989; Reutre has been

:ation algo-

graphy and

:sented here

il be c1assi-

Jr example,

where theyi¡ slope anded by fittingl groups of

rder surface

ughs in theon and ori-

iversities ofJport of the

rch Councili t Researchiartment of~ University, Geologicalties, and toJfthe GMT1 multibeamlarke, Larry

PROCESSING AND ANALYSIS OF SIMRAD MULTIBEAM SONAR DATA 739

Mayer, Erik Hammerstad and Jane Keeton, and forhelpful comments on this paper by Martin Kleinrock,David Caress and an anonymous reviewer. The vesseltime used to collect the EMIOOO data presented herewas provided by the Canadian Hydrographic Service,with help from John Hughes Clarke, André Godin,Andrew Morley, Ziquin Du and Glen Rogers. The

crew of CSS Frederic G Creed are acknowledged, withmany thanks, for their work in collecting these dataand for their hospitality.

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