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Processing Multibeam Backscatter DataQiu-hua Tang a b , Xing-hua Zhou b c , Zhong-chen Liu b & AND De-wen DU b ca Marine Geology College, Ocean University of China , Qingdao,266003, Chinab First Institute of Oceanography, State Oceanic Administration ,Qingdao, 266061, Chinac Department of Land Surveying and Geo-Informatics , Hong KongPolytechnic University, Hung Hom , Kowloon, Hong KongPublished online: 19 Aug 2006.

To cite this article: Qiu-hua Tang , Xing-hua Zhou , Zhong-chen Liu & AND De-wen DU(2005) Processing Multibeam Backscatter Data, Marine Geodesy, 28:3, 251-258, DOI:10.1080/01490410500204595

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Marine Geodesy, 28: 251–258, 2005Copyright © Taylor & Francis Inc.ISSN: 0149-0419 print / 1521-060X onlineDOI: 10.1080/01490410500204595

Processing Multibeam Backscatter Data

QIU-HUA TANG,1,2 XING-HUA ZHOU,2,3

ZHONG-CHEN LIU,2 AND DE-WEN DU2,3

1Marine Geology College, Ocean University of China, Qingdao 266003, China2First Institute of Oceanography, State Oceanic Administration,Qingdao 266061, China3Department of Land Surveying and Geo-Informatics, Hong Kong PolytechnicUniversity, Hung Hom, Kowloon, Hong Kong

A new highly precise source of data has recently become available using multibeam sonarsystems in hydrography. Multibeam sonar systems can provide hydrographic qualitydepth data as well as high-resolution seafloor sonar images. We utilize the seafloorbackscatter strength data of each beam from multibeam sonar and the automatic clas-sification technology so that we can get the seafloor type identification maps. In thisarticle, analyzing all kinds of error effects in backscatter strength, data are based on therelationship between backscatter strength and seafloor types. We emphasize particularlyanalyzing the influences of local bottom slope and near nadir reflection in backscatterstrength data. We also give the correction algorithms and results of these two influentfactors. After processing the raw backscatter strength data and correcting error effects,we can get processed backscatter strength data which reflect the features of seafloortypes only. Applying the processed backscatter strength data and mosaicked seafloorsonar images, we engage in seafloor classification and geomorphy interpretation infuture research.

Keywords Multibeam sonar systems, backscatter strength, seafloor sonar images

With the development of marine engineering, seafloor resources exploration, portconstruction, and seafloor pipe investigation, marine geologists and marine engineeringexperts want to know the detail characteristics of sea bottom sediments. The acousticmethod is available and is a rapid way to detect sea bottom. Moreover, with the developmentof modern sonar technology, acoustic experts need to know the influence of sea bottomacoustic characteristics in ocean sound single transmission. Sea bottom has been animportant research aspect in ocean acoustics. The acoustic method has been applied inconfirming the relationship of sediment acoustic parameters and sediment geological

The authors acknowledge the assistance of the Center for Ocean Mapping and EngineeringInformation Research in the First Institute of Oceanography and the Department of Land Surveyingand Geo-Informatics, Hong Kong Polytechnic University. This research work is supported by theyoung grant from the State Oceanic Administration of China (Project code: 2002306), 863 Programof China (Project code: 2001AA613040), Hong Kong Polytechnic University project (Project code:G-V931) and Hong Kong RGC project (code: BQ 734).

Address correspondence to Qiu-hua Tang, Marine Geology College, Ocean University of China,Qingdao 266003. China, E-mail:

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252 Q.-H. Tang et al.

attributes. Applying this method, geologists can easily distinguish the different seafloortypes, and this is an important development of acoustic remote sensing in marine science.

In the 1970s, Marine geologists first utilized the echo sonar signal in seafloor charac-teristic mapping. When sidescan sonar appeared, marine scientists could get seafloor sonarimages which described seafloor morphology in detail and could qualitatively classify dif-ferent seafloor types. Scientists could qualitatively distinguish many types of seafloor, suchas rock, gravel, sand, mud and so on, but they could not accurately recognize these. Fromthe end of 1980s to the early 1990s, the research importance of acoustic seafloor classi-fication had transferred to the use of digital technical analyzing of the echo signal anddescribing quantificationally seafloor surface layer properties. Multibeam sonar systemscan perfectly survey an entire seafloor area with the swath surveying method. They canprovide hydrographic quality depth data as well as high-resolution seafloor sonar images.We get seafloor backscatter strength data of each beam from multibeam sonar and utilizeautomatic classification technology so that we can obtain the seafloor type identificationmaps.

Seafloor classification using multibeam sonar data started to be researched very early.It appeared in many related articles in the 1990s. Duke University’s D. Alexandrou andD. Pantzartzis studied seafloor classification using neural networks in 1990. In 1993, Nor-wegian computing center’s R. B. Husedy and colleagues applied statistical methods forseafloor classification from multibeam sonar backscatter data.

However, many researches of multibeam seafloor classification ignore or do not con-sider the influences of local bottom slope and near nadir reflection in backscatter strengthdata. Hence, these classification results are not very good. In this article, analysis of all kindsof error effects in BS data is based on the relationship between backscatter strength andseafloor types. Moreover, we give the correction algorithms of local bottom slope and nearnadir reflection influence. After processing the raw backscatter strength data and correctingerror effects, we can get processed backscatter strength data which reflect the features ofseafloor only. Finally, detailed seafloor sonar images can be obtained through mossaickingand gridding processed backscatter strength data. Applying these processed sonar images,we will engage in seafloor classification and geomorphy interpretation in the future research.

The Relationship between Backscatter Strength and Seafloor Types

The multibeam sonar is active sonar system. The strength of the received by the hydrophonesis defined by the sonar equation (Lurton et al., 1994; Simrad 1998; Zietz et al., 1996).

EL = SL − 2TL + BS, (1)

where, EL is the echo level, SL is the transmitter source level, 2TL is the two-way transmis-sion loss, and BS is the target strength which includes the local backscattering strength.

From equation (1), the EL received by the multibeam sonar is corrected using the two-way transmission loss 2TL, then the measured backscatter strength BS can be obtained.The backscatter strength is understood as echoes from the seafloor, and these are dependenton the incidence angle, seafloor roughness, sediment properties (density, sound speed, andvolume inhomogeneities), and the sound through the water column (Simrad 1998).

The backscatter value then represents the seafloor’s ability to reflect sound energy.Typically, this will make it possible to differentiate between different types of sediments.Rock reflects more energy than sand, which in turn reflects more energy than silt, and so

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Processing Multibeam Backscatter Data 253

Figure 1. Beam geometrical configuration of multibeam sonar systems.

on. BS is defined as

BS = BSB + 10 lg A, (2)

where A is the seafloor insonified area (Figure 1).Around normal incidence (θ = 0◦),

A = θT θR R2, (3)

and elsewhere,

A = 1

2 sin θcτθT R, (4)

where θT and θR are respectively, the transmitter and receiver beamwidths; R is the range,θ is the beam departure angle, c is the sound velocity and τ is the pulse length.

In Equation (1), BSB is the bottom backscatter coefficient which is the property thatdetermines the reflectivity of the seafloor. It is dependent on the incidence angle θ .

When θ = 0◦, BSB is the constant.

BSB = BSN (θ = 0◦). (5)

When 0◦ < θ > 25◦, BSB linearly changes with the incidence angle, and its change is ran-dom. When θ ≥ 25◦, BSB is not only determined on the incidence angle but also dependenton the seafloor roughness. Its variety complies with the Lambert’s law.

BSB = BSO + 10 lg cos2 θ (θ ≥ 25◦). (6)

According to Equations (2), (3), (4), (5), and (6), around normal incidence,

BS = BSN + 10 lg(θT θR R2). (7)

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And elsewhere

BS = BSO + 10 lg cos2 θ + 10 lg(cτθT R/2 sin θ ), (8)

Where, BSN is the normal backscatter strength, BSO is the oblique backscatter strength.They reflect the seafloor’s characteristics only. The incidence angle θ can be calculatedthrough Snell’s law.

In order to get BSN and BSO , the backscatter data must be corrected by the Lambert’slaw (removing 10 lg cos2 θ ) and the seafloor insonified area (removing 10 lg A). Finally, theseafloor classification is based on the relationship between corrected backscatter strengthand seafloor types.

Data Processing

In the multibeam sonar recorders, there is longitude, latitude, and backscatter strengthinformation. Due to many factors, such as ocean environmental noise, sound scattering andreverberation, sound transmission loss, sound absorbing, local bottom slope, and near nadirreflection, surveying backscatter strength cannot directly reflect true seafloor characteristics.Hence, we need to process raw backscatter data (Figure 2).

During the traditional processing of multibeam sonar data, only sound transmissionloss, ray bending compensation, and Lambert’s law compensation are only concerned.Local bottom slope and near nadir reflection are not concerned in data processing. In thisarticle, we mainly analyze these two influential factors with EM3000 multibeam sonar datain Jiaozhou Bay, Qingdao, and we give correction algorithms and results in the following.

Local Bottom Slope and Seafloor Insonified Area Correction

When seafloor flatting, seafloor incidence angle θ is achieved by using Snell’s law. If thelocal bottom has slope, sloping angle β has to be taken into account. So actual incidenceangle θ ′ is

θ ′ = θ − β. (9)

Figure 2. Process flow for backscatter data processing.

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Processing Multibeam Backscatter Data 255

According to Equation (6),

BSB = BSO + 10 lg cos2(θ − β). (10)

We choose three points M1(x1, y1, z1), M2(x2, y2, z2), M3(x3, y3, z3) (z1 > z2 > z3) in theslope. The three points are not in a line and they form a Triangulated Irregular Network(TIN). Then the slope equation is

a(x − x1) + b(y − y1) + c(z − z1) = 0, (11)

where, coefficient a, b, c is got through normal vector n:

n =

∣∣∣∣∣∣∣i j k

x2 − x1 y2 − y1 z2 − z1

x3 − x1 y3 − y1 z3 − z1

∣∣∣∣∣∣∣ .Then sloping angle β is:

β = arccos

( |c|√a2 + b2 + c2

). (12)

Equation (2) indicates BS is related to the seafloor insonified area A. Assuming seafloor isflat,

A = 1

2 sin θcτθT R. (13)

Because of slope angle β, seafloor insonified area is lager. It decreases the BS calculatingprecision and needs to be corrected. Corrected actual seafloor insonified area A′ is

A′ = A/ cos β = 1

2 sin θ cos βcτθT R. (14)

Through calculated β and A′, we can remove the local bottom slope and seafloor insoni-fied area influences from raw backscatter strength. Finally, BSN and BSO , which are notdependent of incidence angle but representative the seafloor characteristics, are obtained(Figure 3). In Figure 3, Data 1 represents a flat seafloor with no local bottom slopes correc-tion. Data 2 represents a corrected local bottom slope and seafloor insonified area.

Near Nadir Reflection Correction

In the near nadir area, as the sound signal is more affected by specular reflection, most ofthe energy returned to the sonar cannot be considered as backscatter data, which is formedby near normal incidence specular and subbottom returns. It will only occur within thefirst few degrees from nadir and behave like an irregular band of high-backscatter pointsalong the ship’s track in seafloor sonar image. We can get the seafloor sonar image usingthe traditional multibeam sonar post-process software (Simrad 1998) (Figure 4a). FromFigure 4a, we clearly see a bright strip and high reflectivity values. In this area, the soundreflection energy cannot directly be considered as the backscatter strength to classify theseafloor types; first one needs to remove the influence of near nadir reflection.

Many commercial software and multibeam sonar researchers do not process thiscorrection; however, we apply two methods to remove the influence of near nadir reflection.

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Figure 3. Local bottom slope influence and correction.

1. We set the positive or negative degrees areas in the near nadir and clear up these data.2. We can give the nadir region a lower priority than the other regions, then create a set

of data priorities (priorities range from 0 to 1) as the function of incidence angle (SeeTable 1). We choose Gaussian weighted mean algorithm to calculate the near nadir area

Figure 4. Near nadir reflection influence and correction. (a) No clearing up reflection influence.(b) Clearing up reflection influence.

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Processing Multibeam Backscatter Data 257

Table 1The relationship between incidence angle and corresponding priority values

Incidence angle (◦) −60.0 −45.0 −15.0 −14.9 14.9 15.0 45.0 60.0Values 0.2 1.0 0.8 0.1 0.1 0.8 1.0 0.2

backscatter strength,

Wi = A × er2

a2 , (15)

where, r is the distance from the center of the bin to the data point, a is the distance at whichthe weighting function falls to 1/e of its maximum value, and A is a normalizing factor setso that the sum of all the weights adds to a value of 1.

Calculating backscatter strength

BS =n∑

i=1

Wi BSi

/n∑

i=1

Wi . (16)

While this is calculated nadir area backscatter strength, its values are coherent to other areasbackscatter strength. Moreover, beams are representative of the actual seabed sediment types(Figure 4b).

Filtering Backscatter Strength Data

Although corrected for local bottom slope and near nadir reflection, the backscatter strengthdata still include other noise factors. In order to remove or decrease these influences andget the fine seafloor sidescan map, we should process these sonar data by operating thelo-pass boxcar median filter. The lo-pass boxcar median filter is a typical nonlinear filterand its calculation is very easy. It reveals a nice performance in filtering overlap white noiseand can effectively decrease the stripe noise appearing in the backscatter strength. Hence,using the lo-pass boxcar median filter, we can bring out fine scale features and differencesin seafloor texture from the backscatter data.

Finally, the backscatter strength data have been corrected by the transmission loss,ray bending, Lambert’s correction, seafloor local slope, insonified area correction, nearnadir reflection correction, and lo-pass boxcar median filter. Above all, factual seafloorbackscatter information which represents only the seafloor types can be obtained, and it canbe used for the further seafloor classification research.

Producing Seafloor Sonar Images

With removal from the multibeam sonar raw backscatter data of many kinds of in-fluential factors, the data are now representative of the actual seafloor character. Wehave produced mosaic seafloor sonar images by interpolating, gridding, and mosaickingprocessed backscatter data (Figure 5). Applying these mosaic seafloor sonar images, wemay engage in seafloor geomorphy interpretation, seafloor target detection, and seafloorclassification in the all sorts of marine science research. In Figure 5, the mosaic seafloorsonar image consists of four griding and mosaicking multibeam survey lines.

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Figure 5. Mosaic seafloor sonar images.

Conclusions and Future Work

The first theory analysis presented here confirms that seafloor classification can be under-taken because different seafloor types have different scattering and attenuation properties.By using models and algorithms for the backscatter strength, we were able to correct thestrength data for various confounding influences. We are now developing algorithms to pro-cess the corrected image in order to segment the backscatter strength into uniform texturesand then to classify the seafloor types.

References

Alexandrou, D., and D. Pantzartzis. 1990. Seafloor classification with neural networks. IEEEOceans’90 Conference Proceedings, pp. 18–23.

Husedy, R. B., O. Milvang, A. S. Solberg, et al. 1993. Seabed classification from echosounder datausing statistical methods. IEEE Oceans’93 Conference Proceedings, pp. 229–233.

Lurton, X., S. Dugelay, and J. M. Augustin. 1994. Analysis of multibeam echo-sounder signals fromthe deep seafloor. IEEE Oceans’94 Conference Proceedings, pp. 213–218.

Simrad, 1998. Instruction manual of Simrad Triton seabed classification.Zietz, S., J. H. Satriano, and A. Geneva. Development of physically-based ocean bottom classifi-

cation analysis system using multibeam sonar backscatter. 1996. IEEE Oceans’96 ConferenceProceedings, pp. 1058–1063.

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