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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
To link to this article: http://dx.doi.org/10.1080/01490410500204595
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Marine Geodesy, 28: 251258, 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:
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 Universitys D. Alexandrou andD. Pantzartzis studied seafloor classification using neural networks in 1990. In 1993, Nor-wegian computing centers 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 seafloors 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