Inversion of multi-beam seabed backscatter data in complex sediment areas

JinHua LUO

Prog Geophy ›› 2026, Vol. 41 ›› Issue (3) : 1407-1416.

PDF(3363 KB)
image
Home Journals Progress in Geophysics
Progress in Geophysics

Abbreviation (ISO4): Prog Geophy      Editor in chief:

About  /  Aim & scope  /  Editorial board  /  Indexed  /  Contact  / 
Online first  /  Current issue  /  All issues  /  Top access  /  RSS
PDF(3363 KB)
Prog Geophy ›› 2026, Vol. 41 ›› Issue (3) : 1407-1416. DOI: 10.6038/pg2026II0243

Inversion of multi-beam seabed backscatter data in complex sediment areas

Author information +
History +

Abstract

Seabed characterization based on multi-beam seabed Backscatter Strength (BS) data classifies the seabed as a certain type in the sampling results, which may lead to misclassification in complex sediment areas (including different sediment mixing areas, transition areas, etc.). Currently, there is a lack of reliable analysis and evaluation methods for interpreting the complex sediment areas based on multi-beam seabed BS data. To solve this problem, this article first references seabed samplers to statistic the backscatter data of typical sediment, and obtained its Probability Density Function (PDF). Based on this, (1) for the sediment mixing zone, an equation is established by using a combination of above-mentioned typical PDF to approximate the PDF of the measured BS data, and the steepest descent method is used to solve the proportion of each typical PDF; (2) A qualitative analysis method is proposed for seabed between two typical seabeds, as well as transition zones between different seabeds, based on the PDF of their BS and its skewness, kurtosis, cumulative distribution function, etc. This paper provides a reliable quantitative inversion and qualitative evaluation method for complex seabed areas, where often with characterization errors using conventional methods. It can also be used as an effective means of analyzing the entire area based on BS data when seabed sampling is insufficient.

Key words

Multi-beam seabed backscatter strength / Seabed characterization / Complex seabed / Gaussian mixture model / Statistical distribution

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations
JinHua LUO. Inversion of multi-beam seabed backscatter data in complex sediment areas[J]. Progress in Geophysics. 2026, 41(3): 1407-1416 https://doi.org/10.6038/pg2026II0243

References

List( Publishing order | Descend order by publishing year | Descend order by cited within ) Chart analysis
Amiri-Simkooei A R , Snellen M , Simons D G . Riverbed sediment classification using multi-beam echo-sounder backscatter data. The Journal of the Acoustical Society of America, 2009, 126 (4): 1724- 1738.
https://doi.org/10.1121/1.3205397
Cited in this article [1]
Anderson J T , Van Holliday D , Kloser R , et al. Acoustic seabed classification: current practice and future directions. ICES Journal of Marine Science, 2008, 65 (6): 1004- 1011.
https://doi.org/10.1093/icesjms/fsn061
Cited in this article [1]
Chen J H . Optimal rate of convergence for finite mixture models. The Annals of Statistics, 1995, 23 (1): 221- 233.
Cited in this article [1]
Clarke J E H. 2004. Seafloor characterization using keel-mounted sidescan: proper compensation for radiometric and geometric distortion. //Proceedings of the Canadian Hydrographic Conference. Ottawa: CDROM, 1-18.
Cited in this article [1]
Collier J S , Brown C J . Correlation of sidescan backscatter with grain size distribution of surficial seabed sediments. Marine Geology, 2005, 214 (4): 431- 449.
https://doi.org/10.1016/j.margeo.2004.11.011
Cited in this article [1]
De Oliveira Junior A M. 2007. Maximizing the coverage and utility of multibeam backscatter for seafloor classification [Master's thesis]. Fredericton: The University of New Brunswick, 1-25.
Cited in this article [1]
Fonseca L E, Mayer L A, Kraft B J. 2005. Seafloor Characterization through the application of AVO analysis to multibeam sonar data. Boundary Influences in High Frequency, Shallow Water Acoustics, 340.
Cited in this article [1]
Gaida T C , Mohammadloo T H , Snellen M , et al. Mapping the seabed and shallow subsurface with multi-frequency multibeam echosounders. Remote Sensing, 2020, 12 (1): 52
Cited in this article [1]
Goff J A , Olson H C , Duncan C S . Correlation of side-scan backscatter intensity with grain-size distribution of shelf sediments, New Jersey margin. Geo-Marine Letters, 2000, 20 (1): 43- 49.
https://doi.org/10.1007/s003670000032
Cited in this article [1]
Hamilton L J , Parnum I . Acoustic seabed segmentation from direct statistical clustering of entire multibeam sonar backscatter curves. Continental Shelf Research, 2011, 31 (2): 138- 148.
https://doi.org/10.1016/j.csr.2010.12.002
Cited in this article [1]
Hodgson M E . What size window for image classification? A cognitive perspective. Photogrammetric Engineering & Remote Sensing, 1998, 64 (8): 797- 807.
Cited in this article [1]
Jin S H , Xiao F M , Bian G , et al. A method for extracting seabed feature parameters based on the angular response curve of multibeam backscatter strength. Geomatics and Information Science of Wuhan University, 2014, 39 (12): 1493- 1498.
Jin X L . The development of research in marine geophysics and acoustic technology for submarine exploration. Progress in Geophysics, 2007, 22 (4): 1243- 1249.
https://doi.org/10.3969/j.issn.1004-2903.2007.04.034
Landmark K , Solberg A H S , Austeng A , et al. Bayesian seabed classification using angle-dependent backscatter data from multibeam echo sounders. IEEE Journal of Oceanic Engineering, 2014, 39 (4): 724- 739.
https://doi.org/10.1109/JOE.2013.2281133
Cited in this article [1]
Lucieer V , Lamarche G . Unsupervised fuzzy classification and object-based image analysis of multibeam data to map deep water substrates, Cook Strait, New Zealand. Continental Shelf Research, 2011, 31 (11): 1236- 1247.
https://doi.org/10.1016/j.csr.2011.04.016
Cited in this article [1]
Lucieer V , Hill N A , Barrett N S , et al. Do marine substrates 'look' and 'sound' the same? Supervised classification of multibeam acoustic data using autonomous underwater vehicle images. Estuarine, Coastal and Shelf Science, 2013, 117: 94- 106.
Cited in this article [1]
Luo J H , Feng X Z . Classification of seabed sediment based on multi-beam backscatter statistical distribution. Progress in Geophysics, 2025, 40 (2): 798- 805.
https://doi.org/10.6038/pg2025HH0552
McGonigle C , Collier J S . Interlinking backscatter, grain size and benthic community structure. Estuarine, Coastal and Shelf Science, 2014, 147: 123- 136.
Cited in this article [1]
McSherry T M . A general steepest descent algorithm. IEEE Transactions on Aerospace and Electronic Systems, 1976, AES-12 (1): 12- 22.
Cited in this article [1]
Orange D L , Yun J , Maher N , et al. Tracking California seafloor seeps with bathymetry, backscatter and ROVs. Continental Shelf Research, 2002, 22 (16): 2273- 2290.
https://doi.org/10.1016/S0278-4343(02)00054-7
Cited in this article [1]
Pace N G , Gao H . Swathe seabed classification. IEEE Journal of Oceanic Engineering, 1988, 13 (2): 83- 90.
Cited in this article [1]
Parnum I M, Gavrilov A N, Siwabessy P J W, et al. 2005. The effect of incident angle on statistical variation of backscatter measured using a high-frequency multibeam sonar. //Proceedings of ACOUSTICS 2005. Busselton, 433-438.
Cited in this article [1]
Parnum I M, Gavrilov A N, Siwabessy P J W, et al. 2006. Analysis of high-frequency multibeam backscatter statistics from different seafloor habitats. //Proceedings of the Eighth European Conference on Underwater Acoustics. Carvoeiro, 775-780.
Cited in this article [1]
Parnum I M. 2007. Benthic habitat mapping using multibeam sonar. //Proceedings of the SUT Annual Conference 2009. Perth.
Cited in this article [2]
Preston J . Automated acoustic seabed classification of multibeam images of Stanton Banks. Applied Acoustics, 2009, 70 (10): 1277- 1287.
https://doi.org/10.1016/j.apacoust.2008.07.011
Cited in this article [1]
Preston J M , Christney A C . Compensation of sonar image data primarily for seabed classification. The Journal of the Acoustical Society of America, 2005, 118 (2): 586
https://doi.org/10.1121/1.2040230
Cited in this article [2]
Rzhanov Y , Fonseca L , Mayer L . Construction of seafloor thematic maps from multibeam acoustic backscatter angular response data. Computers & Geosciences, 2012, 41: 181- 187.
https://doi.org/10.1016/j.cageo.2011.09.001
Cited in this article [1]
Sánchez-Carnero N , Rodríguez-Pérez D , Llorens S , et al. An expeditious low-cost method for the acoustic characterization of seabeds in a Mediterranean coastal protected area. Estuarine, Coastal and Shelf Science, 2023, 281: 108204
https://doi.org/10.1016/j.ecss.2022.108204
Cited in this article [1]
Simons D G , Snellen M . A Bayesian approach to seafloor classification using multi-beam echo-sounder backscatter data. Applied Acoustics, 2009, 70 (10): 1258- 1268.
Cited in this article [3]
Snellen M , Gaida T C , Koop L , et al. Performance of multibeam echosounder backscatter-based classification for monitoring sediment distributions using multitemporal large-scale ocean data sets. IEEE Journal of Oceanic Engineering, 2019, 44 (1): 142- 155.
Cited in this article [1]
Tang Q H , Zhou X H , Ding J S , et al. Study on processing of multibeam backscatter data. Acta Oceanologica Sinica, 2006, 28 (2): 51- 55.
https://doi.org/10.3321/j.issn:0253-4193.2006.02.007
Titterington D M , Smith A F M , Makov H E . Statistical analysis of finite mixture distributions. Biometrics, 1987, 150 (3): 283
https://doi.org/10.2307/2981482
Cited in this article [1]
Urick R J . Principles of Underwater Sound. 3rd ed New York: McGraw-Hill Book Company, 1983
Cited in this article [1]
Xu C. 2014. Study on seabed classification technology based on multibeam bathymetry sonar[Ph. D. thesis](in Chinese). Harbin: Harbin Engineering University.
Xu C, Li H S, Wang C, el al. 2014. Seabed classification of multibeam seabed acoustic image based on composite kernel SVM. Progress in Geophysics (in Chinese), 29(5): 2437-2442, doi: 10.6038pg20140567.
Yan J , Meng J X , Zhao J H . Real-time bottom tracking using side scan sonar data through one-dimensional convolutional neural networks. Remote Sensing, 2020, 12 (1): 37
https://doi.org/10.3390/rs12010037
Cited in this article [1]
Zhang Z J, McConnell D R. 2010. Backscatter characterization of seep-associated seafloor features in the vicinity of bush hill, northwest green canyon, Gulf of Mexico. //Proceedings of the Offshore Technology Conference. Houston, doi: 10.4043/20662-MS.
Cited in this article [1]
Zhao L , Cai G Q , Zhong H X , et al. Grain size distribution of surface sediments in the area off southeast Hainan Island and its implications for environmental interpretation. Marine Geology & Quaternary Geology, 2021, 41 (2): 64- 74.
https://doi.org/10.16562/j.cnki.0256-1492.2020051502
金 绍华 , 肖 付民 , 边 刚 , 等. 利用多波束反向散射强度角度响应曲线的底质特征参数提取算法. 武汉大学学报·信息科学版, 2014, 39 (12): 1493- 1498.
Cited in this article [1]
金 翔龙 . 海洋地球物理研究与海底探测声学技术的发展. 地球物理学进展, 2007, 22 (4): 1243- 1249.
https://doi.org/10.3969/j.issn.1004-2903.2007.04.034
Cited in this article [1]
罗 进华 , 冯 湘子 . 基于多波束反向散射统计分布的海底底质分类. 地球物理学进展, 2025, 40 (2): 0798- 0805.
https://doi.org/10.6038/pg2025HH0552
Cited in this article [5]
唐 秋华 , 周 兴华 , 丁 继胜 , 等. 多波束反向散射强度数据处理研究. 海洋学报(中文版), 2006, 28 (2): 51- 55.
https://doi.org/10.3321/j.issn:0253-4193.2006.02.007
Cited in this article [1]
徐超. 2014. 多波束测深声呐海底底质分类技术研究[硕士论文]. 哈尔滨: 哈尔滨工程大学.
Cited in this article [8]
徐 超 , 李 海森 , 王 川 , 等. 基于合成核SVM的多波束海底声图像底质分类研究. 地球物理学进展, 2014, 29 (5): 2437- 2442.
https://doi.org/10.6038/pg20140567
赵 利 , 蔡 观强 , 钟 和贤 , 等. 海南岛东南浅海表层沉积物粒度特征及沉积环境. 海洋地质与第四纪地质, 2021, 41 (2): 64- 74.
https://doi.org/10.16562/j.cnki.0256-1492.2020051502
Cited in this article [2]

感谢审稿专家提出的修改意见和编辑部的大力支持！

RIGHTS & PERMISSIONS

Copyright ©2026 Progress in Geophysics. All rights reserved.
PDF(3363 KB)

Accesses

Citation

Detail
Sections
Recommended

/

〈 〉