Classification of sediment lithology at the Arctic mid-ocean ridges using multibeam water column bottom echo intensity information

Cui Xiaodong; Zhang Feihu; Zhang Tao; Yang Fanlin; Wan Jiaxin; Ji Xue; Li Jiabiao

doi:10.12284/hyxb2025075

Volume 47 Issue 8

Aug. 2025

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Cui Xiaodong，Zhang Feihu，Zhang Tao, et al. Classification of sediment lithology at the Arctic mid-ocean ridges using multibeam water column bottom echo intensity information[J]. Haiyang Xuebao，2025, 47(8)：116–128 doi: 10.12284/hyxb2025075

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Classification of sediment lithology at the Arctic mid-ocean ridges using multibeam water column bottom echo intensity information

doi: 10.12284/hyxb2025075

Cui Xiaodong^{1, 4
,},
Zhang Feihu¹,
Zhang Tao^{2, 3
,
,},
Yang Fanlin¹,
Wan Jiaxin⁵,
Ji Xue⁶,
Li Jiabiao³

1.
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
2.
College of Earth Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
3.
Second Institute of Oceanography, Ministry of Naturel Resources, Hangzhou 310012, China
4.
Ocean Acoustic Technology Laboratory, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
5.
Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, China
6.
College of Geoexploration Science and Technology, Jilin University, Changchun 130026, China

Received Date: 2025-03-25
Rev Recd Date: 2025-05-29

Available Online: 2025-07-01

Publish Date: 2025-08-31

Abstract

Abstract

As a core component of benthic habitat mapping, the detection and classification of deep-sea sediment provides basic information for deep-sea resource exploration and ecological protection. However, due to the limitation of the resolution of deep-sea acoustic observation, the traditional method of sediment classification based on multibeam bathymetry and backscatter intensity information suffers from the difficulty of interpretation and low confidence caused by the mixed sediment on the seafloor. For this reason, this paper innovatively applies multibeam water column data to deep-sea bottom classification, and proposes a mixed bottom classification method based on the multidimensional waveform characteristics of the bottom echo sequence. Firstly, the multidimensional bottom echo waveform features are extracted with the help of the sequence echo information of the interaction between the water body and the seafloor; secondly, a decision fusion classification model under the constraints of water column bottom echo abundance interpretation is constructed by taking into account the mixing of sediments within the intrinsic observational resolution; lastly, the experiments are carried out by using the Arctic shipborne multibeam data for the classification and abundance estimation of the three kinds of sediments, including the sheet basalt, the basalt breccia, and the volcanic glass, the overall accuracy and Kappa coefficient reached 92.46% and 0.89, which are increased by 11.05% and 0.21 respectively compared with the traditional sonar image classification method, providing a new strategy for spatial prediction mapping of deep seabed benthic environment.
- water column bottom echo,
- feature extraction,
- classification of sediment,
- abundance,
- decision fusion

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References

[1]	Gage J D, Tyler P A. Deep-Sea Biology: A Natural History of Organisms at the Deep-Sea Floor[M]. Cambridge: Cambridge University Press, 1991.
[2]	Danovaro R, Gambi C, Dell' Anno A, et al. Exponential decline of deep-sea ecosystem functioning linked to benthic biodiversity loss[J]. Current Biology, 2008, 18(1): 1−8. doi: 10.1016/j.cub.2007.11.056
[3]	Thurber A R, Sweetman A K, Narayanaswamy B E, et al. Ecosystem function and services provided by the deep sea[J]. Biogeosciences, 2014, 11(14): 3941−3963. doi: 10.5194/bg-11-3941-2014
[4]	赵玉新, 赵廷. 海底声呐图像智能底质分类技术研究综述[J]. 智能系统学报, 2020, 15(3): 587−600. doi: 10.11992/tis.202004026 Zhao Yuxin, Zhao Ting. Survey of the intelligent seabed sediment classification technology based on sonar images[J]. CAAI Transactions on Intelligent Systems, 2020, 15(3): 587−600. doi: 10.11992/tis.202004026
[5]	Preston J. Automated acoustic seabed classification of multibeam images of Stanton Banks[J]. Applied Acoustics, 2009, 70(10): 1277−1287. doi: 10.1016/j.apacoust.2008.07.011
[6]	Wang Mingwei, Wu Ziyin, Yang Fanlin, et al. Multifeature extraction and seafloor classification combining LiDAR and MBES data around Yuanzhi Island in the South China Sea[J]. Sensors, 2018, 18(11): 3828. doi: 10.3390/s18113828
[7]	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[J]. Estuarine, Coastal and Shelf Science, 2013, 117: 94−106. doi: 10.1016/j.ecss.2012.11.001
[8]	Lacharité M, Brown C J, Gazzola V. Multisource multibeam backscatter data: developing a strategy for the production of benthic habitat maps using semi-automated seafloor classification methods[J]. Marine Geophysical Research, 2018, 39(1): 307−322.
[9]	Cui Xiaodong, Yang Fanlin, Wu Ziyin, et al. Deep-sea sediment mixed pixel decomposition based on multibeam backscatter intensity segmentation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 1−15.
[10]	Zhang Kai, Li Qianqian, Zhu Hongchun, et al. Acoustic deep-sea seafloor characterization accounting for heterogeneity effect[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(5): 3034−3042. doi: 10.1109/TGRS.2019.2946986
[11]	Liu Hongxia, Yang Fanlin, Zheng Shuangqiang, et al. A method of sidelobe effect suppression for multibeam water column images based on an adaptive soft threshold[J]. Applied Acoustics, 2019, 148: 467−475. doi: 10.1016/j.apacoust.2019.01.006
[12]	Mei Sai, Yang Huiliang, Sun Zhilei, et al. Acoustic detecting technology based on multibeam water column imaging and its application to cold seep plume[J]. Marine Geology & Quaternary Geology, 2021, 41(4): 222−231.
[13]	张万远, 王雪斌, 周天, 等. 基于多波束测深声呐的水中气体目标检测方法[J]. 哈尔滨工程大学学报, 2020, 41(8): 1143−1149. doi: 10.11990/jheu.201906009 Zhang Wanyuan, Wang Xuebin, Zhou Tian, et al. Detection of underwater gas targets by using a multi-beam bathymetric sonar[J]. Journal of Harbin Engineering University, 2020, 41(8): 1143−1149. doi: 10.11990/jheu.201906009
[14]	刘洪霞. 多波束声呐水体影像在中底层水域目标探测中的应用[D]. 青岛: 山东科技大学, 2020 Liu Hongxia. The application of multibeam sonar water column image in middle and bottom water target detection[D]. Qingdao: Shandong University of Science and Technology, 2020.
[15]	Anderson J T, Holliday D V, Kloser R, et al. Acoustic seabed classification: current practice and future directions[J]. ICES Journal of Marine Science, 2008, 65(6): 1004−1011. doi: 10.1093/icesjms/fsn061
[16]	Porskamp P, Schimel A C G, Young M, et al. Integrating multibeam echosounder water-column data into benthic habitat mapping[J]. Limnology and Oceanography, 2022, 67(8): 1701−1713. doi: 10.1002/lno.12160
[17]	Zhao Jianhu, Meng Junxia, Zhang Hongmei, et al. Comprehensive detection of gas plumes from multibeam water column images with minimisation of noise interferences[J]. Sensors, 2017, 17(12): 2755. doi: 10.3390/s17122755
[18]	张目宁, 杨鲲, 吴永亭, 等. 用于海底目标识别与底质分类的多波束水体波形预处理[J]. 海洋通报, 2021, 40(1): 44−52, 69. Zhang Muning, Yang Kun, Wu Yongting, et al. Multi-beam water columndata processing method for target recognition and classification of seabed sediments[J]. Marine Science Bulletin, 2021, 40(1): 44−52, 69.
[19]	Diesing M, Green S L, Stephens D, et al. Mapping seabed sediments: comparison of manual, geostatistical, object-based image analysis and machine learning approaches[J]. Continental Shelf Research, 2014, 84: 107−119. doi: 10.1016/j.csr.2014.05.004
[20]	Luo Xiaowen, Qin Xiaoming, Wu Ziyin, et al. Sediment classification of small-size seabed acoustic images using convolutional neural networks[J]. IEEE Access, 2019, 7: 98331−98339. doi: 10.1109/ACCESS.2019.2927366
[21]	Hu Haiyang, Feng Chengkai, Cui Xiaodong, et al. A sample enhancement method based on simple linear iterative clustering superpixel segmentation applied to multibeam seabed classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 1−15.
[22]	Xia Peipei, Zhang Li, Li Fanzhang. Learning similarity with cosine similarity ensemble[J]. Information Sciences, 2015, 307: 39−52. doi: 10.1016/j.ins.2015.02.024
[23]	Clinton N, Yu Le, Gong Peng. Geographic stacking: decision fusion to increase global land cover map accuracy[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 103: 57−65. doi: 10.1016/j.isprsjprs.2015.02.010
[24]	Löw F, Conrad C, Michel U. Decision fusion and non-parametric classifiers for land use mapping using multi-temporal RapidEye data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 108: 191−204. doi: 10.1016/j.isprsjprs.2015.07.001
[25]	尚晓东, 董理, 赵建虎, 等. 空间适应性分割尺度下的海底底质声学图像分类[J]. 兵工学报, 2025, 46(5): 240599. doi: 10.12382/bgxb.2024.0599 Shang Xiaodong, Dong Li, Zhao Jianhu, et al. Classification of seafloor sediments using acoustic image based on spatially adaptive segmentation scales[J]. Acta Armamentarii, 2025, 46(5): 240599. doi: 10.12382/bgxb.2024.0599
[26]	李苍柏, 肖克炎, 李楠, 等. 支持向量机、随机森林和人工神经网络机器学习算法在地球化学异常信息提取中的对比研究[J]. 地球学报, 2020, 41(2): 309−319. doi: 10.3975/cagsb.2020.022501 Li Cangbo, Xiao Keyan, Li Nan, et al. A comparative study of support vector machine, random forest and artificial neural network machine learning algorithms in geochemical anomaly information extraction[J]. Acta Geoscientia Sinica, 2020, 41(2): 309−319. doi: 10.3975/cagsb.2020.022501
[27]	王慧贤, 靳惠佳, 王娇龙, 等. k均值聚类引导的遥感影像多尺度分割优化方法[J]. 测绘学报, 2015, 44(5): 526−532. doi: 10.11947/j.AGCS.2015.20130497 Wang Huixian, Jin Huijia, Wang Jiaolong, et al. Optimization approach for multi-scale segmentation of remotely sensed imagery under k-means clustering guidance[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(5): 526−532. doi: 10.11947/j.AGCS.2015.20130497
[28]	于俊, 陈辉, 朱大明, 等. 基于软硬协作决策的半监督珊瑚礁底质分类方法[J]. 海洋学报, 2023, 45(4): 154−164. Yu Jun, Chen Hui, Zhu Daming, et al. A semi-supervised coral reef substrate classification method based on soft and hard collaborative decision making[J]. Haiyang Xuebao, 2023, 45(4): 154−164.
[29]	宋佰万, 付明生, 崔晓东, 等. 基于多维度声学特征优选的多波束海底底质分类[J]. 海洋通报, 2024, 43(2): 198−209. Song Baiwan, Fu Mingsheng, Cui Xiaodong, et al. Multi-beam benthic environment classification based on multi-dimensional acoustic feature optimization[J]. Marine Science Bulletin, 2024, 43(2): 198−209.
[30]	王嘉翀, 吴自银, 王明伟, 等. 海底声学底质分类的ELM-AdaBoost方法[J]. 海洋学报, 2021, 43(12): 144−151. doi: 10.12284/j.issn.0253-4193.2021.12.hyxb202112014 Wang Jiachong, Wu Ziyin, Wang Mingwei, et al. ELM-AdaBoost method of acoustic seabed sediment classification[J]. Haiyang Xuebao, 2021, 43(12): 144−151. doi: 10.12284/j.issn.0253-4193.2021.12.hyxb202112014