2023 Vol. 43, No. 6
Article Contents

GAO Junfeng, TANG Songhua, ZHANG Shengjiang, JIANG Shenghui, LIU Longlong, WANG Shengmin, LIN Sen, HUANG Yao. Using amplitude properties of shallow seismic profiles to reveal the seabed sediment types: A case study in Zhoushan Islands[J]. Marine Geology & Quaternary Geology, 2023, 43(6): 131-144. doi: 10.16562/j.cnki.0256-1492.2023050401
Citation: GAO Junfeng, TANG Songhua, ZHANG Shengjiang, JIANG Shenghui, LIU Longlong, WANG Shengmin, LIN Sen, HUANG Yao. Using amplitude properties of shallow seismic profiles to reveal the seabed sediment types: A case study in Zhoushan Islands[J]. Marine Geology & Quaternary Geology, 2023, 43(6): 131-144. doi: 10.16562/j.cnki.0256-1492.2023050401

Using amplitude properties of shallow seismic profiles to reveal the seabed sediment types: A case study in Zhoushan Islands

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  • Using acoustic parameters to reveal sediment types is of great significance for ocean research and development. Obtaining sediment types based on limited seabed sampling or in situ testing has often high cost, low efficiency, and poor continuity, to which acoustic profiling provides an advantageous tool that is rapid, continuous, convenient, and economical. Based on the high density and high resolution shallow stratigraphic profile data obtained in Zhoushan Islands periphery, East Chia Sea, technologies of pre-processing, amplitude attribute extraction, and so on were used to decipher the submarine surface sediment types. By comparing the geomorphic types interpreted from side scan sonar data and measured submarine surface sediment types, we found that the RMS (root mean square) attribute of the amplitude on shallow strata profile could accurately reflect the types of seafloor surface sediments. According to the amplitude RMS attribute of 1100 km shallow stratum profile obtained recently, the sediment types of Zhoushan Islands were interpretated, including mainly clay, clay silt, silt, sand, and bedrock. Compared to the measured data, the rate of successful match reached over 72%. This study provided a feasible way using the amplitude attribute of shallow seismic profiling to determine the surface sediment type in the study area and beyond.

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  • [1] Schiermeier Q. Deep-sea sub aims to get to the bottom of a muddy issue[J]. Nature, 2003, 423(6939):469.

    Google Scholar

    [2] 黄泽鹏, 许江, 胡毅, 等. 泉州海域海砂资源开采区选划与管理[J]. 海洋开发与管理, 2021, 38(3):9-13

    Google Scholar

    HUANG Zepeng, XU Jiang, HU Yi, et al. The selecting and management of sea sand resources in coastal areas of Quanzhou[J]. Ocean Development and Management, 2021, 38(3):9-13.

    Google Scholar

    [3] Shang X D, Zhao J H, Zhang H M. Obtaining high-resolution seabed topography and surface details by co-registration of side-scan sonar and multibeam echo sounder images[J]. Remote Sensing, 2019, 11(12):1496. doi: 10.3390/rs11121496

    CrossRef Google Scholar

    [4] Schock S G, Leblanc L R, Mayer L A. Chirp subbottom profiler for quantitative sediment analysis[J]. Geophysics, 1989, 54(4):445-450. doi: 10.1190/1.1442670

    CrossRef Google Scholar

    [5] 孟珊, 房景辉, 蒋增杰, 等. 双齿围沙蚕对潮间带不同类型底质选择行为的研究[J]. 渔业科学进展, 2020, 41(4):110-116

    Google Scholar

    MENG Shan, FANG Jinghui, JIANG Zengjie, et al. Experimental study of the choice behavior of Perinereis aibuhitensis Grube among different sediment types[J]. Progress in Fishery Sciences, 2020, 41(4):110-116.

    Google Scholar

    [6] 陈炜, 邝晗宇, 蔡梦雅, 等. 基于SonarWiz的多波束声呐图像智能底质分类技术研究[J]. 海洋测绘, 2022, 42(1):41-45 doi: 10.3969/j.issn.1671-3044.2022.01.009

    CrossRef Google Scholar

    CHEN Wei, KUANG Hanyu, CAI Mengya, et al. Research on intelligent seabed sediment classification technology of multibeam sonar image based on Sonarwiz[J]. Hydrographic Surveying and Charting, 2022, 42(1):41-45. doi: 10.3969/j.issn.1671-3044.2022.01.009

    CrossRef Google Scholar

    [7] 何林帮. 基于多波束和浅剖的海底浅表层沉积物分类关键问题研究[J]. 测绘学报, 2016, 45(12):1498-1512

    Google Scholar

    HE Linbang. Research on key issues of sediment classification for seabed and sub-bottom based on multi-beam and sub-bottom profile echo intensity[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(12):1498-1512.

    Google Scholar

    [8] 朱正任. 基于多波束海底反向散射强度的底质分类研究[D]. 山东科技大学, 2021

    Google Scholar

    ZHU Zhengren. Research on seabed sediment classification using multibeam backscatter intensity[D]. Shandong University of Science and Technology, 2021.

    Google Scholar

    [9] Fonseca L, Mayer L. Remote estimation of surficial seafloor properties through the application Angular Range Analysis to multibeam sonar data[J]. Marine Geophysical Researches, 2007, 28(2):119-126. doi: 10.1007/s11001-007-9019-4

    CrossRef Google Scholar

    [10] Reed S, Petillot Y, Bell J. An automatic approach to the detection and extraction of mine features in sidescan sonar[J]. IEEE Journal of Oceanic Engineering, 2003, 28(1):90-105.

    Google Scholar

    [11] 唐秋华, 纪雪, 丁继胜, 等. 多波束声学底质分类研究进展与展望[J]. 海洋科学进展, 2019, 37(1):1-10 doi: 10.3969/j.issn.1671-6647.2019.01.001

    CrossRef Google Scholar

    TANG Qiuhua, JI Xue, DING Jisheng, et al. Research progress and prospect of acoustic seabed classification using multibeam echo sounder[J]. Advances in Marine Science, 2019, 37(1):1-10. doi: 10.3969/j.issn.1671-6647.2019.01.001

    CrossRef Google Scholar

    [12] Diesing M, Mitchell P, Stephens D. Image-based seabed classification: what can we learn from terrestrial remote sensing?[J]. ICES Journal of Marine Science, 2016, 73(10):2425-2441. doi: 10.1093/icesjms/fsw118

    CrossRef Google Scholar

    [13] 陶春辉. 海底沉积物声学原位测试和特性研究[D]. 浙江大学博士学位论文, 2005

    Google Scholar

    TAO Chunhui. In situ acoustic experiment and properties study in marine sediments[D]. Doctor Dissertation of Zhejiang University, 2005.

    Google Scholar

    [14] Yan J, Meng J X, Zhao J H. Real-time bottom tracking using side scan sonar data through one-dimensional convolutional neural networks[J]. Remote Sensing, 2020, 12(1):37.

    Google Scholar

    [15] Alevizos E, Snellen M, Simons D, et al. Multi-angle backscatter classification and sub-bottom profiling for improved seafloor characterization[J]. Marine Geophysical Research, 2018, 39(1-2):289-306. doi: 10.1007/s11001-017-9325-4

    CrossRef Google Scholar

    [16] Cukur D, Um I K, Chun J H, et al. A multi-factor approach for process-based seabed characterization: example from the northeastern continental margin of the Korean peninsula (East Sea)[J]. Geo-Marine Letters, 2018, 38(4):323-339. doi: 10.1007/s00367-018-0537-7

    CrossRef Google Scholar

    [17] 黄必桂, 李家钢, 周庆杰, 等. 基于浅地层剖面的海底浅表层沉积物物理性质参数反演技术研究: 以渤海海底管线路由区为例[J]. 海洋学报, 2022, 44(9):156-164

    Google Scholar

    HUANG Bigui, LI Jiagang, ZHOU Qingjie, et al. Research on inversion technology of physical properties parameters of seafloor sediments based on sub-bottom profile: Taking the Bohai sea submarine pipeline route as an example[J]. Acta Oceanologica Sinica, 2022, 44(9):156-164.

    Google Scholar

    [18] 陈静, 吕修亚, 陈亮, 等. 基于Chirp数据反演琼州海峡海底沉积物物性[J]. 热带地理, 2017, 37(6):874-879

    Google Scholar

    CHEN Jing, LV Xiuya, CHEN Liang, et al. Physical properties of the seabed inversed by chirp data in the Qiongzhou strait[J]. Tropical Geography, 2017, 37(6):874-879.

    Google Scholar

    [19] Schock S G. A method for estimating the physical and acoustic properties of the sea bed using chirp sonar data[J]. IEEE Journal of Oceanic Engineering, 2004, 29(4):1200-1217. doi: 10.1109/JOE.2004.841421

    CrossRef Google Scholar

    [20] Schock S G. Remote estimates of physical and acoustic sediment properties in the South China Sea using chirp sonar data and the Biot model[J]. IEEE Journal of Oceanic Engineering, 2004, 29(4):1218-1230. doi: 10.1109/JOE.2004.842253

    CrossRef Google Scholar

    [21] Zheng H B, Yan P, Chen J, et al. Seabed sediment classification in the northern South China Sea using inversion method[J]. Applied Ocean Research, 2013, 39:131-136. doi: 10.1016/j.apor.2012.11.002

    CrossRef Google Scholar

    [22] Yegireddi S, Thomas N. Segmentation and classification of shallow subbottom acoustic data, using image processing and neural networks[J]. Marine Geophysical Research, 2014, 35(2):149-156. doi: 10.1007/s11001-014-9217-9

    CrossRef Google Scholar

    [23] 陈佳兵, 吴自银, 赵荻能, 等. 基于粒子群优化算法的PSO-BP海底声学底质分类方法[J]. 海洋学报, 2017, 39(9):51-57

    Google Scholar

    CHEN Jiabing, WU Ziyin, ZHAO Dineng, et al. Back propagation neural network classification of sediment seabed acoustic sonar images based on particle swarm optimization algorithms[J]. Acta Oceanologica Sinica, 2017, 39(9):51-57.

    Google Scholar

    [24] 孙英, 黄文盛. 浙江海岸的淤涨及其泥沙来源[J]. 东海海洋, 1984(4):34-42

    Google Scholar

    SUN Ying, HUANG Wensheng. The siltation process and silt sources of the Zhejiang coast[J]. Donghai Marine Science, 1984(4):34-42.

    Google Scholar

    [25] 梁小龙, 杨守业, 印萍, 等. 黄海与东海周边河流及泥质区沉积物黏土矿物的分布特征和控制因素[J]. 海洋地质与第四纪地质, 2015, 35(6):1-15 doi: 10.16562/j.cnki.0256-1492.2015.06.001

    CrossRef Google Scholar

    LIANG Xiaolong, YANG Shouye, YIN Ping, et al. Distribution of clay mineral assemblages in the rivers entering Yellow Sea and East China Sea and the muddy shelve deposits and control factors[J]. Marine Geology & Quaternary Geology, 2015, 35(6):1-15. doi: 10.16562/j.cnki.0256-1492.2015.06.001

    CrossRef Google Scholar

    [26] 胡日军, 吴建政, 朱龙海, 等. 东海舟山群岛海域表层沉积物运移特性[J]. 中国海洋大学学报, 2009, 39(3):495-500,442

    Google Scholar

    HU Rijun, WU Jianzheng, ZHU Longhai, et al. Characteristic of surface sediment transport in Zhoushan archipelago sea area, East China Sea[J]. Periodical of Ocean University of China, 2009, 39(3):495-500,442.

    Google Scholar

    [27] 沈昆明, 蒋玉波, 李安龙, 等. 舟山群岛表层沉积物粒度和黏土矿物分布特征与物源指示[J]. 海岸工程, 2020, 39(1):24-33

    Google Scholar

    SHEN Kunming, JIANG Yubo, LI Anlong, et al. Distributions of grain size and clay minerals in the surface sediments of Zhoushan Islands and their material source indication[J]. Coastal Engineering, 2020, 39(1):24-33.

    Google Scholar

    [28] 朱祖扬, 王东, 周建平, 等. 基于非饱和Biot-Stoll模型的海底沉积物介质声频散特性研究[J]. 地球物理学报, 2012, 55(1):180-188 doi: 10.6038/j.issn.0001-5733.2012.01.017

    CrossRef Google Scholar

    ZHU Zuyang, WANG Dong, ZHOU Jianping, et al. Acoustic wave dispersion and attenuation in marine sediment based on partially gas-saturated Biot-Stoll model[J]. Chinese Journal of Geophysics, 2012, 55(1):180-188. doi: 10.6038/j.issn.0001-5733.2012.01.017

    CrossRef Google Scholar

    [29] Blott S J, Pye K. Particle size scales and classification of sediment types based on particle size distributions: Review and recommended procedures[J]. Sedimentology, 2012, 59(7):2071-2096. doi: 10.1111/j.1365-3091.2012.01335.x

    CrossRef Google Scholar

    [30] Kim G Y, Richardson M D, Bibee D L, et al. Sediment types determination using acoustic techniques in the northeastern Gulf of Mexico[J]. Geosciences Journal, 2004, 8(1):95-103. doi: 10.1007/BF02910282

    CrossRef Google Scholar

    [31] Zheng H B, Yan P, Chen J. The discussion of acoustic seabed sediment classification methods[J]. Applied Mechanics and Materials, 2012, 226-228:1811-1816. doi: 10.4028/www.scientific.net/AMM.226-228.1811

    CrossRef Google Scholar

    [32] Herkül K, Peterson A, Paekivi S. Applying multibeam sonar and mathematical modeling for mapping seabed substrate and biota of offshore shallows[J]. Estuarine, Coastal and Shelf Science, 2017, 192:57-71. doi: 10.1016/j.ecss.2017.04.026

    CrossRef Google Scholar

    [33] 唐保根. 东海西部海域现代沉积环境分区及沉积特征的初步研究[J]. 海洋地质与第四纪地质, 1992, 3(4):29-40 doi: 10.16562/j.cnki.0256-1492.1992.04.004

    CrossRef Google Scholar

    TANG Baogen. Preliminary study on the modern sedimentary environment and sedimentary characteristics in the western part of the East China Sea[J]. Marine Geology & Quaternary Geology, 1992, 3(4):29-40. doi: 10.16562/j.cnki.0256-1492.1992.04.004

    CrossRef Google Scholar

    [34] 周庆杰, 李西双, 刘乐军, 等. 基于Chirp数据和Biot-Stoll模型反演南海北部陆坡海底表层沉积物物理性质[J]. 海洋学报, 2020, 42(3):72-82

    Google Scholar

    ZHOU Qingjie, LI Xishuang, LIU Lejun, et al. Physical properties of the seabed inversed based on Chirp data and the Biot-Stoll model in the northern continental slope of the South China Sea[J]. Acta Oceanologica Sinica, 2020, 42(3):72-82.

    Google Scholar

    [35] 李铭珂. 浅地层剖面数据处理与海底底质分类研究[D]. 天津大学硕士学位论文, 2021

    Google Scholar

    LI Mingke. Research on sub-bottom profile data processing and seabed sediment classification[D]. Master Dissertation of Tianjin University, 2021.

    Google Scholar

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