基于多波束背向散射强度信号的海底表层沉积物粒度分类研究——以澳洲Joseph Bonaparte湾为例
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摘要
近海海底地形探测与沉积物精确分类对涉海工程建设、生物栖息地反演以及海底资源勘查与开发具有重要的现实意义。以澳洲Joseph Bonaparte湾为例,利用多波束测深技术获取了该海湾约880 km~2水域的水深数据与背向散射强度信号,结合同步采集的54个海底表层沉积物样品,通过随机决策树模型对该海域海底表层沉积物进行了分类研究。结果表明:(1)利用随机决策树模型分析该海域沉积物类型与背向散射强度的关系时,当模型内部参数设置:树的总数为200,最小分裂节点为2,每棵树的最大分裂级数为5时,可提高预测准确率;(2)该参数设置下,利用13°和37°入射角的背向散射强度预测该海域沉积物类型时,准确率最高,其值为83.3%,且在研究海域,砂质砾和砾质砂分布在背向散射强度较强的深槽或海沟等地区,而砾质泥质砂和含砾泥质砂主要分布在背向散射强度较弱的浅水海域。分析还发现,当水深数据作为预测海底表层沉积物类型的特征变量时,有可能降低最终预测结果的准确率。
The accurate information of subaqueous topography and seabed substrata are of great significant for marine engineering construction, benthic habitat mapping, and management of marine protected areas(MPAs). The bathymetric and backscatter data of 880 km~(2 )in the Joseph Bonaparte Gulf, Northern Australia were collected by using a multi-beam echo-sounder system(Kongsberg's 300 kHz EM3002), and 54 samples of seabed sediments were collected simultaneously. The Random Forest Decision Tree(RFDT) was chosen as the modelling method for prediction. The results show that:(1) Improvement of the predicted accuracy for bed sediment classification is made when the parameters of RDFT are set as "number of trees" 200, "minimum size node to split" 2 and the "maximum splitting levels" 5 in this paper.(2) The highest accuracy of 83.3% is predicted from the incidence angle(backscatter) of 13° and 37°, and the coarse sediment, such as sandy gravel and gravelly sand are mainly distributed in the area with stronger backscatter intensity, but the fine sediment, such as gravelly muddy sand and(gravelly) muddy sand are distributed in the shallow area. However, it is noteworthy that the predicted accuracy of sediment classification may decrease when bathymetry data is chosen as the characteristic variable with the backscatter.
The accurate information of subaqueous topography and seabed substrata are of great significant for marine engineering construction, benthic habitat mapping, and management of marine protected areas(MPAs). The bathymetric and backscatter data of 880 km~(2 )in the Joseph Bonaparte Gulf, Northern Australia were collected by using a multi-beam echo-sounder system(Kongsberg's 300 kHz EM3002), and 54 samples of seabed sediments were collected simultaneously. The Random Forest Decision Tree(RFDT) was chosen as the modelling method for prediction. The results show that:(1) Improvement of the predicted accuracy for bed sediment classification is made when the parameters of RDFT are set as "number of trees" 200, "minimum size node to split" 2 and the "maximum splitting levels" 5 in this paper.(2) The highest accuracy of 83.3% is predicted from the incidence angle(backscatter) of 13° and 37°, and the coarse sediment, such as sandy gravel and gravelly sand are mainly distributed in the area with stronger backscatter intensity, but the fine sediment, such as gravelly muddy sand and(gravelly) muddy sand are distributed in the shallow area. However, it is noteworthy that the predicted accuracy of sediment classification may decrease when bathymetry data is chosen as the characteristic variable with the backscatter.
引文
[1] Lanier A Romsos C, Goldfinger C. Seafloor habitat mapping on the oregon continental margin: a spatially nested GIS approach to mapping scale, mapping methods, and accuracy quantification[J]. Marine Geodesy, 2007, 30(1/2): 51-76.
[2] McGonigle C, Brown C, Quinn R, et al. Evaluation of image-based multibeam sonar backscatter classification for benthic habitat discrimination and mapping at Stanton Banks, UK[J]. Estuarine, Coastal and Shelf Science, 2009, 81(3): 423-437.
[3] Brown C J, Smith S J, Lawton P, et al. Benthic habitat mapping: a review of progress towards improved understanding of the spatial ecology of the seafloor using acoustic techniques[J]. Estuarine, Coastal and Shelf Science, 2011, 92(3): 502-520.
[4] McGonigle C, Grabowski J H, Brown C J, et al. Detection of deep water benthic macroalgae using image-based classification techniques on multibeam backscatter at Cashes Ledge, Gulf of Maine, USA[J]. Estuarine, Coastal and Shelf Science, 2011, 91(1): 87-101.
[5] 陈吉余, 恽才兴, 徐海根, 等. 两千年来长江河口发育的模式[J]. 海洋学报, 1979, 1(1): 103-111. Chen Jiyu, Yui Caixing, Xu Haigen, et al. The developmental model of the Chang jiang River Estuary during last 2000 years[J]. Haiyang Xuebao, 1979, 1(1): 103-111.
[6] 陶春辉, 金翔龙, 许枫, 等. 海底声学底质分类技术的研究现状与前景[J]. 东海海洋, 2004, 22(3): 28-33. Tao Chunhui, Jin Xianglong, Xu Feng, et al. The prospect of seabed classification technology[J]. Donghai Marine Science, 2004, 22(3): 28-33.
[7] Hamilton E L, Shumway G, Menard H W, et al. Acoustic and other physical properties of shallow-water sediments off San Diego[J]. The Journal of the Acoustical Society of America, 1956, 28(1), doi: 10.1121/1.1908210.
[8] Hamilton E L. Low sound velocities in high-porosity sediments[J]. The Journal of the Acoustical Society of America, 1956, 28(1): 16.
[9] Hamilton E L. Geoacoustic modeling of the sea floor[J]. The Journal of the Acoustical Society of America, 1980, 68(5): 1313-1340.
[10] Schock S G, LeBlanc L R, Mayer L A. Chirp subbottom profiler for quantitative sediment analysis[J]. Geophysics, 1989, 54(4): 445-450.
[11] LeBlanc L R, Mayer L, Rufino M, et al. Marine sediment classification using the chirp sonar[J]. The Journal of the Acoustical Society of America, 1992, 91(1): 107-115.
[12] Panda S, LeBlanc L R, Schock S G. Sediment classification based on impedance and attenuation estimation[J]. The Journal of the Acoustical Society of America, 1994, 96(5): 3022-3035.
[13] Milligan S D, LeBlanc L R, Middleton F H. Statistical grouping of acoustic reflection profiles[J]. The Journal of the Acoustical Society of America, 1978, 64(3): 795-807.
[14] 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. 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]. Haiyang Xuebao, 2017, 39(9): 51-57.
[15] 刘胜旋, 关永贤. 介绍几种典型的海底底质分类技术[J]. 海洋地质, 2003(4): 31-38. Liu Shengxuan, Guan Yongxian. Several typical classification techniques of seabed sediments[J]. Marine Geology, 2003(4): 31-38.
[16] Hewitt A, Salisbury R, Wilson J. Using multibeam echosounder backscatter to characterize seafloor features[J]. Sea Technology, 2010, 51(9): 10-13.
[17] Micallef A, Le Bas T P, Huvenne V A I, et al. A multi-method approach for benthic habitat mapping of shallow coastal areas with high-resolution multibeam data[J]. Continental Shelf Research, 2012(39/40): 14-26.
[18] Cochrane G R, Lafferty K D. Use of acoustic classification of sidescan sonar data for mapping benthic habitat in the Northern Channel Islands, California[J]. Continental Shelf Research, 2002, 22(5): 683-690.
[19] Lathrop R G, Cole M, Senyk N, et al. Seafloor habitat mapping of the New York Bight incorporating sidescan sonar data[J]. Estuarine, Coastal and Shelf Science, 2006, 68(1/2): 221-230.
[20] 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.
[21] Lucieer V L. Object-oriented classification of sidescan sonar data for mapping benthic marine habitats[J]. International Journal of Remote Sensing, 2008, 29(3): 905-921.
[22] Preston J. Automated acoustic seabed classification of multibeam images of Stanton Banks[J]. Applied Acoustics, 2009, 70(10): 1277-1287.
[23] Hamilton L J, Parnum I. Acoustic seabed segmentation from direct statistical clustering of entire multibeam sonar backscatter curves[J]. Continental Shelf Research, 2011, 31(2): 138-148.
[24] 李庆武, 石丹, 霍冠英. 基于Contourlet变换的海底声呐图像特征提取与分类[J]. 海洋学报, 2011, 33(5): 163-168. Li Qingwu, Shi Dan, Huo Guanying. Feature extraction and classification of seabed sonar images based on Contourlet transform[J]. Haiyang Xuebao, 2011, 33(5): 163-168.
[25] 唐秋华, 李杰, 周兴华, 等. 济州岛南部海域海底声呐图像分析与声学底质分类[J]. 海洋学报, 2014, 36(7): 133-141. Tang Qiuhua, Li Jie, Zhou Xinghua, et al. Seabed sonar image analysis and acoustic seabed classification in the south of the Cheju Island[J]. Haiyang Xuebao, 2014, 36(7): 133-141.
[26] 熊明宽, 吴自银, 李守军, 等. 基于遗传小波神经网络的海底声学底质识别分类[J]. 海洋学报, 2014, 36(5): 90-97. Xiong Mingkuan, Wu Ziyin, Li Shoujun, et al. Wavelet neural network identification and classification of sediment seabed sonar images based on genetic algorithms[J]. Haiyang Xuebao, 2014, 36(5): 90-97.
[27] Breiman L. Random forests[J]. Machine Learning, 2001, 45(1): 5-32.
[28] Huang Zhi, Siwabessy J, Nichol S, et al. Predictive mapping of seabed cover types using angular response curves of multibeam backscatter data: testing different feature analysis approaches[J]. Continental Shelf Research, 2013(61/62): 12-22.
[29] Huang Zhi, Siwabessy J, Nichol S L, et al. Predictive mapping of seabed substrata using high-resolution multibeam sonar data: a case study from a shelf with complex geomorphology[J]. Marine Geology, 2014, 357: 37-52.
[30] Lees B G. Recent terrigenous sedimentation in Joseph Bonaparte Gulf, Northwestern Australia[J]. Marine Geology, 1992, 103(1/3): 199-213.
[31] Heap A D, Przeslawski R, Radke L, et al. Seabed environments of the eastern Joseph Bonaparte Gulf, northern Australia[R]. Canberra, ACT, Australia: Geoscience Australia, 2010.
[32] Przeslawski R, Daniell J, Nichol S, et al. Seabed habitats and hazards of the Joseph Bonaparte Gulf and Timor Sea, Northern Australia[M]. Canberra, ACT, Australia: Geoscience Australia, 2011.
[33] 赵建虎, 刘经南. 多波束测深及图像数据处理[M]. 武汉: 武汉大学出版社, 2008. Zhao Jianhu, Liu Jingnan. Multi-beam Sounding and the Process of Image Data[M]. Wuhan: Wuhan University Press, 2008.
[34] 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[J]. 海洋学报, 2006, 28(2): 51-55. Tang Qiuhua, Zhou Xinghua, Ding Jisheng, et al. Study on processing of multibeam backscatter data[J]. Haiyang Xuebao, 2006, 28(2): 51-55.
[35] Folk R L. Petrology of Sedimentary Rocks[M]. Hemphill, Austin: Hemphill Publishing Company, 1968.
[36] Dartnell P, Gardner J V. Predicting seafloor facies from multibeam bathymetry and backscatter data[J]. Photogrammetric Engineering & Remote Sensing, 2004, 70(9): 1081-1091.
[37] Huang Zhi, Nichol S L, Siwabessy J P W, et al. Predictive modelling of seabed sediment parameters using multibeam acoustic data: a case study on the Carnarvon Shelf, Western Australia[J]. International Journal of Geographical Information Science, 2012, 26(2): 283-307.
[38] Mitchell N C, Clarke J E H. Classification of seafloor geology using multibeam sonar data from the Scotian Shelf[J]. Marine Geology, 1994, 121(3/4): 143-160.
[39] Clarke J H. Toward remote seafloor classification using the angular response of acoustic backscattering: a case study from multiple overlapping GLORIA data[J]. IEEE Journal of Oceanic Engineering, 1994, 19(1): 112-127.
[40] De Moustier C, Matsumoto H. Seafloor acoustic remote sensing with multibeam echo-sounders and bathymetric sidescan sonar systems[J]. Marine Geophysical Researches, 1993, 15(1): 27-42.
[41] Goff J A, Orange D L, Mayer L A, et al. Detailed investigation of continental shelf morphology using a high-resolution swath sonar survey: the Eel margin, northern California[J]. Marine Geology, 1999, 154(1/4): 255-269.
[42] Lundblad E R, Wright D J, Miller J, et al. A benthic terrain classification scheme for American Samoa[J]. Marine Geodesy, 2006, 29(2): 89-111.
[43] Wilson M F J, O’Connell B, Brown C, et al. Multiscale terrain analysis of multibeam bathymetry data for habitat mapping on the continental slope[J]. Marine Geodesy, 2007, 30(1/2): 3-35.
[44] Zheng Shuwei, Cheng Heqin, Wu Shuaihu, et al. Discovery and implications of catenary-bead subaqueous dunes[J]. Science China Earth Sciences, 2016, 59(3): 495-502.
[45] Jackson D R, Winebrenner D P, Ishimaru A. Application of the composite roughness model to high-frequency bottom backscattering[J]. The Journal of the Acoustical Society of America, 1986, 79(5): 1410-1422.
[46] Kloser R J, Bax N J, Ryan T, et al. Remote sensing of seabed types in the Australian South East Fishery; development and application of normal incident acoustic techniques and associated ‘ground truthing’[J]. Marine and Freshwater Research, 2001, 52(4): 475-489.
[47] Ferrini V L, Flood R D. The effects of fine-scale surface roughness and grain size on 300 kHz multibeam backscatter intensity in sandy marine sedimentary environments[J]. Marine Geology, 2006, 228(1/4): 153-172.
[48] Lurton X, Lamarche G, Brown C, et al. Backscatter measurements by seafloor-mapping sonars guidelines and recommendations[R]. GeoHab Backscatter Working Group, 2015: 1-200.
[49] De Falco G, Tonielli R, Di Martino G, et al. Relationships between multibeam backscatter, sediment grain size and Posidonia oceanica seagrass distribution[J]. Continental Shelf Research, 2010, 30(18): 1941-1950.
[2] McGonigle C, Brown C, Quinn R, et al. Evaluation of image-based multibeam sonar backscatter classification for benthic habitat discrimination and mapping at Stanton Banks, UK[J]. Estuarine, Coastal and Shelf Science, 2009, 81(3): 423-437.
[3] Brown C J, Smith S J, Lawton P, et al. Benthic habitat mapping: a review of progress towards improved understanding of the spatial ecology of the seafloor using acoustic techniques[J]. Estuarine, Coastal and Shelf Science, 2011, 92(3): 502-520.
[4] McGonigle C, Grabowski J H, Brown C J, et al. Detection of deep water benthic macroalgae using image-based classification techniques on multibeam backscatter at Cashes Ledge, Gulf of Maine, USA[J]. Estuarine, Coastal and Shelf Science, 2011, 91(1): 87-101.
[5] 陈吉余, 恽才兴, 徐海根, 等. 两千年来长江河口发育的模式[J]. 海洋学报, 1979, 1(1): 103-111. Chen Jiyu, Yui Caixing, Xu Haigen, et al. The developmental model of the Chang jiang River Estuary during last 2000 years[J]. Haiyang Xuebao, 1979, 1(1): 103-111.
[6] 陶春辉, 金翔龙, 许枫, 等. 海底声学底质分类技术的研究现状与前景[J]. 东海海洋, 2004, 22(3): 28-33. Tao Chunhui, Jin Xianglong, Xu Feng, et al. The prospect of seabed classification technology[J]. Donghai Marine Science, 2004, 22(3): 28-33.
[7] Hamilton E L, Shumway G, Menard H W, et al. Acoustic and other physical properties of shallow-water sediments off San Diego[J]. The Journal of the Acoustical Society of America, 1956, 28(1), doi: 10.1121/1.1908210.
[8] Hamilton E L. Low sound velocities in high-porosity sediments[J]. The Journal of the Acoustical Society of America, 1956, 28(1): 16.
[9] Hamilton E L. Geoacoustic modeling of the sea floor[J]. The Journal of the Acoustical Society of America, 1980, 68(5): 1313-1340.
[10] Schock S G, LeBlanc L R, Mayer L A. Chirp subbottom profiler for quantitative sediment analysis[J]. Geophysics, 1989, 54(4): 445-450.
[11] LeBlanc L R, Mayer L, Rufino M, et al. Marine sediment classification using the chirp sonar[J]. The Journal of the Acoustical Society of America, 1992, 91(1): 107-115.
[12] Panda S, LeBlanc L R, Schock S G. Sediment classification based on impedance and attenuation estimation[J]. The Journal of the Acoustical Society of America, 1994, 96(5): 3022-3035.
[13] Milligan S D, LeBlanc L R, Middleton F H. Statistical grouping of acoustic reflection profiles[J]. The Journal of the Acoustical Society of America, 1978, 64(3): 795-807.
[14] 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. 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]. Haiyang Xuebao, 2017, 39(9): 51-57.
[15] 刘胜旋, 关永贤. 介绍几种典型的海底底质分类技术[J]. 海洋地质, 2003(4): 31-38. Liu Shengxuan, Guan Yongxian. Several typical classification techniques of seabed sediments[J]. Marine Geology, 2003(4): 31-38.
[16] Hewitt A, Salisbury R, Wilson J. Using multibeam echosounder backscatter to characterize seafloor features[J]. Sea Technology, 2010, 51(9): 10-13.
[17] Micallef A, Le Bas T P, Huvenne V A I, et al. A multi-method approach for benthic habitat mapping of shallow coastal areas with high-resolution multibeam data[J]. Continental Shelf Research, 2012(39/40): 14-26.
[18] Cochrane G R, Lafferty K D. Use of acoustic classification of sidescan sonar data for mapping benthic habitat in the Northern Channel Islands, California[J]. Continental Shelf Research, 2002, 22(5): 683-690.
[19] Lathrop R G, Cole M, Senyk N, et al. Seafloor habitat mapping of the New York Bight incorporating sidescan sonar data[J]. Estuarine, Coastal and Shelf Science, 2006, 68(1/2): 221-230.
[20] 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.
[21] Lucieer V L. Object-oriented classification of sidescan sonar data for mapping benthic marine habitats[J]. International Journal of Remote Sensing, 2008, 29(3): 905-921.
[22] Preston J. Automated acoustic seabed classification of multibeam images of Stanton Banks[J]. Applied Acoustics, 2009, 70(10): 1277-1287.
[23] Hamilton L J, Parnum I. Acoustic seabed segmentation from direct statistical clustering of entire multibeam sonar backscatter curves[J]. Continental Shelf Research, 2011, 31(2): 138-148.
[24] 李庆武, 石丹, 霍冠英. 基于Contourlet变换的海底声呐图像特征提取与分类[J]. 海洋学报, 2011, 33(5): 163-168. Li Qingwu, Shi Dan, Huo Guanying. Feature extraction and classification of seabed sonar images based on Contourlet transform[J]. Haiyang Xuebao, 2011, 33(5): 163-168.
[25] 唐秋华, 李杰, 周兴华, 等. 济州岛南部海域海底声呐图像分析与声学底质分类[J]. 海洋学报, 2014, 36(7): 133-141. Tang Qiuhua, Li Jie, Zhou Xinghua, et al. Seabed sonar image analysis and acoustic seabed classification in the south of the Cheju Island[J]. Haiyang Xuebao, 2014, 36(7): 133-141.
[26] 熊明宽, 吴自银, 李守军, 等. 基于遗传小波神经网络的海底声学底质识别分类[J]. 海洋学报, 2014, 36(5): 90-97. Xiong Mingkuan, Wu Ziyin, Li Shoujun, et al. Wavelet neural network identification and classification of sediment seabed sonar images based on genetic algorithms[J]. Haiyang Xuebao, 2014, 36(5): 90-97.
[27] Breiman L. Random forests[J]. Machine Learning, 2001, 45(1): 5-32.
[28] Huang Zhi, Siwabessy J, Nichol S, et al. Predictive mapping of seabed cover types using angular response curves of multibeam backscatter data: testing different feature analysis approaches[J]. Continental Shelf Research, 2013(61/62): 12-22.
[29] Huang Zhi, Siwabessy J, Nichol S L, et al. Predictive mapping of seabed substrata using high-resolution multibeam sonar data: a case study from a shelf with complex geomorphology[J]. Marine Geology, 2014, 357: 37-52.
[30] Lees B G. Recent terrigenous sedimentation in Joseph Bonaparte Gulf, Northwestern Australia[J]. Marine Geology, 1992, 103(1/3): 199-213.
[31] Heap A D, Przeslawski R, Radke L, et al. Seabed environments of the eastern Joseph Bonaparte Gulf, northern Australia[R]. Canberra, ACT, Australia: Geoscience Australia, 2010.
[32] Przeslawski R, Daniell J, Nichol S, et al. Seabed habitats and hazards of the Joseph Bonaparte Gulf and Timor Sea, Northern Australia[M]. Canberra, ACT, Australia: Geoscience Australia, 2011.
[33] 赵建虎, 刘经南. 多波束测深及图像数据处理[M]. 武汉: 武汉大学出版社, 2008. Zhao Jianhu, Liu Jingnan. Multi-beam Sounding and the Process of Image Data[M]. Wuhan: Wuhan University Press, 2008.
[34] 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[J]. 海洋学报, 2006, 28(2): 51-55. Tang Qiuhua, Zhou Xinghua, Ding Jisheng, et al. Study on processing of multibeam backscatter data[J]. Haiyang Xuebao, 2006, 28(2): 51-55.
[35] Folk R L. Petrology of Sedimentary Rocks[M]. Hemphill, Austin: Hemphill Publishing Company, 1968.
[36] Dartnell P, Gardner J V. Predicting seafloor facies from multibeam bathymetry and backscatter data[J]. Photogrammetric Engineering & Remote Sensing, 2004, 70(9): 1081-1091.
[37] Huang Zhi, Nichol S L, Siwabessy J P W, et al. Predictive modelling of seabed sediment parameters using multibeam acoustic data: a case study on the Carnarvon Shelf, Western Australia[J]. International Journal of Geographical Information Science, 2012, 26(2): 283-307.
[38] Mitchell N C, Clarke J E H. Classification of seafloor geology using multibeam sonar data from the Scotian Shelf[J]. Marine Geology, 1994, 121(3/4): 143-160.
[39] Clarke J H. Toward remote seafloor classification using the angular response of acoustic backscattering: a case study from multiple overlapping GLORIA data[J]. IEEE Journal of Oceanic Engineering, 1994, 19(1): 112-127.
[40] De Moustier C, Matsumoto H. Seafloor acoustic remote sensing with multibeam echo-sounders and bathymetric sidescan sonar systems[J]. Marine Geophysical Researches, 1993, 15(1): 27-42.
[41] Goff J A, Orange D L, Mayer L A, et al. Detailed investigation of continental shelf morphology using a high-resolution swath sonar survey: the Eel margin, northern California[J]. Marine Geology, 1999, 154(1/4): 255-269.
[42] Lundblad E R, Wright D J, Miller J, et al. A benthic terrain classification scheme for American Samoa[J]. Marine Geodesy, 2006, 29(2): 89-111.
[43] Wilson M F J, O’Connell B, Brown C, et al. Multiscale terrain analysis of multibeam bathymetry data for habitat mapping on the continental slope[J]. Marine Geodesy, 2007, 30(1/2): 3-35.
[44] Zheng Shuwei, Cheng Heqin, Wu Shuaihu, et al. Discovery and implications of catenary-bead subaqueous dunes[J]. Science China Earth Sciences, 2016, 59(3): 495-502.
[45] Jackson D R, Winebrenner D P, Ishimaru A. Application of the composite roughness model to high-frequency bottom backscattering[J]. The Journal of the Acoustical Society of America, 1986, 79(5): 1410-1422.
[46] Kloser R J, Bax N J, Ryan T, et al. Remote sensing of seabed types in the Australian South East Fishery; development and application of normal incident acoustic techniques and associated ‘ground truthing’[J]. Marine and Freshwater Research, 2001, 52(4): 475-489.
[47] Ferrini V L, Flood R D. The effects of fine-scale surface roughness and grain size on 300 kHz multibeam backscatter intensity in sandy marine sedimentary environments[J]. Marine Geology, 2006, 228(1/4): 153-172.
[48] Lurton X, Lamarche G, Brown C, et al. Backscatter measurements by seafloor-mapping sonars guidelines and recommendations[R]. GeoHab Backscatter Working Group, 2015: 1-200.
[49] De Falco G, Tonielli R, Di Martino G, et al. Relationships between multibeam backscatter, sediment grain size and Posidonia oceanica seagrass distribution[J]. Continental Shelf Research, 2010, 30(18): 1941-1950.