Vertical errors often present in multibeam swath bathymetric data. They are mainly sourced by sound refraction, internal wave disturbance, imperfect tide correction, transducer mounting, long period heave, static draf...Vertical errors often present in multibeam swath bathymetric data. They are mainly sourced by sound refraction, internal wave disturbance, imperfect tide correction, transducer mounting, long period heave, static draft change, dynamic squat and dynamic motion residuals, etc. Although they can be partly removed or reduced by specific algorithms, the synthesized depth biases are unavoidable and sometimes have an important influence on high precise utilization of the final bathymetric data. In order to. confidently identify the decimeter-level changes in seabed morphology by MBES, we must remove or weaken depth biases and improve the precision of multibeam bathymetry further. The fixed-interval profiles that are perpendicular to the vessel track are generated to adjust depth biases between swaths. We present a kind of postprocessing method to minimize the depth biases by the histogram of cumulative depth biases. The datum line in each profile can be obtained by the maximum value of histogram. The corrections of depth biases can be calculated according to the datum line. And then the quality of final bathymetry can be improved by the corrections. The method is verified by a field test.展开更多
With processing and interpretation of 25 000 km full-coverage multibeam swath data fromthe eastern South China Sea, it is found that NE-trending and NW-trending linear morphological features such as scarps, horsts and...With processing and interpretation of 25 000 km full-coverage multibeam swath data fromthe eastern South China Sea, it is found that NE-trending and NW-trending linear morphological features such as scarps, horsts and grabens, govern the central part (14°-17° N) of eastern subbasin. Compared with reflection seismic profiles, these NE-trending linear morpho-structures are considered to be the representation of basement structures on seabed and can be divided into three linear structural zones. The trend of the central zone is NE45°-50° occurring around extinct spreading center, the trend of the second zone is NE70° - 78° on both sides of the central one and the trend of the third zone is about NE60° just on the north of the second one. These three NE-trending linear zones are formed in late-stage NW - SE-trending seafloor spreading of the eastern subbasin along NW-trending linear faults, and respectively correspond to three spreading episodes: 17.0- 19.0 Ma (5d-5e), 19.0 - 21.0 Ma (5e-6a) and 21.0 - 24.2 Ma (6a-6c) based on the contrast of morpho-structures to magnetic lineation anomalies.展开更多
Sediment mapping is a crucial component of environmental science,particularly in the marine environment,where the analysis of seabed sediments is essential for various purposes,including marine resource management,hab...Sediment mapping is a crucial component of environmental science,particularly in the marine environment,where the analysis of seabed sediments is essential for various purposes,including marine resource management,habitat preservation,and infrastructure development.Sediment refers to the solid particles that are transported and deposited in different areas.Multibeamechosounders have revolutionized the field of seabed sediment mapping by providing unparalleled resolution and accuracy in seafloor surveys.This study aimed to produce sediment maps by implementing multi-frequency,e.g.,200,400,550,and 700 kHz multibeam data using a machine learning algorithm,e.g.,Support Vector Machine(SVM)and Random Forest(RF)approach.The study area of this research was located at Waduk Selorejo,Kecamatan Ngantang,Kabupaten Malang,Indonesia.To achieve the aim,bathymetry and backscatter mosaic with multi-frequency results are produced.Then,the second derivatives from bathymetry and backscatter mosaic are produced(slope,ruggedness,aspect,curvature,mean,homogeneity,entropy,correlation,phi,and characterization)were used as the predictors.All the predictors with multi-frequency each have 0.5 m of spatial resolution.To classify,Pearson’s correlation coefficient method is used to identify strong and weak correlations.The weak correlation was used to run the classification by using SVM and RF.The Angular Range Analysis(ARA)characterization is used as the ground truth data.The accuracy assessment of themultiple-frequency sedimentmap will be compared with the two algorithms of machine learning(e.g.,SVM and RF).This study highlights the importance of sediment mapping and the potential of acoustic methods and machine learning algorithms for seafloor classification.展开更多
基金supported by the project of Shandong Provincial National Science Foundation for Distinguished Young Scholars(Grant No.JQ201113)SDUST's National Science Foundation for Distinguished Young Scholars(Grant No.2010KYJQ102)
文摘Vertical errors often present in multibeam swath bathymetric data. They are mainly sourced by sound refraction, internal wave disturbance, imperfect tide correction, transducer mounting, long period heave, static draft change, dynamic squat and dynamic motion residuals, etc. Although they can be partly removed or reduced by specific algorithms, the synthesized depth biases are unavoidable and sometimes have an important influence on high precise utilization of the final bathymetric data. In order to. confidently identify the decimeter-level changes in seabed morphology by MBES, we must remove or weaken depth biases and improve the precision of multibeam bathymetry further. The fixed-interval profiles that are perpendicular to the vessel track are generated to adjust depth biases between swaths. We present a kind of postprocessing method to minimize the depth biases by the histogram of cumulative depth biases. The datum line in each profile can be obtained by the maximum value of histogram. The corrections of depth biases can be calculated according to the datum line. And then the quality of final bathymetry can be improved by the corrections. The method is verified by a field test.
基金This study was supported by the National Major Fundamental Research Development Project of China under contract No. G2000046704.
文摘With processing and interpretation of 25 000 km full-coverage multibeam swath data fromthe eastern South China Sea, it is found that NE-trending and NW-trending linear morphological features such as scarps, horsts and grabens, govern the central part (14°-17° N) of eastern subbasin. Compared with reflection seismic profiles, these NE-trending linear morpho-structures are considered to be the representation of basement structures on seabed and can be divided into three linear structural zones. The trend of the central zone is NE45°-50° occurring around extinct spreading center, the trend of the second zone is NE70° - 78° on both sides of the central one and the trend of the third zone is about NE60° just on the north of the second one. These three NE-trending linear zones are formed in late-stage NW - SE-trending seafloor spreading of the eastern subbasin along NW-trending linear faults, and respectively correspond to three spreading episodes: 17.0- 19.0 Ma (5d-5e), 19.0 - 21.0 Ma (5e-6a) and 21.0 - 24.2 Ma (6a-6c) based on the contrast of morpho-structures to magnetic lineation anomalies.
文摘Sediment mapping is a crucial component of environmental science,particularly in the marine environment,where the analysis of seabed sediments is essential for various purposes,including marine resource management,habitat preservation,and infrastructure development.Sediment refers to the solid particles that are transported and deposited in different areas.Multibeamechosounders have revolutionized the field of seabed sediment mapping by providing unparalleled resolution and accuracy in seafloor surveys.This study aimed to produce sediment maps by implementing multi-frequency,e.g.,200,400,550,and 700 kHz multibeam data using a machine learning algorithm,e.g.,Support Vector Machine(SVM)and Random Forest(RF)approach.The study area of this research was located at Waduk Selorejo,Kecamatan Ngantang,Kabupaten Malang,Indonesia.To achieve the aim,bathymetry and backscatter mosaic with multi-frequency results are produced.Then,the second derivatives from bathymetry and backscatter mosaic are produced(slope,ruggedness,aspect,curvature,mean,homogeneity,entropy,correlation,phi,and characterization)were used as the predictors.All the predictors with multi-frequency each have 0.5 m of spatial resolution.To classify,Pearson’s correlation coefficient method is used to identify strong and weak correlations.The weak correlation was used to run the classification by using SVM and RF.The Angular Range Analysis(ARA)characterization is used as the ground truth data.The accuracy assessment of themultiple-frequency sedimentmap will be compared with the two algorithms of machine learning(e.g.,SVM and RF).This study highlights the importance of sediment mapping and the potential of acoustic methods and machine learning algorithms for seafloor classification.
