Abstract The wide range of bathymetry models can be estimated using the marine gravity information derived from satellite altimetry. However, due to nonlinear factors influences such as isostasy effects, the bathymetry estimated by gravity anomaly and vertical gravity gradient is not satisfactory. Therefore, to improve the accuracy of bathymetry estimation, a combined neural network and gravity information wavelet decomposition (CNNGWD) method is proposed based on wavelet decomposition and correlation analysis. Next, the bathymetry of the Manila Trench area is estimated using the CNNGWD method and multilayer neural network (MNN) method, respectively. Then, the shipborne sounding data and international bathymetric models such as ETOPO1 and GEBCO_2021 are separately used to evaluate the accuracy of the inversion models. The results show that the root mean square errors (RMSE) of the difference between the bathymetric model one (BM1) estimated by CNNGWD method and the shipborne sounding data is 59.90 m, the accuracy is improved by 12.45%, 64.70% and 28.68% compared with the bathymetric model two (BM2) which estimated by MNN, ETOPO1 and GEBCO, respectively. Finally, by analyzing the bathymetric accuracy shift with depth, the BM1 has lower RMSE at depths ranging from 1000 m to 3000 m. Furthermore, BM1 shows dominance in flat troughs and rugged ridge regions.
{"title":"Improving the accuracy of bathymetry using the combined neural network and gravity wavelet decomposition method with altimetry derived gravity data","authors":"Yongjin Sun, Wei Zheng, Zhaowei Li, Zhiquan Zhou, Xiaocong Zhou, Zhongkai Wen","doi":"10.1080/01490419.2023.2179140","DOIUrl":"https://doi.org/10.1080/01490419.2023.2179140","url":null,"abstract":"Abstract The wide range of bathymetry models can be estimated using the marine gravity information derived from satellite altimetry. However, due to nonlinear factors influences such as isostasy effects, the bathymetry estimated by gravity anomaly and vertical gravity gradient is not satisfactory. Therefore, to improve the accuracy of bathymetry estimation, a combined neural network and gravity information wavelet decomposition (CNNGWD) method is proposed based on wavelet decomposition and correlation analysis. Next, the bathymetry of the Manila Trench area is estimated using the CNNGWD method and multilayer neural network (MNN) method, respectively. Then, the shipborne sounding data and international bathymetric models such as ETOPO1 and GEBCO_2021 are separately used to evaluate the accuracy of the inversion models. The results show that the root mean square errors (RMSE) of the difference between the bathymetric model one (BM1) estimated by CNNGWD method and the shipborne sounding data is 59.90 m, the accuracy is improved by 12.45%, 64.70% and 28.68% compared with the bathymetric model two (BM2) which estimated by MNN, ETOPO1 and GEBCO, respectively. Finally, by analyzing the bathymetric accuracy shift with depth, the BM1 has lower RMSE at depths ranging from 1000 m to 3000 m. Furthermore, BM1 shows dominance in flat troughs and rugged ridge regions.","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46464215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-24DOI: 10.1080/01490419.2023.2184436
D. Ganguly, M. Raman
Abstract In this article, the applicability of India’s indigenously developed scatterometer, Scatsat-1 for marine aerosol characterization and wind dependency of aerosol optical depth (AOD) was investigated using 3 years of satellite data. Wind data from Scatsat-1 and AOD with its size fraction from SNPP VIIRS from 2017 to 2019 were used for the investigation. A technique has been demonstrated for identifying remote oceanic regions with mostly marine aerosol using certain criteria like distance from land, higher fraction of coarse AOD, lower variation of AOD with wind direction, and so forth. Daily wind and AOD from 2017 to 2019 were used for studying the relationship between wind speed and AOD at remote oceanic locations away from terrestrial and anthropogenic influence. Five regions of interest (ROIs) of 5° by 5° were identified in different oceanic regions for carrying out the regression analysis. A linear increase of coarse mode AOD and total AOD with an increase in wind speed was observed for all five remote locations while fine mode AOD was unrelated. The slope of the linear relation agreed with Kiliyanpilakkil and Meskhidze (2011) for intermediate wind values. The remote regions also had a lower variation of AOD with change in wind direction implying less transport of aerosol from nearby landmasses.
{"title":"Estimating the Wind Dependency of Aerosol Optical Depth at Remote Oceanic Regions","authors":"D. Ganguly, M. Raman","doi":"10.1080/01490419.2023.2184436","DOIUrl":"https://doi.org/10.1080/01490419.2023.2184436","url":null,"abstract":"Abstract In this article, the applicability of India’s indigenously developed scatterometer, Scatsat-1 for marine aerosol characterization and wind dependency of aerosol optical depth (AOD) was investigated using 3 years of satellite data. Wind data from Scatsat-1 and AOD with its size fraction from SNPP VIIRS from 2017 to 2019 were used for the investigation. A technique has been demonstrated for identifying remote oceanic regions with mostly marine aerosol using certain criteria like distance from land, higher fraction of coarse AOD, lower variation of AOD with wind direction, and so forth. Daily wind and AOD from 2017 to 2019 were used for studying the relationship between wind speed and AOD at remote oceanic locations away from terrestrial and anthropogenic influence. Five regions of interest (ROIs) of 5° by 5° were identified in different oceanic regions for carrying out the regression analysis. A linear increase of coarse mode AOD and total AOD with an increase in wind speed was observed for all five remote locations while fine mode AOD was unrelated. The slope of the linear relation agreed with Kiliyanpilakkil and Meskhidze (2011) for intermediate wind values. The remote regions also had a lower variation of AOD with change in wind direction implying less transport of aerosol from nearby landmasses.","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47108396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-06DOI: 10.1080/01490419.2023.2175084
H. Guarneri, M. Verlaan, D. C. Slobbe, J. Veenstra, F. Zijl, J. Pietrzak, M. Snellen, L. Keyzer, Y. Afrasteh, R. Klees
Abstract Both empirical and assimilative global ocean tidal models are significantly more accurate in the deep ocean than in shelf and coastal waters. In this study, we answered whether this is due to the quality of the models used to reduce tide and surge or the general approach to treat tide and surge as two separate components of the water level obtained from stand-alone models, which ignores the nonlinear tide–surge interaction. In doing so, we used tide gauge observations as partially synthetic altimeter time series, tide–surge water-level time series obtained with the 2D Dutch Continental Shelf Model – Flexible Mesh (DCSM), and tide and surge water-level time series obtained using the DCSM, FES2014 (FES) and the Dynamic Atmospheric Correction (DAC) product. Expressed in the root-sum-square (RSS) of the eight main tidal constituents, we obtained a reduction % when removing the DCSM tide–surge water levels compared to when we removed the sum of the DCSM tide and DCSM surge water levels. The RSS obtained in the latter case was only 3.3% lower than with FES and DAC. We conclude that the lower tidal estimates accuracy in shelf-coastal waters derives from the missing nonlinear tide–surge interactions.
{"title":"The impact of nonlinear tide–surge interaction on satellite radar altimeter-derived tides","authors":"H. Guarneri, M. Verlaan, D. C. Slobbe, J. Veenstra, F. Zijl, J. Pietrzak, M. Snellen, L. Keyzer, Y. Afrasteh, R. Klees","doi":"10.1080/01490419.2023.2175084","DOIUrl":"https://doi.org/10.1080/01490419.2023.2175084","url":null,"abstract":"Abstract Both empirical and assimilative global ocean tidal models are significantly more accurate in the deep ocean than in shelf and coastal waters. In this study, we answered whether this is due to the quality of the models used to reduce tide and surge or the general approach to treat tide and surge as two separate components of the water level obtained from stand-alone models, which ignores the nonlinear tide–surge interaction. In doing so, we used tide gauge observations as partially synthetic altimeter time series, tide–surge water-level time series obtained with the 2D Dutch Continental Shelf Model – Flexible Mesh (DCSM), and tide and surge water-level time series obtained using the DCSM, FES2014 (FES) and the Dynamic Atmospheric Correction (DAC) product. Expressed in the root-sum-square (RSS) of the eight main tidal constituents, we obtained a reduction % when removing the DCSM tide–surge water levels compared to when we removed the sum of the DCSM tide and DCSM surge water levels. The RSS obtained in the latter case was only 3.3% lower than with FES and DAC. We conclude that the lower tidal estimates accuracy in shelf-coastal waters derives from the missing nonlinear tide–surge interactions.","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41802423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-09DOI: 10.1080/01490419.2023.2166173
P. Elmore, B. Calder, F. Petry, G. Masetti, Ron Yager
Abstract When planning for ship navigation or compiling data for a bathymetry map, the navigator or mapper uses many different sources of bathymetry information and navigation hazards. The quality of these sources is inconsistent in general, however, making it challenging to provide a coherent picture for planning. Here, we describe an approach for consistent planning/mapping that uses a combination of soft computing and Bayesian estimation. The case study used to exercise this system involves NOAA Electronic Nautical Charts for an area in the Chesapeake Bay. We first interpolate each set of irregularly spaced soundings to gridded versions of each point-cloud set. Each of these intermediate grids is then aggregated into a fused bathymetric realization using order weighted averaging (OWA) to provide the weights for each source based on their subjective reliabilities. The OWA allows for fusion informed by the user’s subjective risk allowed in the reconstruction of the seafloor surface and provides quantitative methods to generate, use, and record subjective reliability weights. Each sounding point that went into the bathymetry estimate is then categorized as “no-go,” “caution,” or “go” status. Reliability estimates are reused for weighted Bayesian categorization of each output grid cell to compute the navigable surface.
{"title":"Aggregation Methods Using Bathymetry Sources of Differing Subjective Reliabilities for Navigation Mapping","authors":"P. Elmore, B. Calder, F. Petry, G. Masetti, Ron Yager","doi":"10.1080/01490419.2023.2166173","DOIUrl":"https://doi.org/10.1080/01490419.2023.2166173","url":null,"abstract":"Abstract When planning for ship navigation or compiling data for a bathymetry map, the navigator or mapper uses many different sources of bathymetry information and navigation hazards. The quality of these sources is inconsistent in general, however, making it challenging to provide a coherent picture for planning. Here, we describe an approach for consistent planning/mapping that uses a combination of soft computing and Bayesian estimation. The case study used to exercise this system involves NOAA Electronic Nautical Charts for an area in the Chesapeake Bay. We first interpolate each set of irregularly spaced soundings to gridded versions of each point-cloud set. Each of these intermediate grids is then aggregated into a fused bathymetric realization using order weighted averaging (OWA) to provide the weights for each source based on their subjective reliabilities. The OWA allows for fusion informed by the user’s subjective risk allowed in the reconstruction of the seafloor surface and provides quantitative methods to generate, use, and record subjective reliability weights. Each sounding point that went into the bathymetry estimate is then categorized as “no-go,” “caution,” or “go” status. Reliability estimates are reused for weighted Bayesian categorization of each output grid cell to compute the navigable surface.","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41429797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-22DOI: 10.1080/01490419.2022.2162646
Junting Wang, Tianhe Xu, Yangfan Liu, Mowen Li, Long Li
Abstract Underwater acoustic navigation technology is an important approach to achieving high precision ocean navigation. One of the critical issues of the technology is to correct systematic errors, which are related to time delays and time-varying sound speed errors. In this study, we propose an augmented underwater acoustic navigation with systematic error model based on seafloor datum network. The proposed algorithm first selects data sets of piece-wise systematic error modeling by extracting the main periodic term of systematic errors based on the Fourier transform. Before that, the wavelet transform is used for denoising to better extract the main periodic term. Then the systematic error correction model is constructed by using the polynomial fitting method. After that, an augmented observation equation of underwater acoustic navigation with systematic error correction is constructed. Finally, an adaptive robust Kalman filter is developed for underwater acoustic navigation. The proposed algorithm is verified by an experiment in the South China Sea. The three-dimensional root mean square values of underwater acoustic navigation are 1.010 and 1.502 m in the operating range of 2.7 and 8.7 km. The results demonstrate that the proposed algorithm can efficiently reduce the influence of systematic error, thus improving underwater acoustic navigation accuracy.
{"title":"Augmented Underwater Acoustic Navigation with Systematic Error Modeling Based on Seafloor Datum Network","authors":"Junting Wang, Tianhe Xu, Yangfan Liu, Mowen Li, Long Li","doi":"10.1080/01490419.2022.2162646","DOIUrl":"https://doi.org/10.1080/01490419.2022.2162646","url":null,"abstract":"Abstract Underwater acoustic navigation technology is an important approach to achieving high precision ocean navigation. One of the critical issues of the technology is to correct systematic errors, which are related to time delays and time-varying sound speed errors. In this study, we propose an augmented underwater acoustic navigation with systematic error model based on seafloor datum network. The proposed algorithm first selects data sets of piece-wise systematic error modeling by extracting the main periodic term of systematic errors based on the Fourier transform. Before that, the wavelet transform is used for denoising to better extract the main periodic term. Then the systematic error correction model is constructed by using the polynomial fitting method. After that, an augmented observation equation of underwater acoustic navigation with systematic error correction is constructed. Finally, an adaptive robust Kalman filter is developed for underwater acoustic navigation. The proposed algorithm is verified by an experiment in the South China Sea. The three-dimensional root mean square values of underwater acoustic navigation are 1.010 and 1.502 m in the operating range of 2.7 and 8.7 km. The results demonstrate that the proposed algorithm can efficiently reduce the influence of systematic error, thus improving underwater acoustic navigation accuracy.","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43910412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1080/01490419.2022.2154293
Yu Zhang, Dan Zhang, Zhen Han, Peng Jiang
Abstract The diversity combining technique performs well in the inhibition of multipath, it has been widely used in underwater acoustic (UWA) array signal processing. However, the underwater noise can seriously affect the processing results of the diversity. The conventional filtering algorithms cannot deal with the nonlinear components of underwater radiation noise and have a poor processing effect on complex signals. This study proposes a novel underwater array angle diversity method based on a two-stage model to overcome the problem. A noise-reduction model with a deep convolutional neural network (DCNN) as the backbone network for deep residual learning by preprocessing complex-type data on the received and reference noise signals in the first stage. In the second stage, a modified weighted delay summation beamformer group model is constructed. This model adjusts the weights of each channel by a gradient descent criterion. The desired angle estimates and delay information are then obtained. Finally, the delayed combining of the signals of each path is completed by the combining strategy. Simulation test results show that the proposed algorithm has a lower bit error rate (BER) for diverse received signals. On-lake tests further verify the effectiveness of the algorithm.
{"title":"Two-Stage Learning Model-Based Angle Diversity Method for Underwater Acoustic Array","authors":"Yu Zhang, Dan Zhang, Zhen Han, Peng Jiang","doi":"10.1080/01490419.2022.2154293","DOIUrl":"https://doi.org/10.1080/01490419.2022.2154293","url":null,"abstract":"Abstract The diversity combining technique performs well in the inhibition of multipath, it has been widely used in underwater acoustic (UWA) array signal processing. However, the underwater noise can seriously affect the processing results of the diversity. The conventional filtering algorithms cannot deal with the nonlinear components of underwater radiation noise and have a poor processing effect on complex signals. This study proposes a novel underwater array angle diversity method based on a two-stage model to overcome the problem. A noise-reduction model with a deep convolutional neural network (DCNN) as the backbone network for deep residual learning by preprocessing complex-type data on the received and reference noise signals in the first stage. In the second stage, a modified weighted delay summation beamformer group model is constructed. This model adjusts the weights of each channel by a gradient descent criterion. The desired angle estimates and delay information are then obtained. Finally, the delayed combining of the signals of each path is completed by the combining strategy. Simulation test results show that the proposed algorithm has a lower bit error rate (BER) for diverse received signals. On-lake tests further verify the effectiveness of the algorithm.","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44138157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-29DOI: 10.1080/01490419.2022.2141931
Lu-peng Zhang, Ding-fa Huang, C. Shum, R. Guo
Abstract Slow slip events (SSEs) are interpreted as the transient quasi-static fault deformation in the deep transition zone from locked to freely slipping in many subduction zones. Using continuous Global Positioning System (cGPS) data collected in New Zealand, we estimate the spatiotemporal evolution model during the 2019 SSE and analyze the influence of subduction interface heterogeneity on seismicity during SSEs at the Hikurangi margin. The results reveal that the 2019 SSE extends from the northern (Gisborne) to the central (Hawke’s Bay) Hikurangi subduction interface and decays rapidly within approximately 3-4 weeks. It releases a total seismic moment of about 4.83 × 1019 N·m (Mw 6.8), with a significant slip in Gisborne and a secondary slip in Hawke’s Bay. The slip depths are similar, but peaks, durations, and rates differ slightly. By combining previous SSEs (2011-2019), diverse characteristics are concluded, i.e., shorter duration and more frequency in Gisborne and relatively longer duration and less frequency in Hawke’s Bay. The seismicity offshore and onshore indicates along-strike variations, which appear to be spatially correlated with the variations in topography, such as subduction seamounts. The heterogeneities on the subduction interface are related to the spatiotemporal distribution of SSEs and seismicity along the Hikurangi margin.
{"title":"The 2019 East Coast Slow Slip Event, New Zealand: Spatiotemporal Evolution and Associated Seismicity","authors":"Lu-peng Zhang, Ding-fa Huang, C. Shum, R. Guo","doi":"10.1080/01490419.2022.2141931","DOIUrl":"https://doi.org/10.1080/01490419.2022.2141931","url":null,"abstract":"Abstract Slow slip events (SSEs) are interpreted as the transient quasi-static fault deformation in the deep transition zone from locked to freely slipping in many subduction zones. Using continuous Global Positioning System (cGPS) data collected in New Zealand, we estimate the spatiotemporal evolution model during the 2019 SSE and analyze the influence of subduction interface heterogeneity on seismicity during SSEs at the Hikurangi margin. The results reveal that the 2019 SSE extends from the northern (Gisborne) to the central (Hawke’s Bay) Hikurangi subduction interface and decays rapidly within approximately 3-4 weeks. It releases a total seismic moment of about 4.83 × 1019 N·m (Mw 6.8), with a significant slip in Gisborne and a secondary slip in Hawke’s Bay. The slip depths are similar, but peaks, durations, and rates differ slightly. By combining previous SSEs (2011-2019), diverse characteristics are concluded, i.e., shorter duration and more frequency in Gisborne and relatively longer duration and less frequency in Hawke’s Bay. The seismicity offshore and onshore indicates along-strike variations, which appear to be spatially correlated with the variations in topography, such as subduction seamounts. The heterogeneities on the subduction interface are related to the spatiotemporal distribution of SSEs and seismicity along the Hikurangi margin.","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41848683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-21DOI: 10.1080/01490419.2022.2128124
Gengming Zhang, Lei Zhang, Song Li, Bin Xue, Weishuai Xu
Abstract Most mesoscale eddy tracking methodologies used prior to this study evaluated eddy features using a distance-based proximity relationship, rather than considering similarities between eddies. This study applies a fast normalized cross-correlation methodology in the field of image registration to propose a novel mesoscale eddy tracking methodology that can rapidly and comprehensively calculate the similarities between two eddies and judge their relationship through the correlation coefficient, thus facilitating a more accurate mesoscale eddy trajectory tracking. The sea level anomaly data field is employed to identify the positions of eddies over time. The tracking methodology is then used to track the mesoscale eddy trajectories. After comparing the local nearest neighbor methodology (LNN) with our proposed new methodology in the Northwest Pacific Ocean, we conclude that the proposed methodology can address issues of discontinuity in tracking; especially in cases involving eddies with long lifespans. The tracking trajectories utilized in the proposed methodology achieve superior continuity and integrity and a higher degree of characterization than LNN, with the tracking results showing greater consistency with real eddy motion. The new methodology proposed in this paper has great significance for more widespread use.
{"title":"A new mesoscale eddy tracking methodology based on fast normalized cross-correlation and its validation in the Northwest Pacific","authors":"Gengming Zhang, Lei Zhang, Song Li, Bin Xue, Weishuai Xu","doi":"10.1080/01490419.2022.2128124","DOIUrl":"https://doi.org/10.1080/01490419.2022.2128124","url":null,"abstract":"Abstract Most mesoscale eddy tracking methodologies used prior to this study evaluated eddy features using a distance-based proximity relationship, rather than considering similarities between eddies. This study applies a fast normalized cross-correlation methodology in the field of image registration to propose a novel mesoscale eddy tracking methodology that can rapidly and comprehensively calculate the similarities between two eddies and judge their relationship through the correlation coefficient, thus facilitating a more accurate mesoscale eddy trajectory tracking. The sea level anomaly data field is employed to identify the positions of eddies over time. The tracking methodology is then used to track the mesoscale eddy trajectories. After comparing the local nearest neighbor methodology (LNN) with our proposed new methodology in the Northwest Pacific Ocean, we conclude that the proposed methodology can address issues of discontinuity in tracking; especially in cases involving eddies with long lifespans. The tracking trajectories utilized in the proposed methodology achieve superior continuity and integrity and a higher degree of characterization than LNN, with the tracking results showing greater consistency with real eddy motion. The new methodology proposed in this paper has great significance for more widespread use.","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46928126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-07DOI: 10.1080/01490419.2022.2132327
Sensen Chu, Liang Cheng, J. Cheng, Xuedong Zhang, Jin-Ming Liu
Abstract Satellite-derived bathymetry (SDB), an important technology in marine geodesy, is advantageous because of its wide coverage, low cost, and short revisit cycle. At present, several different kinds of SDB methods exist, and their inversion accuracy is affected by algorithm performance, band selection, and sample distribution, among other factors. But these factors have not been adequately quantified and compared. In the present study, we evaluate the performances and highlight the best scenarios for applying the six classical empirical methods including the log-transformed single band, band ratio (BR), Lyzenga polynomial (LP), support vector regression, third-order polynomial (TOP), and back propagation (BP) neural network. The results reveal that the number of training samples is important for the empirical SDB methods, and the TOP and BP methods need more training samples than other methods. Compared to the robust BR and LP methods, the TOP and BP methods can obtain high accuracy but are severely influenced by incomplete samples. In addition, experiments that prove the local minimum (poor robustness) problem of the BP method exist and cannot be ignored in the bathymetry field. The present study highlights the most suitable method for obtaining reliable SDB results and their applicability.
{"title":"Comparison of Six Empirical Methods for Multispectral Satellite-derived Bathymetry","authors":"Sensen Chu, Liang Cheng, J. Cheng, Xuedong Zhang, Jin-Ming Liu","doi":"10.1080/01490419.2022.2132327","DOIUrl":"https://doi.org/10.1080/01490419.2022.2132327","url":null,"abstract":"Abstract Satellite-derived bathymetry (SDB), an important technology in marine geodesy, is advantageous because of its wide coverage, low cost, and short revisit cycle. At present, several different kinds of SDB methods exist, and their inversion accuracy is affected by algorithm performance, band selection, and sample distribution, among other factors. But these factors have not been adequately quantified and compared. In the present study, we evaluate the performances and highlight the best scenarios for applying the six classical empirical methods including the log-transformed single band, band ratio (BR), Lyzenga polynomial (LP), support vector regression, third-order polynomial (TOP), and back propagation (BP) neural network. The results reveal that the number of training samples is important for the empirical SDB methods, and the TOP and BP methods need more training samples than other methods. Compared to the robust BR and LP methods, the TOP and BP methods can obtain high accuracy but are severely influenced by incomplete samples. In addition, experiments that prove the local minimum (poor robustness) problem of the BP method exist and cannot be ignored in the bathymetry field. The present study highlights the most suitable method for obtaining reliable SDB results and their applicability.","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47218484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-13DOI: 10.1080/01490419.2022.2124560
Albertini Nsiah Ababio, R. Tenzer
Abstract Number of detailed geoid models have been developed to convert geodetic heights measured by the Global Navigation Satellite Systems (GNSS) to heights in the Hong Kong Principal Datum (HKPD). Although gravity measurements were used to compile these geoid models, heights of leveling benchmarks in HKPD were determined from precise spirit leveling measurements but without involving gravity data. To address these inconsistencies, the orthometric heights of HKPD leveling benchmarks were determined from leveling and gravity data. Moreover, the new geoid model HKGEOID-2022 was computed and fitted with the geometric geoid heights at GNSS-leveling benchmarks derived from newly determined orthometric heights. Numerical procedures used to prepare the HKGEOID-2022 geoid are discussed in this study. A gravimetric geoid was computed by using the KTH method. A systematic bias between the gravimetric and geometric geoid heights at GNSS-leveling benchmarks was modeled and reduced by applying a 7-parameter similarity transformation. The accuracy analysis revealed that the resulting detailed geoid model HKGEOID-2022 fits the geometric geoid heights with a standard deviation of ±2.2 cm. This accuracy is compatible with the estimated uncertainties of GNSS measurements as well as with the expected accuracy of a newly developed geoid model, both at the level of approximately ±1–2 cm.
{"title":"Compilation of the new detailed geoid model HKGEOID-2022 for the Hong Kong territories","authors":"Albertini Nsiah Ababio, R. Tenzer","doi":"10.1080/01490419.2022.2124560","DOIUrl":"https://doi.org/10.1080/01490419.2022.2124560","url":null,"abstract":"Abstract Number of detailed geoid models have been developed to convert geodetic heights measured by the Global Navigation Satellite Systems (GNSS) to heights in the Hong Kong Principal Datum (HKPD). Although gravity measurements were used to compile these geoid models, heights of leveling benchmarks in HKPD were determined from precise spirit leveling measurements but without involving gravity data. To address these inconsistencies, the orthometric heights of HKPD leveling benchmarks were determined from leveling and gravity data. Moreover, the new geoid model HKGEOID-2022 was computed and fitted with the geometric geoid heights at GNSS-leveling benchmarks derived from newly determined orthometric heights. Numerical procedures used to prepare the HKGEOID-2022 geoid are discussed in this study. A gravimetric geoid was computed by using the KTH method. A systematic bias between the gravimetric and geometric geoid heights at GNSS-leveling benchmarks was modeled and reduced by applying a 7-parameter similarity transformation. The accuracy analysis revealed that the resulting detailed geoid model HKGEOID-2022 fits the geometric geoid heights with a standard deviation of ±2.2 cm. This accuracy is compatible with the estimated uncertainties of GNSS measurements as well as with the expected accuracy of a newly developed geoid model, both at the level of approximately ±1–2 cm.","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41663924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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