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":"46 1","pages":"129 - 148"},"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":"46 1","pages":"216 - 250"},"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":"46 1","pages":"195 - 215"},"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":"46 1","pages":"175 - 193"},"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":"46 1","pages":"149 - 174"},"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":"45 1","pages":"688 - 709"},"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}
Pub Date : 2022-08-22DOI: 10.1080/01490419.2022.2116616
Bimalkumar Patel, R. Sarangi, Apurva Prajapati, Bhargav Devliya, Hitesh Patel
Abstract TSM is an essential parameter as it affects the biogeochemistry of the ocean. The high TSM range affects light penetration that’s related to the photosynthesis of primary producers. The aim is to develop a TSM algorithm in Gujarat coastal water using remote sensing reflectance (Rrs), to monitor TSM concentration from the satellite. Seawater sampling and HyperOCR radiometer data collection were carried out in the northeast Arabian Sea. The high suspended matter was observed near the Gulf of Khambhat due to industries and riverine fluxes. For an accurate TSM algorithm, we compared the developed algorithm to previous studies. The TSM algorithm has been developed using the Rrs681/Rrs490 band ratio that has the highest linear correlation (R2 = 0.977, MSE = 19.06). Rrs band ratios demonstrated better compared to single Rrs bands. Satellite images were generated by applying the developed algorithm with the input of Rrs681 and Rrs490 from OLCI. The developed algorithm has been validated successfully with in situ TSM data points, collected across the Daman, Porbandar, and Okha coastal waters. The study indicates that the developed algorithm can be more robust and valuable for various satellite-based synoptic mapping of TSM, including the future Indian Oceansat-3 OCM mission.
{"title":"Development of Total Suspended Matter (TSM) Algorithm and Validation over Gujarat Coastal Water, the Northeast Arabian Sea Using In Situ Datasets","authors":"Bimalkumar Patel, R. Sarangi, Apurva Prajapati, Bhargav Devliya, Hitesh Patel","doi":"10.1080/01490419.2022.2116616","DOIUrl":"https://doi.org/10.1080/01490419.2022.2116616","url":null,"abstract":"Abstract TSM is an essential parameter as it affects the biogeochemistry of the ocean. The high TSM range affects light penetration that’s related to the photosynthesis of primary producers. The aim is to develop a TSM algorithm in Gujarat coastal water using remote sensing reflectance (Rrs), to monitor TSM concentration from the satellite. Seawater sampling and HyperOCR radiometer data collection were carried out in the northeast Arabian Sea. The high suspended matter was observed near the Gulf of Khambhat due to industries and riverine fluxes. For an accurate TSM algorithm, we compared the developed algorithm to previous studies. The TSM algorithm has been developed using the Rrs681/Rrs490 band ratio that has the highest linear correlation (R2 = 0.977, MSE = 19.06). Rrs band ratios demonstrated better compared to single Rrs bands. Satellite images were generated by applying the developed algorithm with the input of Rrs681 and Rrs490 from OLCI. The developed algorithm has been validated successfully with in situ TSM data points, collected across the Daman, Porbandar, and Okha coastal waters. The study indicates that the developed algorithm can be more robust and valuable for various satellite-based synoptic mapping of TSM, including the future Indian Oceansat-3 OCM mission.","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":"45 1","pages":"670 - 687"},"PeriodicalIF":1.6,"publicationDate":"2022-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49527140","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 : 2022-08-22DOI: 10.1080/01490419.2022.2116615
Kourosh Shahryari Nia, M. Sharifi, S. Farzaneh
Abstract Predicting tides and water levels had always been such an important topic for researchers and professionals since the study of tidal level has pivotal role in supporting marine economy, port construction projects and maritime transportation. Tidal water levels are a combination of astronomical (deterministic part) and non-astronomical (stochastic part) water levels. In this study, we combined Harmonic Analysis (HA) with three Deep Neural Networks (DNNs), namely the Long-Short Term Memory (LSTM), Convolution Neural Network (CNN), and Multilayer Perceptron (MLP). The HA method is used for predicting the astronomical components, while DNNs are used to predict the non-astronomical water level. We have used tide gauge data from three stations along the southern coastline of Iran to demonstrate the effectiveness and accuracy of our model. We utilized RMSE, MAE, R2 (r-squared), and MAPE to evaluate the performance of the model. Finally, The LSTM network shown superior performance in most of the cases, although other networks also show good results. All three DNNs have R2 of 0.99, and the RMSE, MAE, and MAPE indicate that errors are low.
{"title":"Tidal Level Prediction Using Combined Methods of Harmonic Analysis and Deep Neural Networks in Southern Coastline of Iran","authors":"Kourosh Shahryari Nia, M. Sharifi, S. Farzaneh","doi":"10.1080/01490419.2022.2116615","DOIUrl":"https://doi.org/10.1080/01490419.2022.2116615","url":null,"abstract":"Abstract Predicting tides and water levels had always been such an important topic for researchers and professionals since the study of tidal level has pivotal role in supporting marine economy, port construction projects and maritime transportation. Tidal water levels are a combination of astronomical (deterministic part) and non-astronomical (stochastic part) water levels. In this study, we combined Harmonic Analysis (HA) with three Deep Neural Networks (DNNs), namely the Long-Short Term Memory (LSTM), Convolution Neural Network (CNN), and Multilayer Perceptron (MLP). The HA method is used for predicting the astronomical components, while DNNs are used to predict the non-astronomical water level. We have used tide gauge data from three stations along the southern coastline of Iran to demonstrate the effectiveness and accuracy of our model. We utilized RMSE, MAE, R2 (r-squared), and MAPE to evaluate the performance of the model. Finally, The LSTM network shown superior performance in most of the cases, although other networks also show good results. All three DNNs have R2 of 0.99, and the RMSE, MAE, and MAPE indicate that errors are low.","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":"45 1","pages":"645 - 669"},"PeriodicalIF":1.6,"publicationDate":"2022-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47986084","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 : 2022-08-12DOI: 10.1080/01490419.2022.2113578
Xianping Qin, Yuanxi Yang, Bijiao Sun
Abstract The ranges derived from acoustic measurements between seafloor stations are relatively more accurate compared with those derived from the sea surface vessel transducer to the seafloor transponders, because measurements through mixed water layers will be affected by complex acoustic range errors. Coordinates of seafloor stations can be improved by the direct-path acoustic ranging. Systematic errors in acoustic rangings, however, will significantly deteriorate the accuracy of vertical coordinates. In order to mitigate the effects of these systematic errors (e.g., acoustic ray bending and sound speed variation errors in acoustic measurements on the seafloor station location parameters), the observation model needs to be finely constructed. First, a new observation model with acoustic ray bending and sound speed bias parameters is established. Then, using a seafloor geodetic network with four moored stations at a depth of about 3000 m in the South China Sea, the significance of the acoustic ray bending parameter is tested. The results show that (1) the acoustic ray bending parameter is significant at the 90% confidence level, which means that the acoustic ray bending error in the seafloor geodetic network is not negligible; (2) by estimating the coefficient of acoustic ray bending, the influence of the acoustic ray bending error on the vertical coordinate components can be significantly mitigated; our model improves the accuracy of the seafloor stations’ position with differences in the horizontal coordinate components less than 0.1 cm between the two-dimensional adjustment and three-dimensional adjustment, and also improves the vertical coordinate component to uncertainty less than 3.0 cm; (3) the relative movement between the moored stations is less than 50 cm, and the horizontal movement is larger than the vertical movement.
{"title":"A Robust Method to Estimate the Coordinates of Seafloor Stations by Direct-Path Ranging","authors":"Xianping Qin, Yuanxi Yang, Bijiao Sun","doi":"10.1080/01490419.2022.2113578","DOIUrl":"https://doi.org/10.1080/01490419.2022.2113578","url":null,"abstract":"Abstract The ranges derived from acoustic measurements between seafloor stations are relatively more accurate compared with those derived from the sea surface vessel transducer to the seafloor transponders, because measurements through mixed water layers will be affected by complex acoustic range errors. Coordinates of seafloor stations can be improved by the direct-path acoustic ranging. Systematic errors in acoustic rangings, however, will significantly deteriorate the accuracy of vertical coordinates. In order to mitigate the effects of these systematic errors (e.g., acoustic ray bending and sound speed variation errors in acoustic measurements on the seafloor station location parameters), the observation model needs to be finely constructed. First, a new observation model with acoustic ray bending and sound speed bias parameters is established. Then, using a seafloor geodetic network with four moored stations at a depth of about 3000 m in the South China Sea, the significance of the acoustic ray bending parameter is tested. The results show that (1) the acoustic ray bending parameter is significant at the 90% confidence level, which means that the acoustic ray bending error in the seafloor geodetic network is not negligible; (2) by estimating the coefficient of acoustic ray bending, the influence of the acoustic ray bending error on the vertical coordinate components can be significantly mitigated; our model improves the accuracy of the seafloor stations’ position with differences in the horizontal coordinate components less than 0.1 cm between the two-dimensional adjustment and three-dimensional adjustment, and also improves the vertical coordinate component to uncertainty less than 3.0 cm; (3) the relative movement between the moored stations is less than 50 cm, and the horizontal movement is larger than the vertical movement.","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":"46 1","pages":"83 - 98"},"PeriodicalIF":1.6,"publicationDate":"2022-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49644818","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 : 2022-07-08DOI: 10.1080/01490419.2022.2089412
I. Bij de Vaate, Ericka Martin, D. C. Slobbe, M. Naeije, M. Verlaan
Abstract In the Arctic Ocean, obtaining water levels from satellite altimetry is hampered by the presence of sea ice. Hence, water level retrieval requires accurate detection of fractures in the sea ice (leads). This paper describes a thorough assessment of various surface type classification methods, including a thresholding method, nine supervised-, and two unsupervised machine learning methods, applied to Sentinel-3 Synthetic Aperture Radar Altimeter data. For the first time, the simultaneously sensed images from the Ocean and Land Color Instrument, onboard Sentinel-3, were used for training and validation of the classifiers. This product allows to identify leads that are at least 300 meters wide. Applied to data from winter months, the supervised Adaptive Boosting, Artificial Neural Network, Naïve-Bayes, and Linear Discriminant classifiers showed robust results with overall accuracies of up to 92%. The unsupervised Kmedoids classifier produced excellent results with accuracies up to 92.74% and is an attractive classifier when ground truth data is limited. All classifiers perform poorly on summer data, rendering surface classifications that are solely based on altimetry data from summer months unsuitable. Finally, the Adaptive Boosting, Artificial Neural Network, and Bootstrap Aggregation classifiers obtain the highest accuracies when the altimetry observations include measurements from the open ocean.
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