Articles | Volume 17, issue 5
https://doi.org/10.5194/gmd-17-2039-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-2039-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model experiment description paper
| 
08 Mar 2024
Model experiment description paper |
| 08 Mar 2024
| 08 Mar 2024
High-precision 1′ × 1′ bathymetric model of Philippine Sea inversed from marine gravity anomalies
Dechao An
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
Xiaotao Chang
Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China
Zhenming Wang
Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China
Yongjun Jia
National Satellite Ocean Application Service, Ministry of Natural Resources, Beijing 100081, China
Xin Liu
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
Valery Bondur
AEROCOSMOS Research Institute for Aerospace Monitoring, Moscow 105064, Russia
Heping Sun
State Key Laboratory of Geodesy and Earth's Dynamics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China
Related authors
Shuai Zhou, Jinyun Guo, Huiying Zhang, Yongjun Jia, Heping Sun, Xin Liu, and Dechao An
Earth Syst. Sci. Data, 17, 165–179, https://doi.org/10.5194/essd-17-165-2025, https://doi.org/10.5194/essd-17-165-2025, 2025
Short summary
Short summary
Our research focuses on using machine learning to enhance the accuracy and efficiency of bathymetric models. In this paper, a multi-layer perceptron (MLP) neural network is used to integrate multi-source marine geodetic data. And a new bathymetric model of the global ocean, spanning 0–360° E and 80° S–80° N, known as the Shandong University of Science and Technology 2023 Bathymetric Chart of the Oceans (SDUST2023BCO), has been constructed, with a grid size of 1′ × 1′.
Yixiang Liu, Jinyun Guo, Bin Guan, Shaofeng Bian, Heping Sun, and Xin Liu
EGUsphere, https://doi.org/10.5194/egusphere-2025-2585, https://doi.org/10.5194/egusphere-2025-2585, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
This study refines the coastal gravity anomaly model by constructing a residual terrain model using high-resolution topographic and bathymetric data. In the spatial domain, the RTM (residual terrain model) gravity forward modeling method is applied to effectively compensate for the missing high-frequency information in the XGM2019e-2159 gravity anomaly model. As a result, an RTM-corrected XGM2019e-2159 gravity anomaly model for the study area is obtained.
Yong Wang, Shengjun Zhang, and Yongjun Jia
Ocean Sci., 21, 931–944, https://doi.org/10.5194/os-21-931-2025, https://doi.org/10.5194/os-21-931-2025, 2025
Short summary
Short summary
The distance-weighted averaging method was used to calculate the along-orbit sea surface height (SSH) wavenumber spectra of four satellites and to evaluate the along-track resolution capability of the four satellites. The results show that the resolution of Surface Water and Ocean Topography (SWOT) in the Kuroshio region is 25 km, which is twice the resolution of conventional satellites. A parameter was defined using the cross-power-spectrum approach and used to analyse the global ocean.
Ruichen Zhou, Jinyun Guo, Shaoshuai Ya, Heping Sun, and Xin Liu
Earth Syst. Sci. Data, 17, 817–836, https://doi.org/10.5194/essd-17-817-2025, https://doi.org/10.5194/essd-17-817-2025, 2025
Short summary
Short summary
SDUST2023VGGA is a high-resolution (1' × 1') model developed to map the ocean's vertical gradient of gravity anomaly. By using multidirectional mean sea surface data, it reduces the impact of ocean dynamics and provides detailed global gravity anomaly change rates. This model provides critical insights into seafloor structures and ocean mass distribution, contributing to research in marine geophysics and oceanography. The dataset is freely available on Zenodo.
Shengjun Zhang, Xu Chen, Runsheng Zhou, and Yongjun Jia
Geosci. Model Dev., 18, 1221–1239, https://doi.org/10.5194/gmd-18-1221-2025, https://doi.org/10.5194/gmd-18-1221-2025, 2025
Short summary
Short summary
NSOAS24, a new global marine gravity model derived from multi-satellite altimetry missions, represents a significant advancement over its predecessor, NSOAS22. Through optimized processing procedures, NSOAS24 resolves previous issues and demonstrates improved accuracy. Compared to NSOAS22, NSOAS24 shows a reduction of approximately 0.7 mGal in standard deviation when validated against recent shipborne data. Notably, its accuracy now rivals internationally recognized models DTU21 and V32.1.
Xin Liu, Yang Yang, Menghao Song, Xiaofeng Dai, Yurong Ding, Gaoying Yin, and Jinyun Guo
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-2, https://doi.org/10.5194/essd-2025-2, 2025
Revised manuscript not accepted
Short summary
Short summary
This study tackles the challenge of measuring sea surface height in the Arctic Ocean, where ice coverage makes accurate modeling difficult. Using advanced satellite data and innovative methods, a new high-resolution mean sea surface model was created. It achieves greater precision than previous models and offers valuable insights into Arctic oceanography. This research provides an important tool for understanding changes in the Arctic environment and their global impacts.
Shuai Zhou, Jinyun Guo, Huiying Zhang, Yongjun Jia, Heping Sun, Xin Liu, and Dechao An
Earth Syst. Sci. Data, 17, 165–179, https://doi.org/10.5194/essd-17-165-2025, https://doi.org/10.5194/essd-17-165-2025, 2025
Short summary
Short summary
Our research focuses on using machine learning to enhance the accuracy and efficiency of bathymetric models. In this paper, a multi-layer perceptron (MLP) neural network is used to integrate multi-source marine geodetic data. And a new bathymetric model of the global ocean, spanning 0–360° E and 80° S–80° N, known as the Shandong University of Science and Technology 2023 Bathymetric Chart of the Oceans (SDUST2023BCO), has been constructed, with a grid size of 1′ × 1′.
Zhen Li, Jinyun Guo, Chengcheng Zhu, Xin Liu, Cheinway Hwang, Sergey Lebedev, Xiaotao Chang, Anatoly Soloviev, and Heping Sun
Earth Syst. Sci. Data, 16, 4119–4135, https://doi.org/10.5194/essd-16-4119-2024, https://doi.org/10.5194/essd-16-4119-2024, 2024
Short summary
Short summary
A new global marine gravity model, SDUST2022GRA, is recovered from radar and laser altimeter data. The accuracy of SDUST2022GRA is 4.43 mGal on a global scale, which is at least 0.22 mGal better than that of other models. The spatial resolution of SDUST2022GRA is approximately 20 km in a certain region, slightly superior to other models. These assessments suggest that SDUST2022GRA is a reliable global marine gravity anomaly model.
Fengshun Zhu, Jinyun Guo, Huiying Zhang, Lingyong Huang, Heping Sun, and Xin Liu
Earth Syst. Sci. Data, 16, 2281–2296, https://doi.org/10.5194/essd-16-2281-2024, https://doi.org/10.5194/essd-16-2281-2024, 2024
Short summary
Short summary
We used multi-satellite altimeter data to construct a high-resolution marine gravity change rate (MGCR) model on 5′×5′ grids, named SDUST2020MGCR. The spatial distribution of SDUST2020MGCR and GRACE MGCR are similar, such as in the eastern seas of Japan (dipole), western seas of the Nicobar Islands (rising), and southern seas of Greenland (falling). The SDUST2020MGCR can provide a detailed view of long-term marine gravity change, which will help to study the seawater mass migration.
Zhaoqing Dong, Lijian Shi, Mingsen Lin, Yongjun Jia, Tao Zeng, and Suhui Wu
The Cryosphere, 17, 1389–1410, https://doi.org/10.5194/tc-17-1389-2023, https://doi.org/10.5194/tc-17-1389-2023, 2023
Short summary
Short summary
We try to explore the application of SGDR data in polar sea ice thickness. Through this study, we find that it seems difficult to obtain reasonable results by using conventional methods. So we use the 15 lowest points per 25 km to estimate SSHA to retrieve more reasonable Arctic radar freeboard and thickness. This study also provides reference for reprocessing L1 data. We will release products that are more reasonable and suitable for polar sea ice thickness retrieval to better evaluate HY-2B.
Jiajia Yuan, Jinyun Guo, Chengcheng Zhu, Zhen Li, Xin Liu, and Jinyao Gao
Earth Syst. Sci. Data, 15, 155–169, https://doi.org/10.5194/essd-15-155-2023, https://doi.org/10.5194/essd-15-155-2023, 2023
Short summary
Short summary
The mean sea surface (MSS) is a relative steady-state sea level within a finite period with important applications in geodesy, oceanography, and other disciplines. In this study, the Shandong University of Science and Technology 2020 (SDUST2020), a new global MSS model, was established with a 19-year moving average method from multi-satellite altimetry data. Its global coverage is from 80 °S to 84 °N, the grid size is 1'×1', and the reference period is from January 1993 to December 2019.
Chengcheng Zhu, Jinyun Guo, Jiajia Yuan, Zhen Li, Xin Liu, and Jinyao Gao
Earth Syst. Sci. Data, 14, 4589–4606, https://doi.org/10.5194/essd-14-4589-2022, https://doi.org/10.5194/essd-14-4589-2022, 2022
Short summary
Short summary
Accurate marine gravity anomalies play an important role in the fields of submarine topography, Earth structure, and submarine exploitation. With the launch of different altimetry satellites, the density of altimeter data can meet the requirements of inversion of high-resolution and high-precision gravity anomaly models. We construct the global marine gravity anomaly model (SDUST2021GRA) from altimeter data (including HY-2A). The accuracy of the model is high, especially in the offshore area.
Hanna K. Lappalainen, Tuukka Petäjä, Timo Vihma, Jouni Räisänen, Alexander Baklanov, Sergey Chalov, Igor Esau, Ekaterina Ezhova, Matti Leppäranta, Dmitry Pozdnyakov, Jukka Pumpanen, Meinrat O. Andreae, Mikhail Arshinov, Eija Asmi, Jianhui Bai, Igor Bashmachnikov, Boris Belan, Federico Bianchi, Boris Biskaborn, Michael Boy, Jaana Bäck, Bin Cheng, Natalia Chubarova, Jonathan Duplissy, Egor Dyukarev, Konstantinos Eleftheriadis, Martin Forsius, Martin Heimann, Sirkku Juhola, Vladimir Konovalov, Igor Konovalov, Pavel Konstantinov, Kajar Köster, Elena Lapshina, Anna Lintunen, Alexander Mahura, Risto Makkonen, Svetlana Malkhazova, Ivan Mammarella, Stefano Mammola, Stephany Buenrostro Mazon, Outi Meinander, Eugene Mikhailov, Victoria Miles, Stanislav Myslenkov, Dmitry Orlov, Jean-Daniel Paris, Roberta Pirazzini, Olga Popovicheva, Jouni Pulliainen, Kimmo Rautiainen, Torsten Sachs, Vladimir Shevchenko, Andrey Skorokhod, Andreas Stohl, Elli Suhonen, Erik S. Thomson, Marina Tsidilina, Veli-Pekka Tynkkynen, Petteri Uotila, Aki Virkkula, Nadezhda Voropay, Tobias Wolf, Sayaka Yasunaka, Jiahua Zhang, Yubao Qiu, Aijun Ding, Huadong Guo, Valery Bondur, Nikolay Kasimov, Sergej Zilitinkevich, Veli-Matti Kerminen, and Markku Kulmala
Atmos. Chem. Phys., 22, 4413–4469, https://doi.org/10.5194/acp-22-4413-2022, https://doi.org/10.5194/acp-22-4413-2022, 2022
Short summary
Short summary
We summarize results during the last 5 years in the northern Eurasian region, especially from Russia, and introduce recent observations of the air quality in the urban environments in China. Although the scientific knowledge in these regions has increased, there are still gaps in our understanding of large-scale climate–Earth surface interactions and feedbacks. This arises from limitations in research infrastructures and integrative data analyses, hindering a comprehensive system analysis.
Cited articles
An, D.: High-precision 1′×1′ bathymetric model of Philippine Sea inversed from marine gravity anomalies, Zenodo [code], https://doi.org/10.5281/zenodo.10370469, 2023.
An, D., Guo, J., Li, Z., Ji, B., Liu, X., and Chang, X.: Improved gravity-geologic method reliably removing the long-wavelength gravity effect of regional seafloor topography: A case of bathymetric prediction in the South China Sea, IEEE T. Geosci. Remote, 60, 4211912, https://doi.org/10.1109/TGRS.2022.3223047, 2022.
Annan, R. F. and Wan, X.: Mapping seafloor topography of Gulf of Guinea using an adaptive meshed gravity-geologic method, Arab. J. Geosci., 13, 301, https://doi.org/10.1007/s12517-020-05297-8, 2020.
Bondur, V. G. and Grebenyuk, Y. V.: Remote sensing methods for determining the bottom relief of coastal zones of seas and oceans, Mapp. Sci. Remote Sens., 38, 172–190, https://doi.org/10.1080/07493878.2001.10642174, 2001.
GEBCO Bathymetric Compilation Group 2022: The GEBCO_2022 Grid – A continuous terrain model of the global oceans and land, NERC EDS British Oceanographic Data Centre NOC [data set], https://doi.org/10.5285/e0f0bb80-ab44-2739-e053-6c86abc0289c, 2022.
Hilldale, R. C. and Raff, D.: Assessing the ability of airborne LiDAR to map river bathymetry, Earth Surf. Proc. Land., 33, 773–783, https://doi.org/10.1002/esp.1575, 2008.
Hirt, C. and Rexer, M.: Earth2014: 1 arc-min shape, topography, bedrock and ice-sheet models – Available as gridded data and degree-10,800 spherical harmonics, Int. J. Appl. Earth Obs., 39, 103–112, https://doi.org/10.1016/j.jag.2015.03.001, 2015.
Holt, A. F., Royden, L. H., Becker, T. W., and Faccenna, C.: Slab interactions in 3-D subduction settings: The Philippine Sea Plate region, Earth Planet. Sc. Lett., 489, 72–83, https://doi.org/10.1016/j.epsl.2018.02.024, 2018.
Hsiao, Y. S., Kim, J. W., Kim, K. B., Lee, B. Y., and Hwang, C.: Bathymetry estimation using the gravity geologic method: An investigation of density contrast predicted by the downward continuation method, Terr. Atmos. Ocean. Sci., 22, 347–358, https://doi.org/10.3319/TAO.2010.10.13.01(OC), 2011.
Hsiao, Y. S., Hwang, C., Cheng, Y., Chen, L., Hsu, H., Tsai, J., Liu, C., Wang, C., Liu, Y., and Kao, Y.: High-resolution depth and coastline over major atolls of South China Sea from satellite altimetry and imagery, Remote Sens. Environ., 176, 69–83, https://doi.org/10.1016/j.rse.2016.01.016, 2016.
Hu, M., Li, J., and Jin, T.: Bathymetry Inversion with Gravity-Geologic Method in Emperor Seamount, Geomat. Inf. Sci. Wuhan Univ., 37, 610–612, https://doi.org/10.13203/j.whugis2012.05.008, 2012.
Hu, Q., Huang, X., Zhang, Z., Zhang, X., Xu, X., Sun, H., Zhou, C., Zhao, W., and Tian, J.: Cascade of internal wave energy catalyzed by eddy-topography interactions in the deep South China Sea, Geophys. Res. Lett., 47, e2019GL086510, https://doi.org/10.1029/2019GL086510, 2020.
Hwang, C.: A bathymetric model for the South China Sea from satellite altimetry and depth data, Mar. Geod., 22, 37–51, https://doi.org/10.1080/014904199273597, 1999.
Ibrahim, A. and Hinze W. J.: Mapping buried bedrock topography with gravity, Ground Water, 10, 18–23, https://doi.org/10.1111/j.1745-6584.1972.tb02921.x, 1972.
Jena, B., Kurian, P. J., Swain, D., Tyagi, A., and Ravindra, R.: Prediction of bathymetry from satellite altimeter based gravity in the Arabian Sea: Mapping of two unnamed deep seamounts, Int. J. Appl. Earth Obs., 16, 1–4, https://doi.org/10.1016/j.jag.2011.11.008, 2012.
Kim, K. B. and Yun, H. S.: Satellite-derived bathymetry prediction in shallow waters using the gravity-geologic method: A case study in the west sea of Korea, KSCE J. Civil Eng., 22, 2560–2568, https://doi.org/10.1007/s12205-017-0487-z, 2018.
Kim, K. B., Hsiao, Y. S., Kim, J. W., Lee, B. Y., Kwon, Y. K., and Kim, C. H.: Bathymetry enhancement by altimetry-derived gravity anomalies in the East Sea (Sea of Japan), Mar. Geophys. Res., 31, 285–298, https://doi.org/10.1007/s11001-010-9110-0, 2010.
Kunze, E. and Smith, S. G. L.: The role of small-scale topography in turbulent mixing of the global ocean, Oceanography, 17, 55–64, https://doi.org/10.5670/oceanog.2004.67, 2004.
Lallemand, S.: Philippine Sea Plate inception, evolution, and consumption with special emphasis on the early stages of Izu-Bonin-Mariana subduction, Prog. Earth Planet. Sci., 3, 15, https://doi.org/10.1186/s40645-016-0085-6, 2016.
Mayer, L., Jakobsson, M., Allen, G., Dorschel, B., Falconer, R., Ferrini, V., Lamarche, G., Snaith, H., and Weatherall, P.: The Nippon foundation – GEBCO seabed 2030 project: The quest to see the world's oceans completely mapped by 2030, Geosciences, 8, 63, https://doi.org/10.3390/geosciences8020063, 2018.
Nagarajan, R.: Gravity-geologic investigation of buried bedrock topography in northwestern Ohio, Ohio, The Ohio State University, 1994.
NOAA National Centers for Environmental Information: Seafloor Mapping [data set], https://www.ncei.noaa.gov/maps/bathymetry/, last access: 16 September 2023.
Parker, R. L.: The rapid calculation of potential anomalies, Geophys. J. Int., 31, 447–455, https://doi.org/10.1111/j.1365-246X.1973.tb06513.x, 1973.
Richter, C. and Ali, J. R.: Philippine Sea Plate motion history: Eocene-recent record from ODP Site 1201, central West Philippine Basin, Earth Planet. Sc. Lett., 410, 165–173, https://doi.org/10.1016/j.epsl.2014.11.032, 2015.
Ryabinin, V., Barbiere, J., Haugan, P., Kullenberg, G., Smith, N., McLean, C., Troisi, A., Fischer, A., Arico, S., Aarup, T., Pissierssens, P., Visbeck, M., Enevoldsen, H. O., and Rigaud, J.: The UN decade of ocean science for sustainable development, Front. Mar. Sci., 6, 470, https://doi.org/10.3389/fmars.2019.00470, 2019.
Scripps Institution of Oceanography: V32.1 gravity anomaly model [data set], https://topex.ucsd.edu/pub/global_grav_1min/ (last access: 16 September 2023), 2023a.
Scripps Institution of Oceanography: topo_25.1 bathymetric model [data set], https://topex.ucsd.edu/pub/global_topo_1min/ (last access: 16 September 2023), 2023b.
Smith, W. H. F.: On the accuracy of digital bathymetric data, J. Geophys. Res.-Sol. Ea., 98, 9591–9603, https://doi.org/10.1029/93JB00716, 1993.
Smith, W. H. F.: Introduction to this special issue on bathymetry from space, Oceanography, 17, 6–7, https://doi.org/10.5670/oceanog.2004.62, 2004.
Smith, W. H. F. and Sandwell, D. T.: Bathymetric prediction from dense satellite altimetry and sparse shipboard bathymetry, J. Geophys. Res.-Sol. Ea., 10, 21803–21824, https://doi.org/10.1029/94JB00988, 1994.
Technical University of Denmark: DTU18 bathymetric model [data set], https://ftp.space.dtu.dk/pub/DTU18/1_MIN/, last access: 16 September 2023.
Tozer, B., Sandwell, D. T., Smith, W. H. F., Olson, C., Beale, J. R., and Wessel, P.: Global bathymetry and topography at 15 arc sec: SRTM15+, Earth Space Sci., 6, 1847–1864, https://doi.org/10.1029/2019EA000658, 2019.
Watts, A. B.: An analysis of isostasy in the world's oceans 1. Hawaiian-Emperor seamount chain, J. Geophys. Res.-Sol. Ea., 83, 5989–6004, https://doi.org/10.1029/JB083iB12p05989, 1978.
Wei, X., Liu, X., Li, Z., Chang, X., Luo, H., Zhu, C., and Guo, J.: Gravity anomalies determined from mean sea surface model data over the Gulf of Mexico, Acta Oceanol. Sin., 42, 52–70, https://doi.org/10.1007/s13131-023-2178-6, 2023.
Wei, Z., Guo, J., Zhu, C., Yuan, J., Chang, X., and Ji, B.: Evaluating accuracy of HY-2A/GM-derived gravity data with the gravity-geologic method to predict bathymetry, Front. Earth Sci., 9, 636246, https://doi.org/10.3389/feart.2021.636246, 2021.
Wolfl, A.-C., Snaith, H., Amirebrahim, S., Devey, C. W., Dorschel, B., Ferrini, V., Huvenne, V. A. I., Jakobsson, M., Jencks, J., Johnston, G., Lamarche, G., Mayer, L., Millar, D., Pedersen, T. H., Picard, K., Reitz, A., Schmitt, T., Visbeck, M., Weatherall, P., and Wigley, R.: Seafloor mapping – the challenge of a truly global ocean bathymetry, Front. Mar. Sci., 6, 283, https://doi.org/10.3389/fmars.2019.00283, 2019.
Xu, C., Li, J., Jian, G., Wu, Y., and Zhang, Y.: An adaptive nonlinear iterative method for predicting seafloor topography from altimetry-derived gravity data, J. Geophys. Res.-Sol. Ea., 128, e2022JB025692, https://doi.org/10.1029/2022JB025692, 2023.
Yang, J., Jekeli, C., and Liu, L.: Seafloor topography estimation from gravity gradients using simulated annealing, J. Geophys. Res.-Sol. Ea., 123, 6958–6975, https://doi.org/10.1029/2018JB015883, 2018.
Yang, J., Luo, Z., and Tu, L.: Ocean access to Zachariæ Isstrøm glacier, northeast Greenland, revealed by OMG airborne gravity, J. Geophys. Res.-Sol. Ea., 125, e2020JB020281, https://doi.org/10.1029/2020JB020281, 2020.
Yeu, Y., Yee, J., Yun, H. S., and Kim K. B.: Evaluation of the accuracy of bathymetry on the nearshore coastlines of western Korea from satellite altimetry, multi-beam, and airborne bathymetric LiDAR, Sensors, 18, 2926, https://doi.org/10.3390/s18092926, 2018.
Yu, J., An, B., Xu, H., Sun, Z., Tian, Y., and Wang, Q.: An iterative algorithm for predicting seafloor topography from gravity anomalies, Remote Sens., 15, 1069, https://doi.org/10.3390/rs15041069, 2023.
Yu, L., Du, J., Zhai, R. Wu, F., and Qian, H.: A fast generalization method of multibeam echo soundings for nautical charting, J. Geovisualization Spat. Anal., 6, 2, https://doi.org/10.1007/s41651-021-00096-5, 2022.
Zhou, R., Liu, X., Li, Z., Sun, Y., Yuan, J., Guo, J., and Ardalan, A. A.: On performance of vertical gravity gradient determined from CryoSat-2 altimeter data over Arabian Sea, Geophys. J. Int., 234, 1519–1529, https://doi.org/10.1093/gji/ggad153, 2023.
Zhu, C., Guo, J., Yuan, J., Li, Z., Liu, X., and Gao, J.: SDUST2021GRA: global marine gravity anomaly model recovered from Ka-band and Ku-band satellite altimeter data, Earth Syst. Sci. Data, 14, 4589–4606, https://doi.org/10.5194/essd-14-4589-2022, 2022.
Short summary
Seafloor topography, as fundamental geoinformation in marine surveying and mapping, plays a crucial role in numerous scientific studies. In this paper, we focus on constructing a high-precision seafloor topography and bathymetry model for the Philippine Sea (5° N–35° N, 120° E–150° E), based on shipborne bathymetric data and marine gravity anomalies, and evaluate the reliability of the model's accuracy.
Seafloor topography, as fundamental geoinformation in marine surveying and mapping, plays a...
