Improving the accuracy of bathymetry using the combined neural network and gravity wavelet decomposition method with altimetry derived gravity data

References

• Amante, C., and B. W. Eakins. 2009. ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis. Psychologist 16 (3):20–5.
   Google Scholar
• Annan, R. F., and X. Y. Wan. 2022. Recovering Bathymetry of the Gulf of Guinea Using Altimetry-Derived Gravity Field Products Combined via Convolutional Neural Network. Surveys in Geophysics 43 (5):1541–61.
   Web of Science ®Google Scholar
• Daubechies, I. 1992. Ten Lectures on Wavelets. In CBMS-NSF Regional Conference Series in Applied Mathematics. Philadelphia, PA: Society for Industrial and Applied Mathematics.
   Google Scholar
• Dixon, T. H., M. Naraghi, M. K. Mcnutt, et al. 1983. Bathymetric Prediction from SEASAT Altimeter Data. Journal of Geophysical Research 88 (C3):1563–71.
   Web of Science ®Google Scholar
• Fan, D., S. S. Li, J. K. Feng, et al. 2022. A New Global Bathymetry Model: STO_IEU2020. Remote Sensing 14 (2):5744–65. (): -.
   Web of Science ®Google Scholar
• Fan, D., S. S. Li, S. Y. Meng, et al. 2020. Applying Robust Estimation Method to Estimate Seafloor Topography in the Sea of Japan. Journal of Chinese Inertial Technology 28 (5):576–85.
   Google Scholar
• Fan, D., S. S. Li, J. J. Yang, et al. 2020. Predicting Bathymetry by Applying Multiple Regression Analysis in the Southwest Indian Ocean Region. Acta Geodaetica et Cartographica Sinica 49 (2):147–61.
   Google Scholar
• Fan, D., S. S. Li, S. Y. Meng, et al. 2020. Applying Iterative Method to Solving High-order Terms of Seafloor Topography. Marine Geodesy 43 (1):63–85.
   Web of Science ®Google Scholar
• Gille, S. T. 2012. Mean Sea Surface Height of the Antarctic Circumpolar Current from Geosat Data: Method and Application. Journal of Geophysical Research Oceans 99 (C9):18255–73.
   Web of Science ®Google Scholar
• Guenther, G. C., W. J. Lillycrop, and J. R. Bank. 2002. Future Advancements in Airborne Hydrography. International Hydrographic Review 3 (2):67–90. https://journals.lib.unb.ca/index.php/ihr/article/view/20589.
   Google Scholar
• Hagan, M. T., and M. Menhaj. 1994. Training Feed Forward Networks with the Marquardt Algorithm. IEEE Transactions on Neural Networks 5 (6):989–93.
   PubMed Web of Science ®Google Scholar
• Harris, P. T., M. Macmillan-Lawler, J. Rupp, et al. 2014. Geomorphology of the Oceans. Marine Geology 352 (1):4–24.
   Web of Science ®Google Scholar
• Hu, M. Z., L. Li, T. Y. Jin, et al. 2021. A New 1′×1′ Global Seafloor Topography Model Predicted from Satellite Altimetric Vertical Gravity Gradient Anomaly and Ship Soundings BAT_VGG2021. Remote Sensing 13 (17):3515–31.
   Web of Science ®Google Scholar
• Hu, M. Z., J. C. Li, H. Li, et al. 2014. Bathymetry Predicted from Vertical Gravity Gradient Anomalies and Ship Soundings. Geodesy and Geodynamics 5 (001):41–6.
   Google Scholar
• Hu, M. Z., J. C. Li, H. Li, et al. 2014. Predicting Global Seafloor Topography Using Multi-Source Data. Marine Geodesy 38 (2):176–89.
   Web of Science ®Google Scholar
• Hwang, C. 1999. A Bathymetric Model for the South China Sea from Satellite Altimetry and Depth Data. Marine Geodesy 22 (1):37–51.
   Google Scholar
• Ibrahim, A., and W. J. Hinze. 1972. Mapping Buried Bedrock Topography with Gravity. Ground Water.10 (3):18–23.
   Google Scholar
• Jawak, S. D., S. S. Vadlamani, and A. J. Luis. 2015. A Synoptic Review on Deriving Bathymetry Information Using Remote Sensing Technologies: Models, Methods and Comparisons. Advances Remote Sensing 4 (1):147–62.
   Google Scholar
• Jena, B., P. J. Kurian, D. Swain, and A. Tyagi. 2012. Prediction of Bathymetry from Satellite Altimeter based Gravity in the Arabian Sea: Mapping of Two Unnamed Deep Seamounts. International Journal of Applied Earth Observation and Geoinformation 16 (1):1–4.
   Web of Science ®Google Scholar
• Kim, K. B., Y. S. Hsiao, J. W. Kim, et al. 2010. Bathymetry Enhancement by Altimetry-Derived Gravity Anomalies in the East Sea (Sea of Japan). Marine Geophysical Research 31 (4):285–98.
   Web of Science ®Google Scholar
• Kim, J. W., R. R. B. V. Frese, B. Y. Lee, et al. 2011. Altimetry-Derived Gravity Predictions of Bathymetry by the Gravity-Geologic Method. Pure and Applied Geophysics 168 (5):815–26.
   Web of Science ®Google Scholar
• Larry, M., J. Martin, A. Graham, et al. 2018. The Nippon Foundation—GEBCO Seabed 2030 Project: The Quest to See the World’s Oceans Completely Mapped by 2030. Geosciences 8 (2):63–80.
   Web of Science ®Google Scholar
• Lek, S., M. Delacoste, P. Ba Ran, et al. 1996. Application of Neural Networks to Modelling Nonlinear Relationships in Ecology. Ecological Modelling 90 (1):39–52.
   Web of Science ®Google Scholar
• Liu, S., L. Wang, H. X. Liu, et al. 2018. Deriving Bathymetry from Optical Images with a Localized Neural Network Algorithm. IEEE Transactions on Geoscience and Remote Sensing 56 (9):1–9.
   Web of Science ®Google Scholar
• Mallat, S. G. 1989. A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 11:674–93.
   Web of Science ®Google Scholar
• Nason, G. P. 2008. Wavelet Methods in Statistics with R. Springer: New York, USA.
   Google Scholar
• Ramillien, G., and I. C. Wright. 2000. Predicted Seafloor Topography of the New Zealand Region: A Nonlinear Least Squares Inversion of Satellite Altimetry Data. Journal of Geophysical Research Solid Earth 105 (B7):16577–90.
   Web of Science ®Google Scholar
• Sandwell, D. T., J. A. Goff, H. Harper, et al. 2022. Improved Bathymetric Prediction using Geological Information: SYNBATH. Earth and Space Science 9 (2):1–17.
   Web of Science ®Google Scholar
• Sandwell, D. T., W. Smith, S. Gille, et al. 2006. Bathymetry from Space: Rationale and Requirements for a New, High-resolution Altimetric Mission. Comptes Rendus Geoscience 338 (14-15):1049–62.
   Web of Science ®Google Scholar
• Schimel, A., T. R. Healy, D. Johnson, et al. 2010. Quantitative Experimental Comparison of Single-beam, Sidescan, and Multibeam Benthic Habitat Maps. ICES Journal of Marine Science 67 (8):1766–79.
   Web of Science ®Google Scholar
• Seo, Y., Y. Choi, and J. Choi. 2017. River Stage Modeling by Combining Maximal Overlap Discrete Wavelet Transform, Support Vector Machines and Genetic Algorithm. Water 9 (7):525–48.
   Web of Science ®Google Scholar
• Smith, W. H. F. 2004. Introduction to This Special Issue on Bathymetry from Space. Oceanography 17 (1):6–7.
   Google Scholar
• Smith, W. H. F., and D. T. Sandwell. 1997. Global Sea Floor Topography from Satellite Altimetry and Ship Depth Soundings. Science 277 (5334):1956–62.
   Web of Science ®Google Scholar
• Smith, W. H. F., and D. T. Sandwell. 1994. Bathymetric Prediction from Dense Satellite Altimetry and Sparse Shipboard Bathymetry. Journal of Geophysical Research 99 (B11):21803–24.
   Web of Science ®Google Scholar
• Sun, Y. J., W. Zheng, Z. W. Li, et al. 2021. Improved the Accuracy of Seafloor Topography from Altimetry-Derived Gravity by the Topography Constraint Factor Weight Optimization Method. Remote Sensing 13 (12):2277–94.
   Web of Science ®Google Scholar
• Titov, V., A. B. Rabinovich, H. O. Mofjeld, R. E. Thomson, and F. I. González. 2005. The Global Reach of the 26 December 2004 Sumatra Tsunami. Science (New York, N.Y.) 309 (5743):2045–8.
   PubMed Web of Science ®Google Scholar
• Tozer, B., D. T. Sandwell, W. H. F. Smith, et al. 2019. Global Bathymetry and Topography at 15ArcSec: SRTM15+. Earth and Space Science 6 (10):1847–64.
   Web of Science ®Google Scholar
• Wang, Y. M. 2000. Predicting Bathymetry from the Earth’s Gravity Gradient Anomalies. Marine Geodesy 23 (4):251–8.
   Google Scholar
• Wu, S. Q., Y. Lu, Z. J. Fang, et al. 2010. Weighted Linear Normalization Method of Infrared Image. Journal of Wuhan University of Technology 32 (20):1–5.
   Google Scholar
• Xu, C., Z. C. Luo, R. Sun, et al. 2018. Multilayer Densities Using a Wavelet-based Gravity Method and Their Tectonic Implications Beneath the Tibetan Plateau. Geophysical Journal International 213 (3):2085–95.
   Web of Science ®Google Scholar
• Yang, J. J., C. Jekeli, and L. T. Liu. 2018. Seafloor Topography Estimation from Gravity Gradients Using Simulated Annealing. Journal of Geophysical Research of Solid Earth 123 (8):6958–75.
   Web of Science ®Google Scholar
• Zhang, Y., S. Huang, Y. L. Han, et al. 2022. Machine Learning Methods for Spaceborne GNSS-R Sea Surface Height Measurement From TDS-1. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15 (1):1079–88.
   Web of Science ®Google Scholar