PSO-optimized dual-channel BP neural network for bathymetric prediction using multisource marine geodetic data: A case study of the gulf of Mexico
Accurate seafloor topography is essential for marine scientific research, resource exploration, and understanding geological processes. Traditional bathymetric surveying methods are constrained by limited spatial coverage and high operational costs, particularly in deep-sea environments. To overcome...
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Elsevier
2025-12-01
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| Series: | Science of Remote Sensing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S266601722500080X |
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| author | Jiajia Yuan Haoran Liu Jianli Chen Chen Yang |
| author_facet | Jiajia Yuan Haoran Liu Jianli Chen Chen Yang |
| author_sort | Jiajia Yuan |
| collection | DOAJ |
| description | Accurate seafloor topography is essential for marine scientific research, resource exploration, and understanding geological processes. Traditional bathymetric surveying methods are constrained by limited spatial coverage and high operational costs, particularly in deep-sea environments. To overcome these challenges, we developed a Particle Swarm Optimization (PSO)-optimized dual-channel BP neural network (PSO_BP), integrating shipborne bathymetric data with satellite altimetry-derived gravity anomalies. These gravity anomalies were further decomposed into long-wavelength, short-wavelength, and residual components to enhance bathymetric prediction accuracy. We systematically evaluate the impact of different gravity data combinations, including gravity anomalies, gravity gradients, and vertical deflections, used individually, in pairs, or as a three-component combination, on bathymetric prediction accuracy. Results show that PSO_BP consistently outperforms existing models (GEBCO_2024, Topo_25.1, DTU18_BAT, and SRTM15 + V2.6), achieving the lowest RMSE (25.45 m), MAE (9.95 m), MAPE (3.70 %), and highest R2 (99.96 %) across various depth ranges and shoreline distances. The decomposition of gravity anomalies into long- and short-wavelength components and their residuals proves to be the most effective approach for improving bathymetric prediction accuracy, while PSO optimization enhances model convergence and reduces prediction errors. This study highlights the importance of integrating diverse gravity datasets and advanced optimization techniques to improve the accuracy and robustness of seafloor depth prediction, offering a reliable solution for global bathymetric mapping in deep and remote ocean regions. |
| format | Article |
| id | doaj-art-ee56da8f25964d828a61d72337f067ac |
| institution | Kabale University |
| issn | 2666-0172 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Science of Remote Sensing |
| spelling | doaj-art-ee56da8f25964d828a61d72337f067ac2025-08-24T05:14:33ZengElsevierScience of Remote Sensing2666-01722025-12-011210027410.1016/j.srs.2025.100274PSO-optimized dual-channel BP neural network for bathymetric prediction using multisource marine geodetic data: A case study of the gulf of MexicoJiajia Yuan0Haoran Liu1Jianli Chen2Chen Yang3School of Geomatics, Anhui University of Science and Technology, Huainan, Anhui, China; Corresponding author.School of Geomatics, Anhui University of Science and Technology, Huainan, Anhui, ChinaDepartment of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China; Research Institute for Land and Space, The Hong Kong Polytechnic University, Hong Kong, ChinaSchool of Geomatics, Anhui University of Science and Technology, Huainan, Anhui, ChinaAccurate seafloor topography is essential for marine scientific research, resource exploration, and understanding geological processes. Traditional bathymetric surveying methods are constrained by limited spatial coverage and high operational costs, particularly in deep-sea environments. To overcome these challenges, we developed a Particle Swarm Optimization (PSO)-optimized dual-channel BP neural network (PSO_BP), integrating shipborne bathymetric data with satellite altimetry-derived gravity anomalies. These gravity anomalies were further decomposed into long-wavelength, short-wavelength, and residual components to enhance bathymetric prediction accuracy. We systematically evaluate the impact of different gravity data combinations, including gravity anomalies, gravity gradients, and vertical deflections, used individually, in pairs, or as a three-component combination, on bathymetric prediction accuracy. Results show that PSO_BP consistently outperforms existing models (GEBCO_2024, Topo_25.1, DTU18_BAT, and SRTM15 + V2.6), achieving the lowest RMSE (25.45 m), MAE (9.95 m), MAPE (3.70 %), and highest R2 (99.96 %) across various depth ranges and shoreline distances. The decomposition of gravity anomalies into long- and short-wavelength components and their residuals proves to be the most effective approach for improving bathymetric prediction accuracy, while PSO optimization enhances model convergence and reduces prediction errors. This study highlights the importance of integrating diverse gravity datasets and advanced optimization techniques to improve the accuracy and robustness of seafloor depth prediction, offering a reliable solution for global bathymetric mapping in deep and remote ocean regions.http://www.sciencedirect.com/science/article/pii/S266601722500080XBathymetric predictionParticle swarm optimizationBackpropagation neural networkGravity anomalyMarine geophysical data integration |
| spellingShingle | Jiajia Yuan Haoran Liu Jianli Chen Chen Yang PSO-optimized dual-channel BP neural network for bathymetric prediction using multisource marine geodetic data: A case study of the gulf of Mexico Science of Remote Sensing Bathymetric prediction Particle swarm optimization Backpropagation neural network Gravity anomaly Marine geophysical data integration |
| title | PSO-optimized dual-channel BP neural network for bathymetric prediction using multisource marine geodetic data: A case study of the gulf of Mexico |
| title_full | PSO-optimized dual-channel BP neural network for bathymetric prediction using multisource marine geodetic data: A case study of the gulf of Mexico |
| title_fullStr | PSO-optimized dual-channel BP neural network for bathymetric prediction using multisource marine geodetic data: A case study of the gulf of Mexico |
| title_full_unstemmed | PSO-optimized dual-channel BP neural network for bathymetric prediction using multisource marine geodetic data: A case study of the gulf of Mexico |
| title_short | PSO-optimized dual-channel BP neural network for bathymetric prediction using multisource marine geodetic data: A case study of the gulf of Mexico |
| title_sort | pso optimized dual channel bp neural network for bathymetric prediction using multisource marine geodetic data a case study of the gulf of mexico |
| topic | Bathymetric prediction Particle swarm optimization Backpropagation neural network Gravity anomaly Marine geophysical data integration |
| url | http://www.sciencedirect.com/science/article/pii/S266601722500080X |
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