Preprints
https://doi.org/10.5194/gmd-2022-140
https://doi.org/10.5194/gmd-2022-140
Submitted as: model experiment description paper
06 Jul 2022
Submitted as: model experiment description paper | 06 Jul 2022
Status: this preprint was under review for the journal GMD but the revision was not accepted.

An Improved Method Based on VGGNet for Refined Bathymetry from Satellite Altimetry: Reducing the Errors Effectively

Xiaolun Chen1, Xiaowen Luo1,4, Ziyin Wu1,2,3, Xiaoming Qin3, Jihong Shang1, Mingwei Wang1, and Hongyang Wan1 Xiaolun Chen et al.
  • 1Key Laboratory of Submarine Geosciences, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China
  • 2School of Oceanography, Shanghai Jiao Tong University, Shanghai, China
  • 3Ocean College, Zhejiang University, Zhoushan, China
  • 4Key Laboratory of Ocean Space Resource Management Technology, Marine Academy of Zhejiang Province, Hangzhou, China

Abstract. At present, only approximately 10 % of the global seafloor topography has been finely modeled, and the rest are either lacking in data or not accurate enough to meet practical requirements. On the one hand, satellite altimeter has the advantages of large-scale and real-time observation, thus is widely used in the measurement of bathymetry, the core of seafloor topography. However, there is often room for improvement in its precision. On the other hand, multibeam echosounder bathymetric data is highly precise but normally limited to a smaller coverage, which forms a complementary relationship with the bathymetry derived from satellite altimetry. To combine the advantages of satellite altimetry-derived and multibeam sonar-derived bathymetry, we apply deep learning, which is powerful in the field of digital image automation, to perform multibeam sonar-based bathymetry correction for satellite altimetry bathymetry data. Specifically, we modify and improve a pretrained VGGNet neural network model with a depth of 19 layers to train on three sets of bathymetry data from the West Pacific, Southern Ocean, and East Pacific, respectively. Experiments show that the correlation of bathymetry data before and after correction can reach a high level, with the performance of R2 being as high as 0.81 and the RMSE improved over 19 % compared with previous research. We then explore the relationship between R2 and water depth and conclude that it varies at different depths and thus the terrain specificity was a factor that affects the precision of correction. Finally, we use the difference of water depth before and after the correction to evaluate the correction results, and find that our method can improve by more than 17 % compared with previous research. The results show that using the deep learning VGGNet model can better perform the correction of the bathymetry derived from satellite altimetry, thus providing a method for accurate modeling of the seafloor topography.

Xiaolun Chen et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • AC1: 'Comment on gmd-2022-140', Xiaowen Luo, 10 Jul 2022
  • RC1: 'Comment on gmd-2022-140', Anonymous Referee #1, 27 Aug 2022
    • AC2: 'Reply on RC1', Xiaowen Luo, 13 Sep 2022
  • RC2: 'Comment on gmd-2022-140', Anonymous Referee #2, 03 Sep 2022
    • AC3: 'Reply on RC2', Xiaowen Luo, 13 Sep 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • AC1: 'Comment on gmd-2022-140', Xiaowen Luo, 10 Jul 2022
  • RC1: 'Comment on gmd-2022-140', Anonymous Referee #1, 27 Aug 2022
    • AC2: 'Reply on RC1', Xiaowen Luo, 13 Sep 2022
  • RC2: 'Comment on gmd-2022-140', Anonymous Referee #2, 03 Sep 2022
    • AC3: 'Reply on RC2', Xiaowen Luo, 13 Sep 2022

Xiaolun Chen et al.

Xiaolun Chen et al.

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Short summary
To combine the advantages of satellite altimetry-derived and multibeam sonar-derived bathymetry, we apply deep learning to perform multibeam sonar-based bathymetry correction for satellite altimetry bathymetry data. Specifically, we modify and improve a pretrained VGGNet neural network mode. Experiments show that the model can improve the precision.