Key Laboratory of Submarine Geosciences, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
2.
Ocean College, Zhejiang University, Zhoushan 316021, China
3.
Key Laboratory of Ocean Space Resources Management Technology, Marine Academy of Zhejiang, Hangzhou 310012, China
4.
School of Oceanography, Shanghai Jiao Tong University, Shanghai 200240, China
5.
School of Civil Engineering and Architectures, Zhejiang University of Science and Technology, Hangzhou 310023, China
6.
National Center for Archaeology, Beijing 100013, China
Funds:
The National Key R&D Program of China under contract No. 2022YFC3003800; the National Natural Science Foundation of China under contract No. 41830540; the Open Fund of the East China Coastal Field Scientific Observation and Research Station of the Ministry of Natural Resources under contract No. OR-SECCZ2022104; the Deep Blue Project of Shanghai Jiao Tong University under contract No. SL2020ZD204; Zhejiang Provincial Project under contract No. 330000210130313013006.
Understanding the topographic patterns of the seafloor is a very important part of understanding our planet. Although the science involved in bathymetric surveying has advanced much over the decades, less than 20% of the seafloor has been precisely modeled to date, and there is an urgent need to improve the accuracy and reduce the uncertainty of underwater survey data. In this study, we introduce a pretrained VGGNet method based on deep learning. To apply this method, we input gravity anomaly data derived from ship measurements and satellite altimetry into the model and correct the latter, which has a larger spatial coverage, based on the former, which is considered the true value and is more accurate. After obtaining the corrected high-precision gravity model, it is inverted to the corresponding bathymetric model by applying the gravity-depth correlation. We choose four data pairs collected from different environments, i.e., the Southern Ocean, Pacific Ocean, Atlantic Ocean and Caribbean Sea, to evaluate the topographic correction results of the model. The experiments show that the R2 reaches 0.834 among the results of the four experimental groups, signifying a high correlation. The standard deviation (SD) and normalized root mean square error (NRMSE) are also evaluated, and the accuracy of their performance improved by up to 24.2% compared with similar research done in recent years. The evaluation of the R2 values at different water depths shows that our model can achieve performance results above 0.90 at certain water depths and can also significantly improve results from mid-water depths when compared to previous research. Finally, the bathymetry corrected by our model is able to show an accuracy improvement level of more than 21% within 1% of the total water depths, which is sufficient to prove that the VGGNet-based method has the ability to perform a gravity-bathymetry correction and achieve outstanding results.
Figure 2. Architecture of the VGG-19 model. The boxes represent the size of each layer.
Figure 3. The calculation and processing flow of the filter method.
Figure 4. The training and validation loss from the experiments in the Southern Ocean (a), Pacific Ocean (b), Atlantic Ocean (c), and Caribbean Sea (d).
Figure 5. Comparison of local details of gravity anomalies before and after correction from the Southern Ocean (a), Pacific Ocean (b), Atlantic Ocean (c), and Caribbean Sea (d).
Figure 6. R2 values at different water depths compared with the bathymetry-only correction methos in the Southern Ocean (a), Pacific Ocean (b), Atlantic Ocean (c), and Caribbean Sea (d).
Figure 7. Percentage distributions of NRMSE (×10-3) performance in datasets from the Southern Ocean, Pacific Ocean, Atlantic Ocean, and Caribbean Sea.
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