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AIgravity: Recovering Gravity from Satellite Altimetry Data Using Deep Learning

Welcome to the AIgravity project! This project uses deep learning techniques to recover the ocean gravity field from satellite altimetry data.

Paper

Our paper, titled 'Recovering Gravity from Satellite Altimetry Data using Deep Learning Network', has been accepted by the Journal of IEEE Transactions on Geoscience and Remote Sensing (TGRS). In this paper, we detail the methods and results of this project.

Code

The code for this project is available in this repository. It includes all the scripts and data files necessary to reproduce our results. We have also included detailed comments in the code to explain how it works.

Usage

To use this code, you will need to have Python and several Python libraries installed, including TensorFlow, NumPy, and Matplotlib. You can run the code on any system that supports Python.

Citation

If you find this code useful in your research, please consider citing our paper. You can use the following citation:

@article{zhu_recovering_2023,
	title = {Recovering {Gravity} from {Satellite} {Altimetry} {Data} using {Deep} {Learning} {Network}},
	copyright = {All rights reserved},
	issn = {1558-0644},
	doi = {10.1109/TGRS.2023.3280261},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	author = {Zhu, Chengcheng and Yang, Lei and Bian, Hongwei and Li, Houpu and Guo, Jinyun and Liu, Na and Lin, Lina},
	year = {2023},
	note = {Conference Name: IEEE Transactions on Geoscience and Remote Sensing},
	keywords = {satellite altimetry, gravity anomaly, Sea measurements, Satellites, Data models, Deep learning, Training, Gravity, deep learning, multi-channel convolutional neural network, submarine topography, Underwater vehicles},
	pages = {1--1},
}

Link

For more details, please refer to our paper on IEEE Xplore.

Contact

If you have any questions or suggestions about this project, please feel free to open an issue or submit a pull request.