Deep learning for eastimating dissolved oxygen in global ocean

Details for Oxyformer will be publicly available upon publication!

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📔 Table of Contents

About the Project
- Model architecture
- Contributions
Getting Started
- Prerequisites
- Installation
Data preparation
- Dissolved Oxygen measurements
- Driven factors
Develop the Oxyformer
Data and Results
Contributing
License
Contact
Acknowledgements

🌟 About the Project

🔥 Update

[2023/07/11] We have released the annual Dissolved Oxygen Products derived by Oxyformer, details are shown in here.

🧰 Getting Started

‼️ Prerequisites

Python 3.8
Pytorch 1.10.1
CUDA 11.3 or higher

⚙️ Installation

First, install dependencies

  # clone project 
  git clone https://github.com/Vipermdl/Oxyformer
  
  # install project
  cd Oxyformer
  pip install -r requirements.txt

🧭 Data preparation

Dissolved Oxygen measurements
Driven factors

🎨 Dissolved Oxygen measurements

Name	Date accessed
World Ocean Database	02-2023
CLIVAR and Carbon Hydrographic Database	02-2023
Pangaea Database	02-2023
Global Ocean Data Analysis	02-2023

After download the measurements of oxygen data, run the following command to data compilation and quality control

  # interpolate depth mapping using pchip function and data integrated.
  python scripts/interpolate.py
  # quality control for observational oxygen data
  python scripts/quality_control.py

🔑 Driven factors

More details will be updated soon...
After download the driven factors, run the following command to obtain the dataset dicatating Oxyformer's input and output data and train/val/test splits.

  python scripts/label_utils.py --merge --splits

👀 Develop the Oxyformer

📷 Model architecture

🧪 Training Oxyformer

To train Oxyformer, run the following command

  python main.py experiments/Oxyformer.toml

🏃 Run inference

To save predictions between July 2002 and December 2020 as NetCDFs for Oxyformer run

  python inference.py experiments/inference.toml -o outputdir

🚩 Post process

The post-processing algorithm is then applied

  python post-processing/mask_result_by_bathymetric.py

📜 Data and Results

See DATA.md for instructions on how to download the data of our Oxyformer.
Please read the DRAW.md to generate the paper figures.

👋 Contributing

Contributions are always welcome!

⚠️ License

Distributed under the no License. See LICENSE.txt for more information.

🤝 Contact

Dongliang Ma - @dongliangma1 - mdl.viper@gmail.com

Project Link: https://github.com/Vipermdl/Oxyformer

💎 Acknowledgements

Use this section to mention useful resources and libraries that you have used in your projects.

About

Oxyformer: a deep learning method for estimating global ocean dissolved oxygen

deep-learning earth-science