**5. Conclusions**

Based on the successful application of machine learning technology in gas hydrate saturation using well logs, we proposed a method for estimating gas hydrate saturation from well logs using deep learning technology to establish the deep internal connections and laws of the data. Considering that well logs are sequence samples, this method designed the LSTM recurrent neural network to be suitable for processing sequential data, took the resistivity and acoustic velocity logs that are more sensitive to gas hydrates as input, took the gas hydrate saturation calculated by the chloride concentration as the output, and trained the LSTM recurrent neural network to accurately predict the saturation of gas hydrate. This method had higher accuracy prediction of gas hydrate saturation than traditional machine learning methods and achieved good application results in the two studied sites in the Shenhu area, South China Sea. It demonstrated the unique advantages of deep learning technology in gas hydrate saturation estimates, and laid the foundation for its further application in gas hydrate research.

**Author Contributions:** C.L. designed the experiments and wrote the paper; X.L. proposed the theory. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Natural Science Foundation of China (No. 41974153) and the Fundamental Research Funds for the Central Universities (No. 2652019038).

**Acknowledgments:** The design of this research was done by C.L. while he was a visiting scholar at the College of Earth, Ocean, and Atmospheric Sciences at Oregon State University. C.L. would like to thank Anne Tréhu for her providing the opportunity to work at Oregon State University.

**Conflicts of Interest:** The authors declare no conflict of interest.
