Intelligent identification and application of gas hydrate in South China Sea

TIAN Dongmei; YANG Shengxiong; LIU Xin; LI Yuanheng; HU Guang; CAO Jingya; ZHOU Junming; DENG Yutian

doi:10.16562/j.cnki.0256-1492.2024092401

2024 Vol. 44, No. 6

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Article navigation > Marine Geology & Quaternary Geology > 2024 > 44(6): 25-33

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TIAN Dongmei, YANG Shengxiong, LIU Xin, LI Yuanheng, HU Guang, CAO Jingya, ZHOU Junming, DENG Yutian. Intelligent identification and application of gas hydrate in South China Sea[J]. Marine Geology & Quaternary Geology, 2024, 44(6): 25-33. doi: 10.16562/j.cnki.0256-1492.2024092401

Citation:

TIAN Dongmei, YANG Shengxiong, LIU Xin, LI Yuanheng, HU Guang, CAO Jingya, ZHOU Junming, DENG Yutian. Intelligent identification and application of gas hydrate in South China Sea[J]. Marine Geology & Quaternary Geology, 2024, 44(6): 25-33. doi: 10.16562/j.cnki.0256-1492.2024092401

Intelligent identification and application of gas hydrate in South China Sea

1.
Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China
2.
Guangzhou Marine Geological Survey, Guangzhou 511458, China

More Information

Corresponding author: LIU Xin, lxin05@gmlab.ac.cn

Received Date: 24 September 2024

Revised Date: 23 December 2024

Publish Date: 28 December 2024

Abstract

Abstract

Gas hydrate is an important ideal energy source, with advantages of high energy, large reserves, wide distribution, and shallow burial. Accurate identification of gas hydrate reservoirs and estimation of hydrate saturation are the prerequisite for the application of gas hydrate resources. This study focuses on the difficult issues of hydrate identification, combining the interdisciplinary technologies of oceanology, geology, and artificial intelligence. Effective methods of hydrate-bearing strata identification were proposed based on the geophysical attributes, and verified in the Dongsha area of South China Sea. Machine-learning algorithms were used to analyze whether the sediment contains gas hydrates. Several commonly used machine-learning algorithms were selected, including random forest, Bagging, AdaBoost, and KNN; and data were analyzed based on the P-wave velocity and density attributes that are more sensitive to hydrate existence. The parameters of different algorithms were trained and optimized, and the effects of different algorithms on the identification and classification were compared. All these algorithms could do good on whether there is hydrate in the sediment, of which KNN algorithm was shown the best. Therefore, machine-learning-based methods could improve the identification accuracy of gas hydrate.
- hydrate /
- identification /
- machine learning /
- seismic attributes /
- South China Sea

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References

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