Global Ocean Methane Seepage Flux Based on Submarine Observation of South China Sea Combine with Machine Learning

Data for article “Global Ocean Methane Seepage Flux Based on Submarine Observation of South China Sea Combine with Machine Learning” Marine methane seepage represents a critical component of the global carbon-water cycle, yet existing studies face persistent challenges: scarcity of long-term continuous monitoring data, difficulty in capturing the complex interactions of multiple environmental factors, and limited scalability from local observations to global estimates. This research addresses these gaps through three major innovations. First, we pioneer the application of machine learning with SHAP analysis in marine methane seepage prediction, achieving exceptional model accuracy (R² > 0.95) while ensuring model transparency and interpretability. Second, our study systematically identifies key environmental sensitivity factors controlling seepage dynamics from seafloor sedimentary layers to the seawater interface, revealing that pressure dominates high-intensity seepage events (61% contribution) with distinct tidal periodicity. Third, we provide robust quantitative estimates of methane flux at both regional (South China Sea: 0.964 ± 0.482 Tg CH₄/year) and global scales (11.465±0.382 ~ 49.613±1.394 Tg CH₄/year), global ocean methane seepage flux is significantly underestimated (beforehand 6-12 Tg CH₄/year).