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Submarine landslides in regions enriched with gas hydrates pose a significant threat to submarine pipelines, cables, and offshore platforms. Conducting a comprehensive regional-scale susceptibility assessment is crucial for mitigating the potential risks associated with submarine landslides in gas hydrate enrichment regions. This study conducted a preliminary exploration by presenting a probabilistic assessment framework that integrated database construction, rapid prediction model training, and landslide susceptibility assessment in hydrate enrichment regions. The database was a virtual repository constructed using numerical simulations of hydrate dissociation under various combinations of factors, including water depth, geothermal gradients, seafloor slope gradients, the seafloor temperature’s rate of increase, gas hydrate saturation, and the strength and permeability of sediments. The rapid prediction model was trained using machine learning techniques, relying on the virtual database. A probabilistic assessment was performed using Monte Carlo simulations, with the landslide susceptibility determined by the rapid prediction model. The probability of landslide susceptibility exceeding a certain threshold served as an indicator for classifying the susceptibility of the study area. The proposed framework was implemented in the Shenhu area of the South China Sea, which is a representative region known for its substantial hydrate enrichment and well-developed landslides. The trained rapid prediction model for landslide susceptibility exhibited a speed advantage of over 60,000 times compared to traditional numerical calculation methods. The statistical analysis of the results in Monte Carlo simulations suggested that the landslide susceptibility was subjected to a high level of uncertainty due to limited survey data availability. Based on the probability of landslide susceptibility exceeding 0.4 in Monte Carlo simulations, the study area was classified into three zones of susceptibility: low, moderate, and high levels. |
