Tsunami Potential and Scaling Characteristics of Rotational Submarine Landslides in the Eastern Margin of the Sea of Japan
This study evaluates the tsunami generation potential and scaling characteristics of rotational submarine landslides along the eastern margin of the Sea of Japan, where many submarine active faults and fine-grained sediments are distributed. These landslides pose significant tsunami hazards, but their locations and magnitudes are difficult to predict, making them a major source of uncertainty. We identified landslide traces using high-resolution bathymetric data, estimated landslide volumes, and applied an established empirical formula to calculate initial tsunami amplitudes. The results were validated against numerical simulations using a two-layer model. Several potential tsunami sources were identified with initial amplitudes exceeding 10 m, all located in shallow waters on the continental shelf and characterized by thick landslide deposits. A clear power-law relationship was observed between landslide area and volume, consistent with previous studies of rotational slides. The volume--frequency relationship also followed a power law, in contrast to the logarithmic trends typically associated with flow-type landslides. Spatial analysis revealed that landslide events with significant tsunami impact often clustered near submarine active faults, suggesting strong ground motion as a primary trigger. The consistency between empirical and numerical estimates supports the validity of the empirical formula for rotational landslide tsunamis. These findings demonstrate the value of failure mode-specific scaling analysis and source characterization for improving quantitative tsunami hazard assessments. The results may contribute to probabilistic hazard analysis, such as Monte Carlo simulations, and support coastal disaster prevention planning.
Institutions
- National Defence Academy (IN)
- Okayama University (JP)
- Kanto Gakuin University (JP)
- Ricoh (Japan) (JP)
Publication Details
- Published
- 2025-08-14
- DOI
- https://doi.org/10.22541/essoar.175516860.05726472/v1
- Citations
- 1
- Primary Topic
- Impact of AI and Big Data on Business and Society
- Type
- article
- Field-Weighted Citation Impact
- 2.11