Legacy Effects of Earthquake-Induced Landslides under Sequential Seismic Events:SHAP-Based Interpretation of Preconditioning and Release Processes during the 2023–2024 Noto Earthquakes

The Noto Peninsula, Japan, experienced two strong earthquakes within a short interval of approximately eight months in 2023 and 2024; the first event triggered only a limited number of landslides (28), whereas the second event resulted in widespread slope failures, with more than 2,300 landslides identified. This rare sequence provides a unique opportunity to investigate how landslide susceptibility and triggering mechanisms evolve under repeated seismic loading within the same tectonic and geomorphological setting. However, conventional landslide susceptibility studies typically treat successive earthquakes as independent events, overlooking the potential influence of prior seismic damage on subsequent slope failures.

 

In this study, we propose an interpretable, SHAP-based machine learning framework to analyze the temporal evolution of earthquake-induced landslide susceptibility during the 2023–2024 Noto earthquake sequence. An XGBoost model was first trained using landslide data from the 2023 event, during which landslide occurrences were sparse, and transfer learning was employed to enhance model robustness under small-sample conditions. SHAP-based interpretation indicates that landslide susceptibility in 2023 was primarily controlled by topographic and long-period seismic factors, with the top five contributors being elevation, surface roughness, slope gradient, long-period spectral acceleration (PSA at 3.0 s), and the topographic position index (TPI), reflecting a preconditioning process that brought slopes close to instability. The resulting susceptibility map was then compared with the spatial distribution of landslides triggered by the 2024 earthquake, revealing a pronounced spatial overlap between the 2023 high-susceptibility (potentially unstable) zones and the 2024 observed landslide locations. In contrast, SHAP analysis for the 2024 event shows a shift in dominant controlling factors toward roughness, peak ground velocity (PGV), TPI, mid-period spectral acceleration (PSA at 1.0 s), and slope gradient, indicating a release process in which pre-weakened slopes were driven beyond their stability thresholds by stronger and more velocity-dominated ground motion.

 

The results indicate a pronounced spatial correspondence between high-susceptibility areas identified after the 2023 earthquake and landslide occurrences in 2024, with a lift value of 2.80 for the top 5% susceptibility class. SHAP-based interpretation reveals a clear transition in dominant triggering factors between the two events. In 2023, landslide susceptibility was primarily controlled by long-period ground motion and topographic framework, reflecting a preconditioning process that brought slopes close to failure. In contrast, the 2024 earthquake activated widespread landslides through velocity-related and mid-period seismic components, representing a release process that pushed pre-weakened slopes beyond their stability thresholds.

 

These findings demonstrate that earthquake-induced landslides in the Noto Peninsula may follow a slope-state–controlled evolutionary pattern, in which earlier seismic events systematically modify slope conditions and strongly influence the spatial and mechanistic characteristics of subsequent failures. This study highlights the importance of incorporating inter-event interactions into landslide susceptibility modeling and provides new insights for post-earthquake hazard assessment in regions affected by sequential seismic events.