Potential Earthquake-Triggered Landslide Susceptibility Mapping Integrating Deterministic Ground Motion Simulation and Machine Learning: A Case Study of the Litang Fault Zone

The Litang fault zone, with intense late Quaternary activity, frequent strong earthquakes, and dense landslides along its trend, directly threatens the operational safety of the National Highway 318 (G318) Sichuan–Xizang transportation corridor. Predicting potential earthquake-triggered landslide (EQTL) susceptibility is limited by the spatial heterogeneity of near-fault ground motion coupled with geological/topographic conditions. This study proposes a physics-driven, data-integrated framework that couples curvilinear grid finite-difference (CG-FDM) PGA simulation with a fault-geometry-incorporated random forest model—trained on landslides from the 2008 Mw 7.9 Wenchuan earthquake and adapted to the Litang fault zone via the 71° dip sub-model—for EQTL susceptibility prediction. Validation against historical high-susceptibility landslide zones and unstable slopes confirms the results. Maximum PGA reached 0.611 g with banded distributions along the fault, reflecting significant near-fault, basin, and topographic amplification. High and very high susceptibility zones cover 7.64% of the area, mainly within 5 km of the fault and at intersections, overlapping 57% of historical landslide susceptibility zones and containing 75.0% (by number) and 53.0% (by area) of unstable slopes. The minimum angle between ground-motion vector and aspect suggests enhanced triggering along slope direction. In the G318 Litang section, 13.48% (14.21 km) lies within high–very high susceptibility zones, indicating localized hazards. This framework supports EQTL susceptibility mapping and mitigation for major projects.