Bingxin Shi

In recent years, seismic activity in the southern Sichuan region has increased significantly. Frequent moderate-to-strong earthquakes have caused severe building damage, casualties, and substantial economic losses, making regional seismic risk increasingly prominent. Based on historical seismic catalogs, geological settings, macroseismic intensity data, and strong motion records, this study systematically analyzes regional seismicity, spatial distribution, and strong ground motion characteristics, and quantitatively investigates the distribution and variation of seismic intensity. It further explores the impacts of earthquakes on various building structures, geological disaster chains, lifeline engineering, and human safety, as well as the underlying damage mechanisms. Finally, targeting the widely existing brick masonry structures, this paper proposes cost-effective and easy-to-implement seismic reinforcement measures combined with typical failure modes and casualty causes. The results provide a scientific basis for seismic disaster prevention planning, engineering seismic practice, and risk management in southern Sichuan and comparable regions.

Earthquake-induced landslide reactivation poses a sustained hazard for years following major seismic events, yet operational prediction remains constrained by heterogeneous multi-modal data, sparse supervision, and the absence of uncertainty-aware frameworks. This paper presents PIMSEL, a physically guided multi-modal semi-supervised framework for post-seismic landslide reactivation risk assessment. PIMSEL integrates satellite-derived morphological features, precipitation time series, and seismic hazard attributes through four components: entropy-regularized optimal transport for cross-modal semantic alignment without paired supervision; causally constrained hierarchical fusion enforcing domain-consistent modal weighting; scenario-based prototype mutation for semi-supervised learning from sparse expert annotations; and prototype-anchored variational graph clustering that simultaneously stratifies landslides into HIGH, MEDIUM, and LOW risk tiers and produces decomposed aleatoric and epistemic uncertainty estimates for operational triage. The HIGH risk tier operationally corresponds to predicted reactivation, validated against 598 documented reactivation events across 7482 co-seismic landslides from three Sichuan Province earthquake sequences: the 2013 Lushan (Mw 7.0), 2017 Jiuzhaigou (Mw 7.0), and 2022 Luding (Mw 6.8) events. PIMSEL achieves 82.5% reactivation recall and 66.4% precision, outperforming twelve baselines across clustering quality, classification, and uncertainty calibration metrics. Ablation studies confirm that optimal transport alignment contributes the largest individual performance gain. Current limitations include quarterly assessment frequency and dependence on optical imagery under cloud cover, which future integration of real-time meteorological triggers and SAR data should address.

Rapid and accurate mapping of earthquake-triggered landslides from satellite imagery is critical for emergency response and hazard assessment, yet remains challenging due to irregular boundaries, extreme size variations, and atmospheric noise. This paper proposes BiFusion-LDSeg, a novel bi-directional fusion enhanced latent diffusion framework that synergistically combines CNN-Transformer architectures with generative diffusion models for robust landslide segmentation. The framework introduces three key innovations: (1) a dual-encoder with Bi-directional Attention Gates (Bi-AG) enabling sophisticated cross-modal feature calibration between local CNN textures and global Transformer context; (2) a conditional latent diffusion process operating in learned low-dimensional landslide shape manifolds, reducing computational complexity by 100× while enabling inference with only 10 sampling steps versus 1000+ in standard diffusion models; and (3) a boundary-aware progressive decoder employing multi-scale reverse attention mechanisms for precise boundary delineation. Comprehensive experiments on three earthquake datasets from Sichuan Province, China (Lushan Mw 7.0, Jiuzhaigou Mw 6.5, Luding Mw 6.8) demonstrate superior performance, outperforming state-of-the-art methods by 7–13% in IoU and 5–7% in DSC across all three datasets. The framework exhibits exceptional noise robustness, strong cross-dataset generalization, and inherent uncertainty quantification, enabling reliable deployment for post-earthquake landslide inventory mapping at regional scales.