We observed high-quality waves from a repeatable airgun seismic source recorded by a linear ultra-dense seismic array across the Xiliushui fault zone, one of the inactive faults in the Qilian Mountain, on the northeastern margin of the Tibetan Plateau, China. We used Snell’s law of seismic ray propagation to determine a simplified ambient velocity model. Based on the flexible and precise spectral element method, we computed broadband synthetic seismograms for a shallow low-velocity fault zone (FZ) to model the direct P-wave travel time delay and incident angle of the wavefield near the FZ. The FZ extent range and boundaries were inverted by apparent travel time delays and amplification patterns across the fault. According to prior information on the properties of the direct P-waves, we could constrain the inverse modeling and conduct a grid search for the fault parameters. The velocity reduction between the FZ and host rock, along with the dip angle of the FZ, were also constrained by the P-wave travel time delay systematic analysis and incoming angle of the P-waves. We found that the Xiliushui fault has a 70~80 m-wide low-velocity fault damage zone in which the P-wave velocity is reduced to ~40% with respect to the host rock. The fault damage zone dips ~35°southwest and extends to ~165 m in depth. The repeatability and environment protection characteristics of the airgun seismic survey and the economic benefits of a limited number of instruments setting are prominent.

The Loess Plateau in China is highly susceptible to frequent landslides and other geological disasters, which have led to substantial losses of natural and human resources and are frequently reported in the news media. Yuzhong County, located east of Lanzhou City, is a mountainous region with considerable development potential. On 7 August 2025, this area experienced a large-scale geological disaster characterized by a compound event involving both landslides and debris flows, resulting in nearly several hundred casualties. With the ongoing urban expansion of Yuzhong County in recent years, the prediction and prevention of geological disasters have become increasingly critical. This study employed three machine learning algorithms—Multiple Logistic Regression (LR), Random Forest (RF), and XGBoost (XG)—to assess landslide susceptibility in Yuzhong County. A total of 169 historical landslide points, supplemented by additional sites identified through field investigations, were compiled, along with 200 non-landslide locations. Multiple environmental factors were incorporated into the models to analyze landslide susceptibility across different areas. Because LR can effectively capture the generalized influence of precipitation variability, it was selected as the primary model for the final susceptibility mapping. To more accurately evaluate the impact of precipitation on landslide occurrence, average seasonal precipitation across the four seasons was used as a predictive factor. To refine the risk assessment at the township level, both raster-based and landslide-unit-based evaluation approaches were adopted. Overlay analyses were then performed by integrating urban infrastructure, population distribution, and predicted landslide hazard zones, while also accounting for the potential influence of extreme precipitation events. The results reveal that the mountainous areas in eastern Mapo Township, southern Xiaokangying Township, southern Xiaguanying Town, and the south-central part of Qingshuiyi Township are high-risk zones prone to group-occurrence landslide disasters.