An Improved Method for Computing Broadband Green’s Functions of Surface Sources and Its Application to Inverting the Processes of the 2017 Xinmo Landslide

Landslides are dramatic and complex surface processes that can result in extensive casualties and property damage. The broadband seismic signals generated by landslides provide datasets essential for understanding time-dependent sliding processes. However, traditional methods for computing Green’s functions based on wavenumber integration converge very slowly for surface sources, especially at high frequencies. Usually, long-period synthetic waves with a cutoff k-integral for an approximated near-surface source are adopted for landslide studies, which may lead to artifacts. Thus, the development of efficient methods for computing the broadband Green’s functions of surface sources is important. The generalized reflection and transmission method with the peak-trough averaging technique can overcome the difficulties in wavenumber integration for surface sources, quickly converging even for high-frequency calculations. We use this improved method to compute Green’s functions for surface single-force sources and invert the force histories of the 2017 devastating Xinmo landslide in different frequency bands. The results indicate that the complex sliding process of this drastic event can be revealed by broadband signals (0.02–0.5 Hz), and that the initiation stage of this event shows a dominant frequency up to 0.2 Hz.

Characteristics and Numerical Runout Modeling Analysis of the Xinmo Landslide in Sichuan, China

A catastrophic landslide hit Mount Fugui, Diexi Township, Mao County, Sichuan Province at 05:38:58 on June 24, 2017. This landslide buried Xinmo village, caused 83 deaths and resulted in enormous loss to people’s lives and properties. The Xinmo landslide was an earthquake-induced shattered mountain formed in the epicenter zone of the 1933 Ms7.5 Diexi earthquake (with an intensity of level X) and the strong motion zone of the Ms8.0 Wenchuan earthquake (with an intensity of level IX).The landslide mass cut out and slid from a high position, loaded continuously and accumulated at the top of the slope body. Subsequently, the landslide mass was transformed into avalanche debris, which clogged Songpinggou and thus formed a landslide dam, indicating a typical chain disaster of avalanche debris triggered by a ridge-top landslide. The total volume, elevation difference and horizontal distance of the landslide were 1637.6×104m3, 1200 m and 2800m, respectively. The authors of this study identified the disaster-formation mechanism of the Xinmo Landslide based on a field geological survey, remote sensing satellites and the other means. The authors analyzed the disaster characteristics of the landslide source zone, avalanche debris zone and accumulation zone, numerically simulated and comparatively studied the whole process of the Xinmo Landslide movement using DAN-W, i.e., dynamic landslide software, and multiple groups of rheological models. The research findings indicated that the friction model was able to favorably simulate the movement characteristics of various phases of the Xinmo Landslide; this landslide lasted approximately 120 s and had a maximum velocity of movement of 74 m/s. As a result, the friction model and its parameters can be used in similar studies on dynamic disaster effects of ridge-top rock landslides.

Diagnosis of Xinmo (China) Landslide Based on Interferometric Synthetic Aperture Radar Observation and Modeling

The Xinmo landslide occurred on 24 June 2017 and caused huge casualties and property losses. As characteristics of spatiotemporal pre-collapse deformation are a prerequisite for further understanding the collapse mechanism, in this study we applied the interferometric synthetic aperture radar (InSAR) technique to recover the pre-collapse deformation, which was further modeled to reveal the mechanism of the Xinmo landslide. Archived SAR data, including 44 Sentinel-1 A/B data and 20 Envisat/ASAR data, were used to acquire the pre-collapse deformation of the Xinmo landslide. Our results indicated that the deformation of the source area occurred as early as 10 years before the landslide collapsed. The deformation rate of source area accelerated about a month before the collapse, and the deformation rate in the week before the collapse reached 40 times the average before the acceleration. Furthermore, the pre-collapse deformation was modeled with a distributed set of rectangular dislocation sources. The characteristics of the pre-collapse movement of the slip surface were acquired, which further confirmed that a locked section formed at the bottom of the slope. In addition, the spatial-temporal characteristics of the deformation was found to have changed significantly with the development of the landslide. We suggested that this phenomenon indicated the expansion of the slip surface and cracks of the landslide. Due to the expansion of the slip surface, the locked section became a key area that held the stability of the slope. The locked section sheared at the last stage of the development, which triggered the final run-out. Our study has provided new insights into the mechanism of the Xinmo landslide.