Size distribution law of earthquake-triggered landslides in different seismic intensity zones

Abstract

Abstract. The Ms 8.0 Wenchuan earthquake in 2008 and Ms 7.0 Lushan earthquake in 2013 produced thousands of landslides in the southern region of the Longmen Mountains in China. We conducted field investigations and analyzed remote sensing data to determine the distribution law of earthquake-triggered landslides. The results show a strong negative power law relationship between the size and frequency of landslides in VII, VIII, and IX seismic intensity zones, a weak power law in the X seismic intensity zone, and a lognormal distribution in the XI seismic intensity zone. Landslide density increases with increasing seismic intensity. A sandpile cellular automata model was built under the conceptual framework of self-organized criticality theory to simulate earthquake-induced landslides. Data from the simulations demonstrate that, with increasing disturbance intensity, the dynamical mechanism of the sandpile model changes from a strong power law to a weak power law and then to a lognormal distribution. Results from shaking table experiments of a one-sided slope sandpile show that, for peak ground acceleration (PGA) in the range of 0.075–0.125 g, the relation between the amount and frequency of sand follows a negative power law. For PGA between 0.15 and 0.25 g, the relation obeys a lognormal distribution. This verifies that the abovementioned distribution of earthquake-induced landslides should be a universal law from a physical viewpoint and may apply to other areas. This new perspective may be used to guide the development of an inventory of earthquake-triggered landslides and provide a scientific basis for their prediction.

Simulation of Long-period Ground motion: verification and sensitivity study on the 2013 Lushan Mw6.6 earthquake

“…The main direction of the maximum horizontal principal stress in the south section of the Longmenshan fault zone is NW (Meng et al 2015). Although the magnitude of the Lushan earthquake is lower than that of the Wenchuan Mw7.9 earthquake, the secondary disasters triggered by the Lushan earthquake can not be neglected due to the influence of terrain (Huang et al 2021).…”

Using short-interval landslide inventories to build short-term and overall spatial prediction models for earthquake-triggered landslides based on machine learning for the 2018 Lombok earthquake sequence

“…From the 1900s to the 2010s, much effort has been devoted to investigating the relationship between earthquake-triggered landslides and the possible impact factors (Keefer, 1984;Rodriguez et al, 1999;Mahdavifar et al, 2006). These studies have shown that the factors that have a great in uence on earthquake-triggered landslides are the topographical factors (Yagi et al, 2009;Yuan et al, 2015;Serey et al, 2019), the geological structure (Mahdavifar et al, 2006;Sato et al, 2007;Huang et al, 2009), the ground shaking parameters (Meunier et al, 2007;Nowicki et al, 2014;Huang et al, 2021) . Many scholars have used remote sensing images from before and after the earthquake to identify post-seismic landslides and generate earthquake-triggered landslide inventories (ETLIs) (Sato et al, 2007;Wartman et al, 2013;Valkaniotis et al, 2018;Ferrario, 2019).…”