Dynamics of the Wulong landslide revealed by broadband seismic records

Abstract

The catastrophic Wulong landslide occurred at 14:51 (Beijing time, UTC+8) on 5 June 2009, in Wulong Prefecture, Southwest China. This rockslide occurred in a complex topographic environment. Seismic signals generated by this event were recorded by the seismic network deployed in the surrounding area, and long-period signals were extracted from 8 broadband seismic stations within 250 km to obtain source time functions by inversion. The location of this event was simultaneously acquired using a stepwise refined grid search approach, with an error of ~2.2 km. The estimated source time functions reveal that, according to the movement parameters, this landslide could be divided into three stages with different movement directions, velocities, and increasing inertial forces. The sliding mass moved northward, northeastward and northward in the three stages, with average velocities of 6.5, 20.3, and 13.8 m/s, respectively. The maximum movement velocity of the mass reached 35 m/s before the end of the second stage. The basal friction coefficients were relatively small in the first stage and gradually increasing; large in the second stage, accompanied by the largest variability; and oscillating and gradually decreasing to a stable value, in the third stage. Analysis shows that the movement characteristics of these three stages are consistent with the topography of the sliding zone, corresponding to the northward initiation, eastward sliding after being stopped by the west wall, and northward debris flowing after collision with the east slope of the Tiejianggou valley. The maximum movement velocity of the sliding mass results from the largest height difference of the west slope of the Tiejianggou valley. The basal friction coefficients of the three stages represent the thin weak layer in the source zone, the dramatically varying topography of the west slope of the Tiejianggou valley, and characteristics of the debris flow along the Tiejianggou valley. Based on the above results, it is recognized that the inverted source time functions are consistent with the topography of the sliding zone. Special geological and topographic conditions can have a focusing effect on landslides and are key factors in inducing the major disasters, which may follow from them. This landslide was of an unusual nature, and it will be worthwhile to pursue research into its dynamic characteristics more deeply.

Frequency Characteristics and Numerical Computation of Seismic Records Generated by a Giant Debris Flow in Zhouqu, Western China

“…Previous studies have shown that the seismic signals generated by landslides and debris flows can be divided into highfrequency components and low-frequency components. The high-frequency components are mainly caused by the friction between the sliding material and the surroundings and collisions within the sliding material (e.g., Ekström and Stark 2013;Yamada et al 2013;Hibert et al 2014); while the low-frequency component characterizes unloading and reloading processes of the crust caused by acceleration and deceleration of the sliding mass (e.g., Li et al 2017). The amplitude spectrum of the seismic signal generated by the Sanyanyu debris flow is shown in Fig.…”

“…The velocity model is derived from Crust1.0. In calculating synthetic seismograms of a landslide, the seismic source is generally assumed to be a point source at a fixed location (e.g., Ekström and Stark 2013;Hibert et al 2014;Li et al 2017;Huang et al 2018). This assumption is satisfied when the epicentral distance of the seismic stations is much larger than the spatial range of the landslide.…”

Variation patterns of landslide basal friction revealed from long-period seismic waveform inversion

“…In the recent decades, broadband seismic signals are more and more frequently used in landslide research. The applications include determining the time and location of a landslide (e.g., Lin et al 2010;Kao et al 2012;Chen et al 2013;Hibert et al 2014b;Levy et al 2015), quickly estimating the volume and slide distance (e.g., Dammeier et al 2011;Hibert et al 2011;Yamada et al 2012;Lin et al 2015;Chao et al 2016;Manconi et al 2016), and performing long-period waveform inversion to obtain force-time history of landslides (e.g., Moretti et al 2012;Allstadt 2013;Ekström and Stark 2013;Yamada et al 2013;Hibert et al 2014aHibert et al , 2015Moretti et al 2015;Chao et al 2017;Hibert et al 2017a, b;Li et al 2017Li et al , 2019bKääb et al 2018). Studies show that quantitative extraction and analysis of landslide seismic signals can help to explain the key stages and processes of landslides and understand their inherent mechanism and geological characteristics (e.g., Favreau et al 2010;Ekström and Stark 2013;Petley 2013).…”

“…Studies show that quantitative extraction and analysis of landslide seismic signals can help to explain the key stages and processes of landslides and understand their inherent mechanism and geological characteristics (e.g., Favreau et al 2010;Ekström and Stark 2013;Petley 2013). In addition, based on landslide force history inversion, landslide basal friction can be directly estimated considering a block model (Brodsky et al 2003;Allstadt 2013;Yamada et al 2013;Zhao et al 2015;Li et al 2017) or obtained from combined analysis with numerical landslide simulation (Moretti et al 2012(Moretti et al , 2015Yamada et al 2016aYamada et al , 2018Kääb et al 2018), providing a novel approach to estimate dynamic parameters of landslides.…”

Detectability of seismic waves from the submarine landslide that caused the 1998 Papua New Guinea tsunami

“…analyzed seismic waves generated by landslides (vols. of up to 13.6 × 10 6 m 3 ) caused by heavy rain in Japan Li et al (2017). used seismic data to analyze the dynamic process of a landslide (vol.…”