Introduction

In southwest China, especially in Guizhou province, karst is the dominant geological feature. In the past few decades, this region has been subjected to catastrophic long-runout landslides triggered by earthquakes, heavy rainfall, and human activities (Chigira et al. 2010; Xu et al. 2010; Yin and Xing 2012; Xing et al. 2015, 2016; Li et al. 2017). On July 23, 2019, at 8:40 p.m., a massive landslide occurred in Shuicheng, Guizhou, China, resulting in more than 50 casualties as well as a massive loss to infrastructure in that area. The pre- and post-event images are shown in Fig. 1.

Fig. 1
figure 1

a Pre-event image of the Shuicheng landslide. b Post-event image of the Shuicheng landslide

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Distinctive efforts have been made to prevent and mitigate such kind of landslide hazard, and there is a significant reduction in the casualties caused by these disasters. To better understand the mechanisms involved in landslides, various methods are being used to analyze or simulate landslides, including seismic records, numerical simulation, and video records. Video records are the most perceptual method, but it is difficult to obtain, especially in remote areas (Zhu et al. 2019). In this paper, seismic record analyses and numerical simulation were conducted. Furthermore, their comparative analyses were done to determine the run-out behavior of the Shuicheng landslide.

Large landslides can generate seismic waves strong enough to be visible above the noise level that can be recorded by the seismic stations locally, even globally (Yamada et al. 2013). By comparing the analyses of the seismic records with the video record, the results indicate that each impact of the rockfall along the run-out path produces strong P-waves (Vilajosana et al. 2008). Many studies have been performed through seismic records, and different methods have been proposed to detect, locate, and monitor landslides through analyses of seismic records (Navarre et al. 2009; van Herwijnen and Schweizer 2011b; Van Herwijnen and Schweizer 2011a; Leprettre et al. 1996; Leprettre et al. 1998; Helmstetter and Garambois 2010). It is noted that machine learning method was used to develop a pattern recognition to detect avalanche from passive seismic data (Rubin et al. 2012).

Due to its unique ability to collect information, seismic records are widely used to interpret the process during landslides with a small time duration. Combining the seismic record analyses with field investigation, a detailed time history of the mass movement can be resolved (Allstadt 2013; Yamada et al. 2013; Moretti et al. 2015; Bai et al. 2019; Coe et al. 2016; Moran et al. 2008; Ward and Day 2006).

Numerical studies have been carried out to obtain a better understanding about the hypermobility of landslides, and some rational methods have been proposed to predict run-out behavior of landslides (Sassa 1988; Pastor et al. 2002, 2009, 2018; McDougall and Hungr 2004, 2005; Hungr and McDougall 2009; Hungr 1995; Denlinger and Iverson 2004; Cascini et al. 2016). Many studies have validated these methods through back analyses (Quan Luna et al. 2016; Liu et al. 2015; Dai et al. 2016).

In this paper, a thorough field survey was conducted to obtain the geological and climatic setting of the Shuicheng landslide and determine its main characteristics. The dynamic DAN3D model was used to simulate the run-out behavior of the landslide. For numerical analyses of the Shuicheng landslide, the most suitable rheological models and parameters were calibrated and validated according to the results of field investigation and seismic records. In addition, seismic records were analyzed to determine the location and the process of the Shuicheng landslide, and the results were compared with the numerical analyses. The results of the numerical simulation and seismic record analyses are expected to portray the process of a similar landslide in a better way, and hence improve the accuracy of landslide hazard mapping in the region.

Geological and climatic setting

The study area is located in the middle mountain-plain of the central part of Guizhou. The elevation ranges from 1180 to 2070 m at the bottom to top of the mountain, while the source area is at an elevation of 1658 m. There is an elevation difference of 890 m, and the average angle of the side slope is around 24°. The pre-event location of the Shuicheng landslide is shown in Fig. 2(a). There were two pre-event valleys shown in Fig. 2(b), providing the sliding mass with separate distinctive paths.

Fig. 2
figure 2

a Pre-event location of the Shuicheng landslide. b Pre-event image of the two valleys

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The exposed rocks in the study area range in age from Permian to Quaternary. It includes the Feixianguan Group sandstone and mudstone (T1f) of Early Triassic, the Xuanwei Formation (P3x), the Emei Mountain basalt (P2β) of Middle Permian, and Quaternary deposits (Q4al + pl) with a thickness of 0 to 20 m (Fig. 3). The landslide mainly occurred in the Emei Mountain basalt of Middle Permian.

Fig. 3
figure 3

Geological map of the Shuicheng landslide. a: Second part of Feixianguan Formation, Lower Triassic; b: first part of Feixianguan Formation, Lower Triassic; c: third part of Xuanwei Formation, Upper Permian; d: second part of Xuanwei Formation, Upper Permian; e: first part of Xuanwei Formation, Upper Permian; f: Permian basalt; g: landslide area

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The study area is located in the Yangzi Platform. The basic structural framework of the study area was formed due to the Yanshan tectonic uplift at the end of the Late Mesozoic Era and the regional plateau resulted from peneplanation by the end of the Miocene. Joints and fractures are extremely developed in the study area. The majority of the mountains in this area are distributed along the geological structure line that develops into deep, long, and V-shaped valleys. Furthermore, the undulations in the terrains are more organized in a parallel comb arrangement; landform erosion, dissolution-landform erosion, and karst landform are relatively more developed.

The study area has a plateau monsoon climate, with mild temperature and abundant rainfall. According to the data from the Meteorological Bureau of Guizhou province, the annual average temperature ranges from 11 to 17 °C. The annual average rainfall ranges from 940 to 1450 mm, primary precipitation occurs during the monsoon season which lasts from June to August. An intermittent rainfall occurred in Shuicheng county from June 1 till the evening of the event (July 23). From June 23 to July 23, the accumulated rainfall in the study area was 369.2 mm, of which the continuous heavy rainfall from July 18 to July 23 accumulated 287.1 mm. The rainfall recorded on July 22 was up to 98 mm. There were three heavy rainfall spells in Shuicheng county, just before the landslide event. These sessions occurred on the night of July 18, from the night of July 19 to 20, and on the night of July 22.

The groundwater in the study area can be divided into two types: fissure water in basalt and pore water in loose Quaternary deposits. The Quaternary deposits are mostly comprised of silty clay, which has a medium permeability. The pore water in the quaternary silty clay was mainly recharged from atmospheric precipitation. Due to the overtime development of the basalt joint fissures, a high yield of water fills the joint fissures in the basalt layer of the bedrock.

Shuicheng landslide

The Shuicheng landslide involved a total volume of 2 Mm3, including 1.6 Mm3 of jointed basaltic rock mass from the source area and 0.4 Mm3 of material entrained during transport. The Shuicheng landslide can be divided into three zones based on the field survey, namely source area, entrainment area, and deposition area.

Based on the field survey, a 3D sketch of the post-event Shuicheng landslide is presented in Fig. 4. The landslide dropped approximately 465 m in elevation and extended about 1250 m from the head scarp to the distal edge of the landslide toe. The landslide initiated as a rock slide in the large basalt rock mass with a strike angle of 20° to 26° and a dip of 50°, respectively. After detaching from its source area, the slide mass ran down rapidly. The displaced material was divided into two branches due to a collision with a basaltic ridge (Fig. 5) at an elevation of 1450 m. The separated sliding mass continued to travel along both banks of the valley, entraining sediments in the run-out path. At the deposition area, both branches of the displaced mass once again merged into each other.

Fig. 4
figure 4

Three-dimensional image of Shuicheng landslide

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Fig. 5
figure 5

UAV image of basaltic ridge

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The source area is located in the upper part of the roadway X244 at an elevation ranging from 1518 to 1658 m. The toe of the rupture surface is approximately located at an elevation of 1515 m. Before the landslide event, a house was located near the head scarp of the source area. When the moving mass detached, it also carried that house along with it. After covering a short distance, the remains of it were distributed along the run-out path. It can be determined by the remains of the house that the slope angle of the source area is approximately 50°. The source area has an estimated volume of 1.6 Mm3 and an average thickness of 20 m. After the event, three major joints sets are presented in the rear of the source area at 160°/26°, 300°–305°/50°–55°, and 136°/69°.

The entrainment area is located at a relief ridge at an elevation ranging from 1182 to 1518 m. The average slope angle of the entrainment area is around 35°. Two natural valleys surround the basaltic ridge (Fig. 5) provided the moving mass with two entrainment run-out paths. The right valley has a width of 130 to 150 m, and the left valley has a width of 140 to 170 m. Approximately 0.4 Mm3 of loose deposits was eroded and entrained during emplacement.

The deposition area is located at an elevation ranging from 1180 to 1200 m with an average slope angle of 0 to 10°. The displaced material was mainly deposited in the middle part of the deposition area due to the two branches of the moving mass collided with each other. It is estimated that the displaced material has a volume of 1.37 Mm3 by a comparison of pre- and post-event DEMs.

Another important thing to mention is that there were a lot of human engineering activities near the source area before the event. In November 2018, there was a widening of the X244 highway. Also, in July 2019, excavation works were carried out on the X244 highway. Those human activities could be one of the triggering factors of the landslide. InSAR was used to monitor the deformation of the X244 highway. From May 26 to July 7, 2019, InSAR results reveal no obvious deformation, which was further validated by the field survey. Hence, human activities are one of the factors triggering the landslide.

Numerical analyses

Dynamic model and parameters

In this paper, a dynamic model DAN3D, developed by Hungr and his colleagues (McDougall and Hungr 2004, 2005; Hungr et al. 2005), was used to simulate and analyze the behavior of the Shuicheng landslide. Based on smoothed particle hydrodynamics (SPH), the numerical model is used for the dynamic analyses of rapid flow slides, debris flows, and avalanches. To simulate different types of rapid landslides involving different geological materials, various kinds of basal rheological relationships were implemented in DAN3D, including the frictional and Voellmy model.

The frictional rheology assumes the resisting shear force (τ) to depend only on the effective normal stress (σ). The governing equation of the frictional rheology is expressed as:

$$ \tau =\sigma \left(1-{r}_u\right)\tan \varphi $$
(1)

where the pore pressure ratio, ru, and the dynamic basal friction angle, φ, are the rheological parameters of the frictional model.

The Voellmy rheology combines frictional and turbulent behaviors:

$$ \tau =\sigma f+\rho {gv}^2/\xi $$
(2)

where the friction coefficient, f correlates the shear stress with the normal stress, and the turbulence coefficient summarizes all the velocity-dependent factors related to the flow resistance.

However, the controlling process of the Shuicheng landslide is too complex to select an appropriate method beforehand. Frictional-Voellmy combined model has been found the most accurate basal rheologies through a trial-and-error procedure. The frictional model was employed in the source area, and the Voellmy model was used to simulate the behavior of entrainment and deposition material. The source area, entrainment, and deposition areas are displayed in Fig.6.

Fig. 6
figure 6

Outline of the simulation area. I Initiation area. II Entrainment-deposition area

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Back-analyses were performed to determine the most suitable rheological parameters for the simulation (Cuomo et al. 2016). A friction angle of 24° and a pore pressure ratio of 0.4 were selected for the frictional model. In the Voellmy model, a frictional coefficient of 0.2 and a turbulence coefficient of 900 m/s2 were adopted for the entrainment and deposition areas. Detailed rheological parameters are presented in Table 1.

Table 1 Models and rheological parameters for the Shuicheng landslide
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Simulated results

The results of the DAN3D simulation are presented in Fig. 7. The depth distribution at different time steps was plotted at 6-m intervals. The duration of the movement lasted approximately 60 s with only lateral expansion occurring in the following time. The final distribution of the deposited sliding mass ranges from 2 to 24 m, with the maximum thickness located at the end of the left valley.

Fig. 7
figure 7

Evolution of the Shuicheng landslide simulated by DAN3D

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On the basis of the field survey, the results of the simulation are thus consistent with the actual event in terms of the run-out distance and deposit distribution. At 15 s, the front edge of the slide mass collided with a dense and stable basaltic ridge at an elevation of 1450 m. Then the moving mass divided into two debris avalanche and moved forward along the valleys surrounding the basaltic ridge. The sliding mass moved entirely out of the source area in approximately 20 s, which is consistent with the result of the seismic signals. According to the maximum velocity image (Fig. 8), the mass moving on the right valley reached the deposition area first because the velocity of the right branch of the moving mass was higher than the left one. Both branches of the moving mass collided and merged into each other and gradually stopped at the deposition area.

Fig. 8
figure 8

Maximum velocity contour

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The plot of maximum velocity versus running distance is shown in Fig. 8. A high velocity of 24 m/s was recorded at the initial stage of the landslide. The maximum velocity is estimated at around 40 m/s at the right bank of the valley.

To further understand the erosion depth of the Shuicheng landslide, the depth of erosion is plotted in Fig 9. In the deposition area, the depth of erosion was less than 1.5 m. Erosion mainly took place in the entrainment area with an average depth value of 2 m. The maximum erosion depth is 4 m at a path distance of about 200 m at the right valley.

Fig. 9
figure 9

Erosion depth contour

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In terms of landslide evolution and entrainment, it is essential to highlight the similarities of the present study with the 1998 Cortadonica debris avalanche. In both events, the moving mass bifurcated into two valleys, and the two separated debris avalanches merged again in the deposition zone, to form a single large debris avalanche. In terms of entrainment, the findings of Shuicheng landslide numerical simulation indicate a maximum erosion depth in the middle part of the avalanche run-out path, which also correlates to the conclusion of the literature (Cascini et al. 2011).

Seismic records

Seismic signals of the nearby seismic stations revealed that, on July 23, 2019, an earthquake was detected in Shuicheng, Guizhou, China, with a focal depth of 0 km. However, a high energetic event like the Shuicheng landslide could generate strong seismic signals that were visible above the noise level at 15 three-component broadband seismometers throughout the Guizhou province. Due to the different characteristics and lack of a natural earthquake report of the event area, it can be determined that Shuicheng landslide was the source of those seismic signals.

Data and methods

From all the seismic signals available, those with the best signal quality were selected by visual inspection. Seismic signals from twelve stations were used to obtain the location of the event (Fig. 10), while data from YIP station was used to recover the process of the Shuicheng landslide. The seismic stations are shown in relation to the location of the landslide, using google map in Fig. 11 and tabulated details in Table 2.

Fig. 10
figure 10

Seismic signals (vertical component) recorded by near seismic stations

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Fig. 11
figure 11

Locations of the Shuicheng landslide (red star) and seismic stations (blue triangles)

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Table 2 Seismic stations used in the inversion
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The intersection method was performed to calculate the location of the event. Assuming seismic station as the center of the circle, while radius as the distance between the seismic station and the calculated location of the event, circles could be drawn based on the arriving time difference between the P-wave and S-wave. When the number of stations is greater than three, an approximate intersection can be obtained by the least square method.

Assuming that the focal depth of the landslide is 0 km, the binary non-linear regression model could be built based on longitudes and latitudes. It is expressed as:

$$ {t}_i={t}_0+\frac{d_i}{v_s}+{\varepsilon}_i $$
(3)

where ti is the time when a seismic station receives seismic signals, t0 is the moment when the event occurs, di is the distance between the location of the event and the seismic station, vs is the velocity of the seismic wave, and εi is the random error.

In this study, the Arias intensity was used to describe the variation of kinetic energy (Arias 1970). The governing equation of Arias intensity, IA, is expressed as:

$$ {I}_A=\frac{\pi }{2g}{\int}_0^{T_d}{a}^2(t) dt $$
(4)

where a(t) is the acceleration of the seismic signals, Td is the duration of the seismic signal, and g is the gravitational acceleration.

In order to better understand the pattern of the Arias intensity, the non-dimensionalization method was used by the following equation:

$$ {I}_{A- non}={I}_A/\left(\max {I}_A\right) $$
(5)

Seismic signals were analyzed by Empirical Mode Decomposition (EMD) method to obtain the intrinsic mode function (IMF). The amplitude-time-frequency spectrum was solved by the Hilbert-Huang transform method (Huang and Wu 2008) to obtain the seismic characteristics of the landslide.

For a landslide of 2 Mm3 volume, it can be simplified as a block of constant mass sliding down a slope (Fig. 12). Due to gravity and friction, the governing equation of this simplified model is expressed as:

$$ F= mg\sin \theta -f $$
(6)

where F is the magnitude of the slope parallel force, g is the magnitude of the gravitational acceleration, θ is the slope angle, and f is the frictional force. The magnitude of the frictional force on the block of mass is expressed as:

$$ f=N\cdot \mu $$
(7)

where μ is the apparent dynamic coefficient of friction, which accounts for both friction and the basal pore fluid pressures, and N is the magnitude of the normal force. The sum of forces in the direction perpendicular to the slope is zero because the landslide moves along the slope and does not accelerate into or out of the slope, so the magnitude of the normal force is equal to:

$$ N= mg\cos \theta . $$
(8)
Fig. 12
figure 12

Schematic diagram illustrating rock avalanche kinematics

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This means that the magnitude of the net force Fnet is equal to the magnitude of the slope parallel force F. In addition, the net force acting on the block is equal to its mass times acceleration, according to Newton’s second law. Eq. (6) can be rewritten as:

$$ {F}_{net}= mg\left(\sin \theta -\mu \cos \theta \right)= ma $$
(9)

where a is the acceleration of the moving mass. Then, the force on the Earth’s crust, Fe, can be obtained due to Newton’s third law:

$$ {F}_e(t)=- ma(t). $$
(10)

Two assumptions were made to invert the force-time function. Rather than the double-couple mechanism of earthquakes, the single force model is applied to the Earth’s surface (Kawakatsu 1989; Fukao 1995). It is assumed that the mass of the moving block is constant. Then, the displacement information (U) recorded by seismic stations can be calculated by the convolution of the force-time function with the Green's function (G):

$$ U(t)= Fe(t)\ast G(t)\ast I(t) $$
(11)

where I(t) is the station response. Then, Fe can be solved by using a discrete wave number Green’s function (Zhu and Rivera 2002).

Seismic record results

As it is shown in Fig. 13, the distance between the location of the event and different stations was calculated by the intersection method, and the results were processed by the least square method. The event was located at latitude 26°13′6.91″N and longitude 104°40′52.32″E, and results are in good agreement with the location of the landslide. The velocity of the surface wave between the monitoring stations and the location of the event is around 3.58 km/s.

Fig. 13
figure 13

0.01 ~ 0.1 Hz band-pass filtered seismic signals (vertical component)

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According to the result of the Arias intensity, in Fig 14(a), the peak acceleration was approximately 13 × 10−4 m/s2, and it lasted about 38 s. In Fig 14(b), before 70 s on the time scale, the change of Arias intensity was negligible. It is noted that the rate of the Arias intensity slope slowed down twice, at 88 s and 95 s. It is deduced that the first slump was at 88 s when the sliding mass collided with the basaltic ridge at an elevation of 1450 m. The second drop was at 95 s when the left branch of the separated sliding mass collided with the outer boundary of the valley and changed its run-out direction. However, there was no considerable change in the Arias intensity when the separated sliding mass collided and merged. The reason is that there was no impact force on the ground surface when the separated mass rammed into each other.

Fig. 14
figure 14

The intensity of the strong motion in UD direction (YIP)

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IMFs (Fig. 15) and the amplitude-time-frequency image (Fig. 16) were obtained through the Hilbert-Huang transform. In Fig. 16, high-frequency energy signal was shown during 60 to 80 s, and maintained a maximum level at 70 to 108 s. As shown in the image, there is an increase in the signal twice, from 70 to 81 s and 81 to 108 s. The low-frequency signal reached its peak during 90 to 95 s. High-frequency signal bottoms out from 108 to 120 s and then disappears from background noise after 120 s.

Fig. 15
figure 15

IMFs 1–13 of the velocity in UD direction from the YIP station

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Fig. 16
figure 16

The amplitude-time-frequency Hilbert spectrum

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The following conclusions were obtained through the amplitude-time-frequency Hilbert-Huang spectrum image. At 10 s after the initiation of the landslide, the detached mass has divided into parts. After 10 s, the moving mass entrained a large amount of pre-existing loose deposits. At 21 s, the moving mass transformed into a debris avalanche. Combined with the field investigation, it can be deduced that the moving mass collided with the basaltic ridge at an elevation around 1450 m and divided into two branches. The two pre-existing valleys provided the moving mass with distinctive paths and entrainment material. Starting from 30 till 35 s, two separated branches of the same debris avalanche collided and combined at the deposition area, which lies just below those two valleys. From 48 to 60 s, the motion of the debris avalanche was relatively slow and but it continued to accumulate mass. Eventually, the moving mass came to a stop at the deposition area.

The different layered velocity models were calculated on different paths between the monitoring stations, as shown in Table 3. The estimated force-time functions were obtained through the deconvolution of Eq. (11), as it is shown in Fig. 17. No visible pattern appears on the image. In order to validate the result of the force-time functions, we calculated the synthetic seismic signals by the convolution of the obtained force-time and Green’s function (Fig. 18). The results show that the synthetic seismic signals and recorded seismic signals are not in agreement with each other. It is deduced that the long-period seismic wave could not be separated from the background noise, leading to the instability of the deconvolution.

Table 3 The layered velocity models
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Fig. 17
figure 17

The force-time function for the Shuicheng landslide

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Fig. 18
figure 18

Synthetic (redlines) and recorded (blacklines) seismic signals

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Conclusions

On July 23, 2019, a large catastrophic landslide occurred in Shuicheng, Guizhou, China. The Shuicheng landslide was a rapid and long run-out landslide that caused 43 deaths and another nine missing. The displaced material traveled approximately 1250 m and descended approximately 465 m in height. The initial velocity of the slide mass is approximately 24 m/s. After colliding with the basaltic ridge at an elevation of 1450 m, the slide mass transformed into two separated debris avalanches eroding and entraining along the left and right valleys. The two separated branches of debris avalanche collided, merged, and stopped at the deposition area.

In this paper, the propagation and run-out behavior of the Shuicheng landslide were analyzed based on the DAN3D numerical simulation and the seismic signals recorded from the nearby seismic stations. Conclusions are made as follows:

  1. (1)

    The occurrence of the Shuicheng landslide is closely related to rock mechanical, geological, environmental conditions, and human activities. The main factor of triggering the Shuicheng landslide is heavy rainfall.

  2. (2)

    The DAN3D model was used to simulate the run-out process, and results showed that a combined Frictional-Voellmy model provides the best performance in simulating the Shuicheng landslide. The total duration of the landslide was 60 s with an average velocity of 20 m/s. A high initial velocity of 24 m/s was recorded at the initiation of the landslide, while the maximum velocity is estimated to be 40 m/s along the right valley adjacent to the basaltic ridge.

  3. (3)

    The seismic signals recorded by the nearby seismic stations were analyzed to calculate the location of the event and then interpret the run-out process of the Shuicheng landslide. Combining the results of the Arias intensity and Hilbert-Huang transform with the numerical simulation, a detailed time history of the Shuicheng landslide was obtained. The Shuicheng landslide had a duration of 60 s, comprised of three stages: the initial stage lasted for 10 s, the second stage was between 10 and 48 s, and the third stage had a time duration of 12 s. In the initial stage, the sliding mass detached from the source area with an initial velocity of 24 m/s and collided with the basaltic ridge at an elevation of 1450 m. The second stage lasted 38 s. It started when the sliding mass transformed into two branches around the basaltic ridge, entraining material along the two valleys. The third stage began at 48 s after the initiation of the landslide when the two separated debris avalanches collided and merged into each other. Solved by deconvolution of the displacement function with the Green’s function, the force-time functions appeared to be unstable due to the background noise. A comparative analysis between the synthetic and recorded seismic signals was conducted to verify the inaccurate behavior of the force-time function.