the characteristics of the seismic signals induced by...
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Landslides (2017) 14:661–674DOI 10.1007/s10346-016-0714-6Received: 24 April 2015Accepted: 15 April 2016Published online: 25 April 2016© Springer-Verlag Berlin Heidelberg 2016
Zheng-yi Feng I Chia-Ming Lo I Qun-Fu Lin
The characteristics of the seismic signals inducedby landslides using a coupling of discrete elementand finite difference methods
Abstract Landslide seismic signals support researchers to esti-mate magnitudes and locations of landslides. They can serve as acrucial data for landslide warning systems. However, the random-ness of landslide locations makes the acquisition of landslide-induced seismic signals difficult and limits the number of availablefield data. The objectives of this study are to establish a numericalmodeling approach to examine the characteristics of seismic sig-nals induced by landslides and perform parametrical study. Thetwo-dimensional particle flow code (PFC) and Fast LagrangianAnalysis of Continua (FLAC) are coupled to simulate the landslideprocess. The force and velocity data at the coupled interfaces ofFLAC and PFC are transferred back and forth via a Socket I/Oconnection. Four locations were monitored for the induced verti-cal seismic signals, including velocity, acceleration, and stresshistories. The signals were analyzed by Hilbert-Huang transformto obtain the time-frequency spectrograms for examining thecharacteristics of the signals. The particle size, wall friction, parti-cle friction, and parallel bond of PFC input parameters wereparametrically investigated. The Xiaolin landslide in 2009 wassuccessfully simulated, and the characteristics of the seismic sig-nals were studied and compared with the data from a broadbandseismic station. These results demonstrate that terrain and transi-tion in the movement type of a complex landslide do influence theseismic signals. A landslide with larger rock particles generateslower-frequency content seismic signals. Also, there can be ap-proximately 40 s to escape before a large-scale landslide hits ifseismic instrumentation is installed. The method proposed canbe further applied for studies on many other large-scale rockavalanches to verify recorded signals and further correlate thesignals with the landslide characteristics.
Keywords Landslide . Seismic signal . Numericalmodeling . PFC . FLAC
IntroductionThe processes of a landslide event can be explained by the seismicsignals that they produce, a technique that has received wide-spread attention in recent years (Moretti et al. 2012; Yamadaet al. 2012; Ekström and Stark 2013; Petley 2013; Zhou et al. 2013).Researching the characteristics in such signals is a relatively newfield. Seismic signals enable researchers to estimate the magni-tudes and locations of landslides. They can even serve as a crucialdata for landslide warning systems.
The randomness of large-scale landslide locations makes theacquisition of onsite signals difficult and limits the number ofavailable field data. Even when seismic data can be taken, analyz-ing the seismic characteristics of landslides is a complex task.Thus, it is essential that an appropriate numerical simulationmodel be established to examine the characteristics of seismicsignals induced by landslides. This study proposes a coupling
approach for simulation of rock avalanches using particle flowcode (PFC; Itasca 2008) as a means to model the rock particlessliding, flowing, and collapsing. The traveling of the seismic stresswaves generated by the impact of rock particles on a slope issimulated in the finite difference model, Fast LagrangianAnalysis of Continua (FLAC) (Itasca 2011). Coupling PFC andFLAC models enables us to simulate and analyze the seismicsignals induced during landslides and to distinguish their features.This can be very helpful in investigating the characteristics ofseismic signals of landslides.
Numerous researchers, including Tang et al. (2009), Lo et al.(2011), and Chang et al. (2012a), have utilized two-dimensional orthree-dimensional discrete element models to simulate large-scalelandslide events in Taiwan, such as the Chiufenershan andTsaoling landslides in 1999 and the Xiaolin landslide in 2009.Considerable rock mass volumes involved in these events causeddeposits that formed the natural dams and blocking the rivers.Their kinetic behaviors vary corresponding to their types of land-slides. Previous studies have simulated and analyzed the landslideprocess and their hazardous zones. However, no numerical studysimulates seismic signals due to landslide. Most researchers, suchas Tommasi et al. (2008), Lo et al. (2011), Chang et al. (2012b), Tanget al. (2009), and Liu and Koyi (2013), just investigated the mor-phology of the resulting deposits rather than the seismic signals.
The use of PFC to simulate the sliding and flowing processes oflandslide mass enables researchers to record the impact force oneach wall element (note that wall elements are the boundaryelements of PFC model). However, the wall elements in PFC areunable to transfer energy to one another; only the compressiveloading of the landslide rock particles over wall elements can berecorded. As a result, we were unable to use wall elements tomonitor cycling seismic signals. Replacing wall elements with ballelements for the construction of strata at the bottom of the PFCmodel would significantly increase computational complexity, ul-timately making it impractical because the calculations for discreteparticles are considerably more time-consuming than those forcontinua. Moreover, the microscopic parameters of particle ele-ments are complex, from which it would be exceedingly difficult tosimulate the macroscopic parameters of actual stratum materials,let alone to simulate the transfer of vibrations between strata.
To overcome these difficulties, this study constructed the topboundary of the strata in FLAC and matched it to the wall elementboundary in PFC. This enabled us to use the commands in FLACto record vibrations of the ground surface. The resulting coupledmodel is capable of simulating the Xiaolin avalanche processincluding the stresses induced by impact of rock particles on theslope surface and the seismic wave signals that travel in the FLACmesh.
The Xiaolin landslide occurred on Mt. Xiandu in Jiaxian,Kaohsiung, Southern Taiwan, on August 9, 2009 (Tsou et al. 2011;
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Feng 2011; Lo et al. 2011). A portion of the slid mass coveredapproximately half of Xiaolin village at the foot of the mountain.Most of the debris moved down along the valley, damming theQishan River and forming a natural dam (Fig. 1a, b). The volumeof the landslide was estimated as approximately 25 million cubicmeters, with the traveling distance about 2.83 km at speed rangingbetween 20.4 and 33.7 m per second (Tsou et al. 2011). The dimen-sions of the resulting natural dam were approximately 53 m inheight, 350 m in width, and 600 m in length (Wu et al. 2013). Thebroadband seismic station (SGSB) near Jiaxian, which is 11.4 km toXiaolin landslide (Fig. 1a), recorded the seismic signals throughoutthe entire landslide incident, including velocity records of approx-imately 90 s. The seismic vibrations caused by the landslide wereidentical to those of an earthquake with a magnitude of ML 4.3(Feng 2011), which demonstrates the massive scale of the landslide.
By coupling FPC and FLAC, we are able to construct thenumerical model for landslide avalanche and performparametrical analysis to explain the kinetic process and seismiccharacteristics of the landslide. The simulated seismic signal wascompared with the Xiaolin landslide field data recorded by thebroadband seismic station (SGSB).
Method
Fast Lagrangian Analysis of Continua and particle flow codeFLAC (Itasca 2011) is based on the explicit finite difference method(FDM), and it can establish the relationship between force anddisplacement in a continuum system. The behavior of each zone(element) follows a linear or non-linear constitutive model andresponds to the restrictions of the applied forces and boundaryconditions. The force and velocity between nodes can be convertedusing a force equilibrium equation, the Gauss divergence theorem,and the Cauchy formula, thereby allowing the derivation of mo-mentum equilibrium for each node with time steps to createequilibrium in the system and accord with Newton’s second law
of motion. FLAC is useful to perform large-strain analysis forcontinuous media when large deformation encountered. FLAC isalso applicable to calculate engineering mechanics, particularlysuited to research on the mechanical behavior of soil and rockmaterials.
PFC2D (Itasca 2008) uses the discrete element method (DEM)to simulate continuous interactions and motions among spheri-cal particles in order to calculate their location and relativedisplacement and the amount of overlap of particles at each timestep. The force-displacement law of PFC can be used to calculatethe transfer and consumption of energy among particles as wellas the contact force between them. The updated speed andlocation of the particles can be obtained in accordance withNewton’s second law of motion. New contact points are auto-matically detected, and the contact points that are separated willbe eliminated during computation (Itasca 2008). González et al.(2002) indicated the following: (1) PFC can simulate the bondingand separation of rock, and (2) using the contact between wallelements and ball elements, PFC is able to simulate collisions andfriction in landslide behavior.
Coupling particle flow code and Fast Lagrangian Analysis of ContinuaThe explanation of the data transfer and the coupling schemebetween PFC and FLAC are shown in Fig. 2a (modified from Caiet al. 2007) and Fig. 2b (modified from Caudron et al. 2006). Thecoupling approach of PFC and FLAC is detailed stated in themanuals by Itasca, Consulting Group Inc (2008). The couplingtechnique had been used in many researches such as Cai et al.(2007), Caudron et al. (2006), and Zhou and Jin (2010)). Accordingto these literatures, we briefly explain the coupling approach asfollows.
The code PFC and FLAC are executed separately in a comput-er. FLAC (server) sends the velocities of the nodes on the coupledinterface to PFC (client). PFC sends FLAC the forces applied bythe parties on the wall elements. The two codes transfer data
ABCD
AB
CD
(a) (b)
Fig. 1 a Location of the Xiaolin landslide and the broadband seismic station SGSB at Jiaxian. The four monitored points A, B, C, and D in the numerical model are alsoshown (modified from Feng 2012). b The Xiaolin landslide and the location of the cross section with the four monitored points A, B, C, and D for the numerical simulation(modified from Tsou et al. 2011)
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back and forth via socket I/O connections (Fig. 2a). The coupledinterface is discretized as a series of wall elements in PFC. Eachwall element is corresponding to a single zone segment in FLAC.The nodes of PFC wall elements and FLAC zones shall be setexactly the same (x, y) coordinates. The movement of the wallelements in PFC is Bslaved^ to the movement of the zone seg-ments in FLAC. The large-strain mode shall be assigned in FLACfor the wall elements of PFC to keep track of the zone segmentmovement in FLAC. In the coupling calculations, the two codesare both run in dynamic configuration with an identical timestep. Data of the coupled interface should be exchanged at everycycle. During each cycle, FLAC firstly calculates one cycle andsends the node velocities of the coupled interface to PFC(Fig. 2b), thereby the wall elements move the same way as thecoupled interface of FLAC. Secondary, PFC calculates one cycle,updates the particle positions, and sends the resulting wall forcesto FLAC due to particle interaction with the wall elements. In thisway, the coordinates, velocities, and forces at the coupled inter-face of FLAC and PFC are exchanged and updated, and then, thecoupled simulation is achieved.
Hilbert-Huang transformThis study applied Hilbert-Huang Transform (HHT, Huang et al.1998) to perform time-frequency analyses for the seismic signals.We used the Visual Signal (AnCAD 2012) as a tool to perform HHTanalyses. The HHT contains two signal processing procedures:empirical mode decomposition (EMD) and the Hilbert transform(HT). EMD is able to extract intrinsic mode functions (IMFs) fromtime series in different signal frequencies. Each IMF is a simplenon-linear signal. If an HT is performed on IMFs, a time-frequency spectrum can be obtained. The HHT is very suitablefor non-stationary and non-linear signals; therefore, we used HHTto analyze the seismic signals induced by landslides. In addition,the average frequency and percent power of each IMF can becalculated.
Coupling model setupThis study collected a post-event (2010) 1 × 1 m DEM and a pre-event (2004) 5 × 5 m DEM provided by Aerial Survey Office,Forestry Bureau, Taiwan, to establish the numerical models. Inorder to reconstruct the Xiaolin landslide event, all of the
FLAC model(server)
PFC model(client)
Send forces on wall elementsGet forces
Send velocities of the coupled interface nodes
Get velocities
Socket I/Oconnections
(a)
PFC model
FLAC model
PFC model
PFC calculates one cycle, updates the particle positions and sends the resulting wall forces to FLAC due to particle interaction with the wall elements.
FLAC calculates another cycle for displacements, updates the node positions and sends the new node velocities of the coupled interface to PFC.
FLAC model
And so on
(b)
FLAC calculates one cycle and sends the node velocities of the coupled interface to PFC.
. . .
Fig. 2 a Data transfer between FLAC and PFC via a socket connection (modified from Cai et al. 2007). b the FLAC/PFC coupling scheme (modified from Caudron et al. 2006)
Landslides 14 & (2017) 663
precision of the terrain data must be consistent. Therefore, for thesliding mass in the numerical model, we adopted a post-event(2010) 5 × 5 m DEM converted from 1 × 1 m DEM, and for thedeposition area, we used a pre-event (2004) 5 × 5 m DEM. Thenumerical model also selected an analysis profile from DEM, thelocation of which is exhibited at X-Y in Fig. 1b.
The PFC numerical model with a local detail of the landslide’ssource area is provided in Fig. 3. The PFC model boundary, thewall elements, contains 160 line segments formed by the 161 nodesthat share the same coordinates as the 161 nodes of the topboundary of the FLAC zones. The sliding body is shown in redcolor.
We consider Highland 590 and the terrace in the PFC model(Fig. 3), and it is explained as follows. According to the topograph-ical analysis of Xiaolin area (Lo et al. 2011), many hummockyterrains in the source area and Highland 590 were formed due toslope movement or deformation. The time scale of these deformedphenomena might be over a hundred years (Fig. 4a–d). In 1936,parts of the colluvium deposits appeared on the upstream gully ofthe right bank and formed a hummocky surface. Furthermore, thecolluvium of various events created Highland 590 on the top of ahigh-level terrace and diverted the river course. In 1996, most ofthe hummocky surface in the source area (the main landslidemass) and Highland 590 still existed and even expanded.Highland 590 finally collapsed under Battack^ from the Xiaolincatastrophic landslide. The hummocky terrain was eradicated andtransformed into the smooth terrain (the elevation changed from290 to 270 m). In 2009, parts of the colluvium were still depositedin upper reaches of Unnamed Creek A; most of the debris, how-ever, were deposited on the high-level terrace and at thedownstream.
A number of points are worthy of note here. First, most of thehummocky surface in the source area was a result of buried byupstream gullies before 2009. This is indicative of gravitationalslope deformation processes and convincing evidence of landslidedeposition on the source area before the Xiaolin landslide. Second,Highland 590 was formed by deposition of colluvial materials ontop of the high-level terrace and consequently deflected the rivercourse. It is plausible that landslide events smaller than Xiaolinlandslide had deposited colluvial materials on the top of the high-level terrace. These findings were considered to reconstruct thepre-collapse area (Fig. 3). Third, high potential energy of the hugeamounts of slide materials was sufficient to generate tremendousdynamic energy that was high enough to cause collapse of
Highland 590, the river terrace, and to trigger a debris avalanchethat overflowed the high-level terrace (Lo et al. 2011). Therefore,the numerical model for the Xiaolin landslide involved threemajor portions, namely the main landslide body, Highland 590,and the terrace (Fig. 3). We simulated the sliding mass using 9532ball elements with a radius of 0.5–2.0 m. In the source area, pre-landslide terrain was constructed using wall elements with ballelements to fill the main landslide source, Highland 590 and theterrace. We used wall elements in PFC model to form these threecollapse regions and inserted the particles uniformly into the threeregions. Four monitoring locations were also established to mon-itor vertical forces.
Based on the difference in elevation between the digital terrainprofiles before and after the Xiaolin landslide, we established theFLAC mesh for the Xiaolin landslide as shown in Fig. 5. The FLACmesh was setup as 4800 m× 5500 m. The coordinates of the nodesof the coupled interface of the FLAC mesh were set exactly thesame as the wall elements of the PFC model in Fig. 3 as aforemen-tioned. A local detail of the coupling of PFC and FLAC boundariesis shown in the inset plot in Fig. 5. The zones of the FLAC meshwere approximately 30 m× 30 m. We set up the free field bound-aries, one kind of absorbing boundaries (Itasca 2011) on left andright sides of the model to prevent seismic waves reflecting fromthe left and right model boundaries. The bottom boundary of theFLAC mesh was assigned fixed condition to provide reactions tosupport the weight of the ground. However, there were still somelittle wave reflections from the bottom boundary. We thereforebuilt the mesh quite deep as 5500 m and assumed 8 % Rayleighdamping during dynamic computation to reduce the effect of wavereflection from the bottom boundary. The FLAC model mustachieve static equilibrium prior to coupling dynamic analysis.
Due to PFC limitation, we can only monitor the compressivecontact force on wall elements; we established four monitoringpoints (A, B, C, D) in the FLAC model as shown in Figs. 1 and 5,which monitored the velocity, acceleration, and stress all in verti-cal direction at points A, B, C, and D within FLAC. Monitoringpoint A is situated in the central region of the primary landslidemass, B is located at the toe of the primary landslide mass, C is atHighland 590, and D is situated at in the primary deposit area. Thepurpose is to obtain the seismic signals of the four locations forfurther time-frequency analysis and for comparisons.
We assumed that the strata materials were isotropic and linearelastic. The material parameters required for the strata in FLACincluded density, bulk modulus, and shear modulus, as listed inTable 1. For simplification, groundwater is not considered in thisstudy. For the material parameters of the modeled shale formationin PFC, we referred to the uniaxial compression test resultsobtained by Lo et al. (2011) and utilized PFC to simulate the biaxialtests. Using normal stiffness and shear stiffness that we derived aswell as the normal stiffness suggested by Potyondy and Cundall(2004), we converted and corrected the macroscopic and micro-scopic parameters to obtain the particle bonding strength andcontact stiffness required for the numerical simulations as shownin Table 2.
To deal with material collision and energy dissipation, wereferred to the normal and shear rebound coefficients derivedby Giani et al. (1994) for analysis of various slopes and theviscous damping coefficient used by Lo et al. (2011) to simulatethe Xiaolin landslide, as shown in Table 3. These references
0 1000 2000 3000 4000
500
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1500
(m)
The terraceHighland 590
The sliding body
Fig. 3 The PFC Xiaolin landslide model—with particles and wall elements
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Landslides 14 & (2017)664
allowed us to select a viscous damping ratio well suited to the siteconditions.
The selection of a kinetic friction coefficient is important be-cause the coefficient is crucial to the kinetic processes involved inlandslides. Nonetheless, landslide particles are not uniform in sizeand the coefficient of kinetic friction can change continuouslyduring the landslide, making such coefficient difficult to assign.For this reason, we observed the deposit profiles resulting fromdifferent friction coefficient assumptions and compared them withthe post-disaster terrain to adjusting the appropriate coefficients.
We define the simulation results using the above model as-sumptions as the Bbaseline^ case for further parametrical studyand comparison.
Result and discussionThrough the above PFC/FLAC coupling calculations for the base-line case, the seismic signals induced during the landslide processwere successfully simulated. We recorded the seismic signals of thevertical velocity and vertical acceleration at the four monitoringpoints as well as the vertical stress variations in FLAC model.
Figure 6 shows the vertical stress histories of the four moni-tored points from PFC model. It reflects the stresses applying onthe four points in FLAC during the landslide mass slides/impactsand debris flowing down the slope. We can also observe that theimpact stress of point B (toe of the source area) can be as high as6 MPa explaining the strong kinematic impact on ground thatcould destroy everything on the path, no wonder that theHighland 590 was scoured during the landslide. When the debrishas passed points A and B, their vertical stress reduced to lowstress level. Also, the vertical stresses due to the overburden of theslid masses after the landslide stopped are around 1.6 and 1.7 MPafor points C and D, respectively.
Also from Fig. 6, the vertical stresses of points C and D showthe circumstances of masses flowing down the slope that withsmaller stress amplitude and higher vibration frequency. In con-trast, larger stress amplitude and lower vibration frequency thatcan be observed for points A and B infer that rupturing andseparating process occurred between 6 and 12 s at point A andstrong impacting during 14 to 34 s at point B. The frequency ofvibrations associated with the impacting of slid masses is lowerthan that of the debris flow.
The shear loading induced by the movement of the landsidebodies may be partially transferred from the wall elements of PFC
Fig. 4 a–d The development process of hummocky surface with river course of Unnamed Creek A at different periods (modified from Lo et al. 2011). a 1904. b 1936. c1996. d 2009
Fig. 5 The mesh of the FLAC numerical model—the FLAC mesh is formed by 160-column-by-160-row zones (totally 25,600 zones), and each element is about 30m × 30 m. The PFC model in Fig. 3 is plotted on top of the FLAC mesh boundary forvisual illustration
Table 1 The material properties for the FLAC numerical model
Item Experimental results
Unit weight 2600 (kg�m3 )
Elastic modulus (Ec) 4.8 GPa
Bulk modulus (K) 4.7 GPa
Shear modulus (G) 1.8 GPa
Poisson’s ratio 0.33
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to the zones of FLAC through the interface during the couplingcalculation already. This can be further studied in future work.
The vertical velocities of the four points of the baseline casemonitored by FLAC are shown in Fig. 7a, and HHT was applied toderive the time-frequency spectra. Correspondingly, the simulatedaccelerations and the time-frequency spectra are shown in Fig. 7b.Based on the data shown in Fig. 7a, b, we can roughly suggest thatthe separation, rupture, and impact of the sliding masses occurredat approximately 6 to 34 sec after the beginning of the landslide.The largest velocity amplitudes appeared during this period, there-by again indicating that the velocity amplitudes of impact andseparation at the source were greater than those of the debrisflowing down the slope.
Large simulated velocity and acceleration are noticed at thefour monitoring points in Fig. 7a, b. The explanation is that thePFC particle elements impact the ground surface directly andcaused the large velocity and large acceleration. For example, ifwe use a hammer to hit the ground where it is very close to anaccelerometer, the accelerometer will show a very high accelera-tion of the impact. The acceleration of direct impact on a surfaceby an object is much higher than the acceleration of earthquakepropagated from far field.
In addition, the average velocity of the landslide of Xiaolinlandslide is estimated as high as 20.4~33.7 m/s (Tsou et al. 2011).Under such impact of high speed directly on the ground surfaceand by large rock masses, the impact energy is very high and itcould have scoured the Highland 590 as above mentioned.
Therefore, we believe that the calculated large seismic velocityand acceleration in this study are quite reasonable. The accelera-tion data in the Fig. 7b even show that the highest acceleration atpoints A, C, and D is around 1 g, and the highest acceleration atpoint B is up to 2 g at an instantaneous time. In addition, thegeological materials are modeled using linear elastic constitutivelaw and that may cause a little bit larger velocity.
Broadband Station SGSB at Jiaxian recoded the seismic veloc-ity signals during Xiaolin landslide as shown in Fig. 8. The signalis compared with the signal from point C. The spectra are similarin that the spectra can be approximately enclosed by a triangleshown as the dashed curves in the spectrograms (Fig. 8). Thisfinding is similar to the time-frequency spectra obtained by Feng(2011) and Feng (2012). Furthermore, we can also see that theenergy distributions of the velocity and acceleration signals areall approximately triangular in the time-frequency spectrograms(Fig. 7a, b).
We analyzed the average frequency and percent power of eachIMF of the signals of point C and Station SGSB as shown inTable 4. The sum of the power of IMF 6~10 is 85.14 % for pointC. The frequencies range from 0.37 to 4.51 Hz. IMF 9 contains thelargest power 32.2 % with an average frequency of 0.57 Hz. Thesum of the power of the IMF 5~6 is 84.18 % for Station SGSB. Therange of the frequencies is 0.55 to 0.97 Hz. IMF 5 contains thelargest power 61.83 % with an average frequency of 0.97 Hz.
The frequency content of the seismic signal of point C is muchhigher than that of Station SGSB. The is reasonable because the
Table 2 Microscopic mechanical properties of the particles in PFC
Item The numerical parameters ofthe compression test model
The numerical parametersof the landslide model
Particle density (kg�m3) 2600 2600
Range of particle radius (m) 0.047~0.0625 0.5~2
Normal stiffness (N=M) 2.5e11 2.5e10
Shear stiffness N=M 1.25e11 1.25e10
Range of particle friction coefficient 0.5 0.05~0.35
Friction coefficient of wall 0.5 0.5
Normal strength of parallel bonds (MPa) 16 16
Shear strength of parallel bonds (MPa) 8 8
Normal stiffness of parallel bonds (Pa=m) 2.8e12 1.25e8
Shear stiffness of parallel bonds (Pa=m) 1.4e12 6.25e7
Viscous damping ratio of normal direction 0.4 0.4
Viscous damping ratio of shear direction 0.2 0.2
Table 3 The relation of restitution coefficient and damping ratio by rock fall test in the field
Land cover types Normal restitutioncoefficient
Converted normaldamping ratio
Shear restitutioncoefficient
Converted sheardamping ratio
Bedrock slope 0.50 0.21 0.95 0.02
Bedrock slope covered with broken rock 0.35 0.32 0.85 0.05
Slope covered with rock debris and soil 0.30 0.36 0.70 0.11
Soil slope covered with lush vegetation 0.25 0.40 0.55 0.20
Modified from Giani et al. (1994)
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distance from Station SGSB to Xiaolin landslide is 11.4 km and thehigh-frequency portion of the signal damps out much faster thanthat of low frequency. In other words, the actual landslide signalsat SGSB were far-field recordings, whereas the simulation resultsin this study were of near field. Therefore, the high-frequencysignals in the near-field simulation of seismic velocity still existed,and low frequencies are more apparent in the actual records, inwhich the high-frequency portion was attenuated. Again, due tonear-field simulation, the large velocity amplitudes were obtained.
In Fig. 8, IMF 9 and IMF 10 are low-frequency signals with longperiods. These low-frequency signals were generated as the slidebegan, which is consistent with the findings obtained by Yamada
et al. (2012), who collected broadband seismic records of theAkatani landslide from the NOKF station. Yamada et al. (2012)filtered and divided the original signal into low-frequency(<0.1 Hz) and high-frequency (>1 Hz) waves. The larger ampli-tudes of the former occurred approximately 20 s earlier than thoseof the latter. Yamada proposed that the long-period waves weregenerated by the collapse at the beginning of the slide, rather thanby the later mass flowing.
Relationship between landslide process and seismic signalsTo discuss the relationship between velocity waveforms of the fourmonitoring points and the kinetic circumstances of the landslideprocess, we present the waveforms of the four points simulta-neously and the five snap shots of movements at timings: 10, 30,50, 70, and 90 s in Fig. 9a–e. The corresponding characteristics ofthe five stages are discussed as the following:
1. Stage 1 (0~10 s): This is the initial rupture and separation stageof the landslide process. The sliding mass on the source areahad ruptured, collapsed, and begun sliding. The frequency ofvibration during this stage was low, thereby low-frequencyseismic signals appeared during this stage. A comparison of
Pt. APt. BPt. CPt. D
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Fig. 6 The vertical stress vs time at the four monitored points in the FLAC model
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Fig. 7 a The simulated vertical velocity from FLAC simulation and the time-frequency spectra of the baseline case. b The simulated vertical accelerations from FLACsimulation and the time-frequency spectra of the baseline case
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Fig. 7 (continued)
Fig. 8 Data comparison of the vertical velocity, IMF, and time-frequency spectra of point C and those of Broadband Station SGSB
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Landslides 14 & (2017)668
the mass movement in Fig. 9a and the seismic signals at thefour monitoring points reveals that at 10 s, the centroid of thesliding body passed monitoring point A, and the Bfront^ of thesliding body just passed point B. Therefore, the vibrationvelocity was very large at point A and was beginning to in-crease at point B. In contrast, the velocity measured at point C,which is set at Highland 590, was relatively lower than thosemeasured at points A and B. At point D, which is located onthe terrace, significant seismic signals had yet to be detected.This demonstrates that the location of the landslide mass hasconsiderable influence on the seismic signals measured at themonitoring points. When the sliding mass passed point A,small amplitude of seismic signals can be detected at pointsC and D, which are approximately 1 km downslope. Thispreceded the arrival of the landslide mass by approximately35~40 s, which can be applied in landslide warning for largelandslide in the future.
2. Stage 2 (10~30 s): This is the severe impact stage. At approx-imately 20 s, the main sliding mass impacted the ground nearpoint B and eroded Highland 590 (point C) producing aconsiderable amount of kinematic impact energy. As a result,at point B, the largest signal amplitudes appeared approxi-mately at 20 s. At approximately 30 s, most of the sliding masshad disintegrated and the sliding mass was mainly betweenpoints B and C (Fig. 9b), gradually moving away from point A.Therefore, the velocity amplitudes measured at point B werethe largest, followed by those at point C. The velocity ampli-tudes measured at point A were beginning to decline, andnoticeable velocity amplitudes were beginning to be detectedat point D. Again at 20 s, the seismic velocities measured at
point B peaked, and the sliding mass was just leaving thegentler slopes at the source area (approximately 15 to 25°)and entering the steeper slopes (approximately 35 to 40°).Due to the steeper terrain, the kinetic impact energy couldhad increased, thus caused large velocity amplitude.
3. Stage 3 (30~50 s): This is the transitioning stage from severeimpact to debris flow. At 38 s, the centroid of the sliding masspassed point C; at which timing, the velocity peaks were mea-sured at point C. The sliding mass had already left point A, andthe seismic velocities detected there were the lowest, followedby those at point B. Relatively higher velocities were measuredat points C and D. Furthermore, the gentle terrain of theground surface caused the type of the movement to graduallytransform from collapsing (impacting) into flowing. As a re-sult, the overall seismic velocities measured at points C and Dwere lower than those at points A and B. This demonstrates theinfluence of terrain and changes in the movement type oflandslides on seismic signals.
Table 4 Comparison of the average frequency and percent power of the IMFs ofPt. C and Station SGSB
IMF Avg. freq.of Pt. C(Hz)
Power(%) ofPt. C
Avg. freq.of SGSB(Hz)
Power(%) ofSGSB
IMF1 105.03 0.00 21.18 0.16
IMF2 56.15 0.00 7.65 0.46
IMF3 31.86 0.05 3.84 1.04
IMF4 16.96 1.44 1.65 5.21
IMF5 9.00 8.99 0.97 61.83
IMF6 4.51 13.82 0.55 22.35
IMF7 2.57 13.35 0.30 5.04
IMF8 1.25 17.77 0.18 3.12
IMF9 0.57 32.20 0.12 0.43
IMF10 0.37 7.99 0.10 0.16
IMF11 0.21 2.89 0.02 0.20
IMF12 0.10 0.41 – –
IMF13 0.04 0.84 – –
IMF14 0.04 0.24 – –
Sum the power of IMF 6~10 is 85.14 % for Pt. C. Range of the frequencies is 0.37~4.51Hz. The IMF 9 contains the largest power 32.2 % with an average of 0.57 Hz. Sum of thepower of the IMF 5~6 is 84.18 % for Station SGSB. Range of the frequencies is0.55~0.97 Hz. The IMF 5 contains the largest power 61.83 % with an average of 0.97 Hz
AB
CD
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Fig. 9 Landslide mass movement and the vertical velocity of the four monitoredpoints. a 10 s. b 30 s. c 50 s. c 70 s
Landslides 14 & (2017) 669
4. Stage 4 (50~70 s): This is a flowing stage. The sliding mass hadalready left points A and B and was just passing point C. Theseismic signals at points A and B were already very weak dueto energy dissipation and away from the sliding mass. Whenthe centroid of the sliding mass passed monitoring point D (atapproximately 60 s), higher seismic velocities were detectedthere. However, as the primary movement type had becomeflowing, the overall seismic velocities obtained at point D werelower than those measured at the other three monitoringpoints. At 60 s, the front of the sliding mass had arrived atthe terrace (i.e., Xiaolin Village) and started to erode and burythe terrace. This reconstructs and explains the burying event ofXiaolin village during Typhoon Morakot in 2009. Over 400people were missing (Feng 2011; Lo et al. 2011).
5. Stage 5 (70~90 s): This is the deposition stage. The sliding massgradually came to a stop and deposited on the river down-slope, blocking the river and forming a natural dam. Theseismic signals gradually die out at 90 s, indicating that theentire landslide process had finished at this point. Only a verysmall portion of the landslide mass was still depositing, caus-ing some very weak seismic signals to be picked up at points Cand D.
A comparison of the seismic signals received at the fourmonitoring points revealed that the duration of significant seis-mic signals was shortest at point A, which was situated in thesource area in which the terrain was generally steeper. This showsthat when a large landslide occurs in a steeper terrain, the slidingmass moves downslope more quickly, thereby contributing to ashorter-duration seismic signal. Point B is located where thesteepness of the terrain changed most drastically, and as a result,the seismic signals detected there changed radically. At 20 s, inparticular, the impact of the sliding mass on the steeper slopeinto the relatively gentler Highland 590 generated the greatestseismic signals of the entire series, which then quickly droppedoff in seismic amplitude. The velocity amplitude variation herewas most significant at point B. Point C is situated near Highland590, where the slope of the terrain was gentler (approximately 8°to 13°). At this timing, movement type transformed fromcollapsing/impacting to flowing; therefore, the seismic signalspicked up at point C were less volatile than those detected atpoints A and B. Point D is located at the cliff of the terrace, andthe nearby terrain was relatively level. When the sliding masspassed point C (Highland 590), the sliding mass almostdisintegrated as debris and flowing to the terrace downslope.The seismic signals detected at point D were the weakest butpersisted for the longest duration.
Parametrical studyThe ability of real landslide movement is controlled by velocity-related friction and dragging forces. Different resistances willcause the speed of landslide to vary. The runout distance is possi-bly related to a combination of various kinematic parameters(Steven and Simon 2006). The frictional property of the rupturesurface is decisive for conducting the numerical simulation of alandslide. This study varied a range of the wall friction coefficients(0.1, 0.5, and 1) and particle friction coefficient (0.08, 0.2, and 0.5)to discuss the influence of the friction coefficient of wall andparticle elements on the seismic signals. The Bwall friction^ stands
for the friction coefficient between wall elements and particleelements. The wall friction coefficient of the walls was equivalentto the coefficient of static friction, which we set at 0.5 in thebaseline case. The bond strength of micro-parameters is mainlyrelated to macro-uniaxial compression strength, since the uniaxialcompression strength of in situ rock mass is usually weaker thanthat obtained from the triaxial test of rock specimens (Hoek andBrown 1980). Hence, three bonding strengths (parallel bond) wereadopted as 4, 16, and 60 MPa to represent different degrees ofsliding mass strength and to compare their influence on theseismic signals. The results of parametrical study on particle size,wall friction, particle friction, and parallel bond strength are ex-plained as follows.
Particle sizeAccording to the results of the field survey, the diameter of thedeposited materials at Highland 590 and Qishan River is 6–11 m(accounting for about 68 %) and 0.5–6 m (accounting for about53 %) respectively. Therefore, a parametrical case was performedfor assuming the radiuses of the particle elements ranging from 3to 4 m. The other numerical parameters were kept the same as thebaseline case to investigate the influence of the particle size on thefrequency content and amplitude of the monitored seismic signals.The radiuses of the particle elements of baseline case were rangingfrom 0.5 to 2 m. The velocity signal and the time-frequency spectraof point B were selected for observation as shown in Fig. 10.Compared with the plots of point B in Fig. 7a, more lower-frequency signals appeared in the spectrum of this case than thatof baseline case; i.e., larger particle size would produce lower-frequency signals than those of the smaller particle size. In addi-tion, the velocity amplitude increases almost double, illustratingthat stronger energy was produced by the impacts of largerparticles.
Wall friction coefficientIn this parametrical analysis of the wall friction coefficient, weobserved the differences in flow distance, deposit distribution, and
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Fig. 10 The vertical velocity and spectrum of the case of larger particle radius sizeof 3~4 m (Pt. B)
Original Paper
Landslides 14 & (2017)670
flow velocity with the wall coefficient of 0.1, 0.5, and 1 fromFig. 11a–c. For wall friction coefficient set as 0.5, it led to asignificant increase in runout distance and thicker depositiondeposited on the terrace and river (Fig. 11a). With a wall frictioncoefficient of 0.5 (baseline case), the debris piled up between theterrace and Highland 590. For wall friction coefficient set as 1, itcaused the rock debris to accumulate near Highland 590.
We compared the velocity signal of point B for different wallfriction coefficient (Figs. 11b and 7a). The timing of the velocitypeaks resulting from a wall friction coefficient of 0.1 was approx-imately 2 s earlier than that in baseline case. In contrast, thevelocity signal resulting from a wall friction coefficient of 1(Fig. 11b) and a coefficient of 0.5 (Fig. 7a) did not present signif-icant differences.
In a comparison of the three wall friction coefficients above, weconclude that with smaller wall friction coefficient, the landslidemass travels farther and its traveling velocity is higher.
Particle friction coefficientWe changed the friction coefficient of the particles to 0.15 and 0.5and compared the results with those of baseline case, in whichthe friction coefficient of particles was 0.08. The friction coeffi-cients of the particles at Highland 590 and the terrace remainedthe same as 0.08. The results (Fig. 12a) demonstrate that as thefriction coefficient increased, the flow distance of the landslidewas reduced. This characteristic is similar to that of varying wallfriction coefficient. However, unlike the wall friction coefficient,the contacts among particles are much more frequent than be-tween particles and the wall elements. When ball friction coeffi-cient was increased to 0.5, the sliding body would have beenstable and it would not slide (Fig. 12a). In other words, if thefriction coefficients for the particles and the wall were assignedboth 0.15, then the flow distance resulting from the particlefriction coefficient of 0.15 would be shorter than that from a wallfriction coefficient of 0.15.
Figure 12b exhibits the seismic signals and time-frequencyspectra of a particle friction coefficient increased to 0.15. As thecoefficient of friction increases in the particles, it becomes increas-ingly difficult for particles to move and, therefore, the seismicvelocity and frequency drop. If the particle friction coefficient wereeven increased to 0.5 (Fig. 12c), then the landslide body would bealmost stable, which explains why the signals and the time-frequency spectrum are barely visible.
Parallel bond strength between particlesRock particles can be lumped into rock mass by using the parallelbond assignment. The strength of parallel bonds influences theinstantaneous fracturing and separation in the lumped rock mass.If the strength of the bond is weak, it results in the movement ofrock mass in the form of particles. According to the results of theuniaxial compression test by Lo et al. (2011), the Young’s modulus
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Fig. 11 a Comparison of landslide deposits for different wall friction coefficients. bThe vertical velocity and spectrum of the case of wall friction coefficient 0.1 (Pt. B).c The vertical velocity and spectrum of the case of wall friction coefficient 1 (Pt. B)
Pt. B
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Fig. 11 (continued)
Landslides 14 & (2017) 671
(E) of Yenshuikeng Shale (the main landslide material) was4.8 GPa and the uniaxial compression strength (UCS) was16 MPa. The UCSs of the colluvium and Tangenshan sandstoneare 3–4 and 42 MPa, respectively. In this study, we divided parallelbond strength into three magnitudes: weak (4 MPa), moderate(16 MPa; baseline case), and strong (60 MPa). From Fig. 13a,parallel bond strength exerts an influence on the breaking of thelandslide body and the spread zone of the landslide. Strongerparallel bonds make it more difficult for the rock mass to breakor separate. For this reason, we can see many large lump massdeposited between Highland 590 and the terrace for the strongbond case. The greater the parallel bond strength is, the larger thelump mass pile up. In contrast, weaker parallel bonds would mean
that the sliding body breaks up into individual particles moreeasily and forms a more leveled colluvium spreading across theground surface. Because the collision and friction between parti-cles dissipated energy quickly, the deposition area would spreadwider than those cases with higher bond strength. Therefore,parallel bond strength is also a major factor in simulating land-slide deposit patterns.
If weaker bond strength was assumed (Fig. 13b), the amplitudesof the seismic signals would be considerably reduced and thevibration frequencies would be higher than that of baseline case.When parallel bond strength was increased to 60 MPa, the energyof the seismic signals increased significantly and the vibrationfrequencies will be low, as it can be observed in the time-frequency spectrum of point B (Fig. 13c).
Limitations of the coupling simulation1. If we set the bottom of the model as a Bquiet^ boundary, also
one kind of absorbing boundary, we experienced difficulty indynamic force equilibrium. Therefore, the bottom of the modelwas only established as a fixed boundary in the numericalmodel in this study. However, we also extended the bottomof the model to reduce the rebounding signals from bottomboundary.
2. Common issues in two-dimensional simulations include find-ing a representative section and wave propagation mechanism.Seismic waves and energy transfer in the third direction ofthree-dimensional simulations differ considerably from thosein two-dimensional simulations. For the 3D, the wave propa-gation is by spherical waves, but it becomes cylindrical wavesin 2D. In this study, we only considered the stress waves andseismic energy propagating in two-dimensional scheme, due tothe fact that three-dimensional simulation can be exceedinglycomplex.
3. A significant amount of rainfall water was involved in theXiaolin landslide during Typhoon Morakot. In circumstanceswith steep slopes, water generally changes the pattern of thelandslide, e.g., decreased viscosity of the landslide mass. The
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Fig. 12 a Comparison of landslide deposits for different particle frictioncoefficients. b The vertical velocity and spectrum of the case of particle frictioncoefficient 0.15 (Pt. B). c The vertical velocity and spectrum of the case of particlefriction coefficient 0.5 (Pt. B)
Pt. B
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Fig. 12 (continued)
Original Paper
Landslides 14 & (2017)672
current PFC code was unable to simulate the influence ofrainfall water in the model. Nonetheless, the impact of rainfallwater on the slope should have little influence on the overallamount of vibration. Moreover, the debris flow effects can stillbe simulated with the decrease of the friction coefficient of walland particle elements. Therefore, we chose not to consider theinfluence of rainfall and groundwater on seismic signals in thisstudy.
4. Differences in the size of particle elements and the actual rockson site may have affected the simulation results. Nevertheless,the precisely defining the processes involved in the disintegra-tion of rock during landslides is very difficult. Therefore, weassumed the particle size based on the results of onsite obser-vations. Deposition profiles were compared with those of the
landslide site as close as possible to ascertain whether theparticle size was suitable for the landslide simulation.
ConclusionsThis study investigates the characteristics of seismic signals causedby landslides by coupling PFC and FLAC codes. We simulatedXiaolin landslide as an example for the coupling approach andalso performed parametrical study. The following conclusions arederived.
1. The rupturing, separation, and sliding of rock mass fromupslope (source area, Pt. A and B) generate larger amplitudeand lower-frequency signals than those of debris flowing at thedownslope (Pt. C and D).
2. The simulated velocity and acceleration are significantly largerthan those field data of Station SGSB due to direct impact andnear-field effect. Also, more high-frequency waves exist in thesimulated signals than those field data because of the same reason,the near-field effect, and because that high-frequency waves atten-uate very fast when traveling to the far-field Station SGSB.
3. A landslide with larger rock particles generates lower-frequency content seismic signals; i.e., larger particles led toseismic signals with lower frequencies and also higher ampli-tude and stronger signals. For further study, we can observewhether lower frequency in the signal corresponds to large sizerock fragments to explain landslide characteristics.
4. Analysis of deposit conditions resulting from different frictioncoefficients for both wall and particle indicated that smallercoefficients contribute to longer runout distances and fastertraveling velocity of landslide. Influence of the particle frictioncoefficient on runout distance is significantly greater than thatof the wall friction coefficient.
5. Weaker parallel bonds induced rock mass to break up intoindividual particles more easily, resulting gentler depositioncovering the ground over a wider deposition zone. With stron-ger parallel bond, the frequency of the seismic signals moni-tored is lower but stronger energy.
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parallel bond strength 16 MPa
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parallel bond strength 60 MPa
Pt. B
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.,H
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(a)
(b)
Fig. 13 a Comparison of landslide deposits of different parallel bond strengths. bThe vertical velocity and spectrum of the case of parallel bond strength: 4 MPa(weak model). c The vertical velocity and spectrum of the case of parallel bondstrength 60 MPa (strong model)
Pt. B
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Fig. 13 (continued)
Landslides 14 & (2017) 673
6. The most pronounced seismic velocities were detected thesliding mass passed point A. Meanwhile at point D, locatedat the downslope, there were already some seismic signalsdetected. Although the signal is weak, it preceded the arrivalof the sliding mass by 35 to 40 s. This simple fact can be usefulfor developing a landslide warning system in the future.
7. The monitoring results of the duration of the seismic signals atpoints A, B, C, and D demonstrate the influence of terrain andchanges in the movement type of landslides on seismic signals,and then, we can know more relationships between the seismicsignals and the characteristics of a complex (avalanche andflow) landslide.
AcknowledgmentsThe authors appreciate the National Science Council of Taiwan intheir support of this research under contract NSC99-2625-M-005-004-MY3. In addition, the authors would like to thank Dr. Ching-Ying Tsou for his help in preparation of Fig. 1.References
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Z. Feng : Q. LinDepartment of Soil and Water Conservation,Chung Hsing University,Taichung City, Taiwan, ROC
C. Lo ())Department of Civil and Disaster Prevention Engineering,National United University,Miaoli County, Taiwan, ROCe-mail: [email protected]: [email protected]
Original Paper
Landslides 14 & (2017)674