Seismic Recordings of Landslides Caused by Typhoon Talas (2011), Japan

Authors*Masumi Yamada1
Yuki Matsushi1,2Masahiro Chigira1
Jim Mori1

Affiliations
1 Disaster Prevention Research Institute, Kyoto University
Gokasho, Uji, Kyoto, 611-0011, Japan
M.Y., Y.M., J.M., and M.C.

2 CREST, Japan Science and Technology Agency
K's Gobancho, 7, Gobancho, Chiyoda-ku, Tokyo, 102-0076 Japan
Y.M.

Corresponding author
Correspondence to: Masumi Yamada

1

Large deep-seated landslides occurred in Nara, Wakayama, and Mie prefectures ofwestern Japan when Typhoon Talas passed through the region on September 3-4,
2011. Signals of large landslides have been recorded by seismic networks aroundthe world, and overall force estimates have been previously determined for large
landslides using long-period waves. This study focuses on the high-frequency
waveforms and presents signals of 18 landslides caused by Typhoon Talas (2011).
The location of the landslides can be determined by a seismologicalback-projection technique and these locations correlate with the observed surface
features. We have found that the volumes of the landslide correlated with anenergy parameter derived from the seismic records.

1. Introduction
When Typhoon Talas moved through western Japan on September 3-4, 2011, there wereextensive slope failures across a wide region in Nara, Wakayama, and Mie prefectures.
Heavy rainfall, exceeding 2000 mm on the Kii peninsula, triggered these destructive
landslides which killed 97 people [Fire and disaster Management Agency, 2011].
Previously, signals of large landslides have been recorded by seismic networks aroundworld [e.g. Kanamori et al., 1984; La Rocca et al., 2004; Surinach et al., 2005; Tang etal., 2009; Favreau et al., 2010; Lin et al., 2010; Feng, 2011 ; Hibert, et al., 2011] andthe long-period data are useful for determining the force-history of landslides. A force
generated by the mass sliding can be inverted by matching the phase and amplitudes ofthe observed data to theoretical waveforms [e.g. Kanamori et al., 1984; Brodsky et al.,
2003; Stark and Ekstrom 2010]. Here we focus on the more complicated high-frequency
waveforms recorded by high-sensitivity networks. The station distribution is muchdenser than for broadband stations in Japan, so that analytical results of the data canhave higher resolution in time and space [Ohsumi et al., 2005; Ohsumi et al., 2006].
This study presents signals of 18 landslides caused by Typhoon Talas (2011), whichwere detected by high-frequency waveforms. These landslides can be located by aback-projection technique, and the volumes of the landslides correlate with an energy
parameter determined from the seismic records.
2. Data
The seismic signals due to these landslides were recorded by dense seismic networks inJapan. The low-frequency surface waves of the larger landslides were recorded acrossall of Japan by the broadband seismic network F-net [National Research Institute for2Earth Science and Disaster Prevention, 2010]. Higher frequency waves were also
recorded by high-gain seismic networks (Hi-net, Japan Meteorological Agency, anduniversity networks) at distances up to a few hundred kilometers. The landslide signals
are usually tens of seconds in duration and show emergent onsets, thus they can beeasily distinguished from records of small earthquakes, which have different shapes andshorter duration.
Figure 1(a) shows the Akatani landslide signal recorded by a broadband
seismometer 35 km away from the source. Filtering of the data shows that thehigh-frequency (>1 Hz) and low-frequency (<0.1 Hz) components of the waveform
have different characteristics. The onset of the large amplitude low-frequency motion isabout 20 sec earlier than the initiation of the observable high-frequency motions. The
low-frequency component shows the peak amplitude significantly earlier than the peak
amplitudes of the high-frequency component.
The low-frequency component can be modeled assuming a single force for amass sliding down the slope. The size of the maximum force generated by the mass
sliding was estimated as 7x1010 N with a total duration of 70 sec, using modeling ofbroadband waveforms from 18 regional stations in Japan [National Research Institutefor Earth Science and Disaster Prevention, 2011]. The low-frequency component
reflects the progressive unloading and reloading of the solid earth beneath the slide,
while much of the high-frequency component probably is generated by complicatedmultiple momentum exchanges between landslide debris clasts and the bed as it issliding. The earlier onset of the long-period waves suggests that the landslide starting
sliding rather smoothly as a coherent block. The large amplitude in the high-frequency
waveform at about 150 sec may be related to a fairly sudden stopping of part of thelandslide. From field observations it can be seen that the Akatani event was stopped
when it hit the opposite valley wall (see Figure 1(b)).
The landslide sequence was distributed over a broad area, so data from thedense high-gain short-period networks (about 20 km spacing) were used for the
analyses. Applying a 4th order Butterworth band-pass filter (1-4 Hz) to the seismograms
recorded between September 3rd and 5th, we identified 18 landslide events (see Table 1)
which are seen on multiple stations.
3. Back-projection Analysis
In order to determine the location and timing of the landslide, we applied aback-projection technique [Spudich and Cranswick, 1984] to the seismic records. The
advantage of the back-projection is that little prior information is necessary and the3results are reasonably stable. We process the seismic data with the following procedure.
1. An envelope of the band-passed records is calculated using a Hilbert transform. 2. A
mean smoothing algorithm with a window of 200 samples (2 seconds) is applied. 3. The
noise amplitude is subtracted from the data. 4. The maximum amplitude is normalized
to a value of 1.0. This process reduces the effect of high-frequency noise and makes theresults of back-projection more stable. We use the Japan Meteorological Agency 1-D
velocity structure, and assume an S-wave velocity for the analysis. The recorded waves
are likely a mixture of S and surface waves with a velocity close to the S-wave velocity.
First, we perform the back-projection with all the available data. Then, the location isrefined in a second iteration by repeating the process using only the near-source data
corresponding to the most probable location determined in the first iteration. The
probability distribution for the location of the second iteration for the 09040722 event isshown in Figure 2. There are several landslides very close to the most probable locationof this signal. However, this event shows significantly large amplitudes in the seismic
records, so we inferred that this signal is from the Akatani landslide, which has thelargest volume of sliding mass among the possible sources. The band-passed waveforms
and envelopes of the Akatani landslide are shown in Figure 3. The timing is aligned andthe amplitude decays as a function of distance, which shows qualitatively that thelocation is reasonable. We also used information provided by local witnesses andpersonal communications with electric power companies when there were multiple
choices for an event (See Supporting Table 1 and Supporting Figure 2). We located 13
events out of 18 by this method, and the other 5 events have multiple solutions so thelocation is not well resolved.
The largest landslide was the Kuridaira event, with a total volume of 13.6
million m3. The main collapse of the Kuridaira event occurred at 23:06 on September 3
followed by subsequent minor slides (See Supporting Figure 1). A second slide occurredat 23:07, and third at 23:12. Since waves from a distant M7 earthquake arrived at about
the same time as the occurrence of the landslide, the onsets of the landslide records arecontaminated by the teleseismic P-waves. However, main energy of the landslide can beseparable from the earthquake, since the largest amplitudes from the landslide are about
20 seconds after the onset of the teleseismic P-wave arrival.
4. Size Estimate of Landslides
In order to quantify the size of the seismic signals, we use the sum of the squared
band-passed velocity data as an energy parameter. Note that with a distance correction,
this measurement is proportional to the radiated seismic energy [Kanamori et al., 1993;
4

Suwa et al., 2003] in this frequency range. Since we are using band-pass filtered records,
it is not equal to the total radiated energy of the landslide movement. Here we simply
use the sum of the squared velocity and define it as our energy parameter,
Energy parameter E = ∑t =t 0 v(t ) 2 dt
t =t1

(1)

where v (t ) is the 1-4Hz band-pass filtered velocity record (cm/s) at time t, t0 is a
trigger time when the standard deviation of a 10 s record exceeds twice the amplitude of
the noise level, t1 is a threshold time when the standard deviation becomes smaller than
1.5 times of the noise level, and dt is the sampling interval of 0.01 s. Integration overtime takes into account the duration of the signal as well as the amplitude. Since
amplitudes of the signals can be significantly affected by the subsurface soil
amplification, we use data recorded at only the borehole sites. The source-station
distance range is 1 to 80 km for this analysis. From these data it is difficult to discuss
the physical interpretation of this band-limited energy estimate, because we do notknow the shape of the source spectrum across a wide frequency range.
Figure 4(a) shows the relationship between the source-station distance and thismeasurement. The energy parameter shows larger amplitude for large landslides (e.g.
Kuridaira, Akatani) and decays as a function of distance. However, the slope seems tobe similar for all events. Using these data, we can empirically derive an attenuation
relation. Therefore, we regress the dataset using a linear function with a constant slope
α and determine the intercept βi for each event i.
log10 E = α log10 R + βi(2)where R is the source-station distance (km). The optimal slope minimizing the error for
11 landslides is -2.15, and the value of βi for each event is shown in Table 1. The
Kuridara event was not used in the regression because it is contaminated by a distantearthquake. The βi value for the Kuridaira events was later evaluated using the
optimal value of α from the regression.
The values of βi represent the log of the high-frequency radiated energy at a
distance of 1 km from the source. Since we have the locations of the landslides, we cancorrelate the seismic signals with the observed surface features. We compared thehigh-frequency energy values with the volumes of the landslides, as estimated fromairborne LiDAR (light detection and ranging) topographic surveys before and after the5event, where available. Otherwise the volume is estimated from 5 or 10 m-meshdigital elevation models and aerial photographs. Although there is a fairly large
variation for the small events, in general, there appears to be a good correlation of theenergy parameter with the landslide volume (Figure 4(b)). If a linear relation is fit to thedata, the regression equation is expressed as;
E |R=1km = 8.5 ×10−20V 2.06

(3)where V is the volume of the landslides. Note that this equation shows the band-limited
energy parameter is approximately correlated with the square of the volume of thelandslides. Physically, this energy parameter may be related to the mass (and maybe
some unknown quantity) of the landslides, or alternatively this high-frequency energy
may be more related to the dynamic properties of the moving slide. Using this empirical
relationship the energy parameter of the seismic records can be used to determine thesize of remotely detected landslides.
5. Conclusions
The landslides caused by Typhoon Talas (2011) represent one of the bestrecorded sequences of catastrophic deep-seated landslides in the world. The events canbe located by conventional seismological techniques, and the volumes of the landslidecorrelated with the energy parameter of the seismic records. The seismic signals canprovide information about the energy radiation and durations of the landslides, which
are rarely obtained from visual observations [Suwa et al., 2010; Socio et al., 2008].
Although the seismic networks were originally designed for locating earthquakes, thecontinuously recorded data can be very important for understanding the mechanisms ofother natural phenomenon, such as landslides. These high sensitivity networks are partof the earthquake early warning system and data are transferred in real time. Therefore,
using the techniques shown here, there is a possibility to determine the locations andsizes of landslides within a minute of their occurrences. Such information can be usefulfor prompt emergency information, rescue efforts, and mitigation of further damage
from these large landslides.
Acknowledgements
We acknowledge the National Research Institute for Earth Science and Disaster
Prevention (NIED), Japan Meteorological Agency (JMA), and Japanese universities for6the use of the seismic data. High-resolution DEM data, which have been used to
calculate landslide volumes, were provided by the Nara Prefectural Government and the
Kinki Regional Development Bureau, Ministry of Land, Infrastructure and Transport.
We thank Dr. Colin Stark and an anonymous reviewer for their helpful comments.
References
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8

(a)

1.5

Original

Velocity (x10-6 m/s)

0
−1.5
0.4

HP filtered 1Hz

0
−0.4
0.4

LP filtered 0.1Hz

0
−0.4

0

50

100

150

Time (s)

200

250

300

(b)

Fig. 1 (a) Broadband seismic records of Akatani landslide at NOKF station. The verticallines show the onset and approximate time of impact on the far valley wall. Originalrecord, high-pass filtered record at 1Hz, low-pass filtered record at 0.1Hz from the top.
9

(b) Elevation change at the Akatani landslide estimated from airborne LiDAR
topographic surveys. The projected coordinate system is the Japan plane rectangularcoordinate system zone 6, and the location of the landslide center is 34.126°N and
135.725°E in the WGS84 coordinate system.

34.8˚
DP.KS88
HEGURIN.YZEH
DP.KS87
DP.KS86

N.TRIH

34.5˚

KATADA

N.TISHN.URSH

E.HBR
N.HYSH

N.NAGHGS.NGR
E.NWK134.2˚

DP.KS84
DP.KS83 N.KRTH

N.MGWH
N.KAWH
DP.KS64KOUYAKIHOKU

1.0

N.NOKF
N.HNZH
N.NKMH
E.OIC DP.KS82

0.9
N.OWSH

0.8

N.HRKHDP.KS80

33.9˚

0.7

DP.KS81N.INMH

0.6

MNV

0.5
N.KTDH

0.4

N.OTOH

E.SHR

33.6˚

0.3

N.NKTH

0.2

N.SSMH
E.ESM

135˚

135.3˚

20km

135.6˚

135.9˚

136.2˚

0.1
0.0

Fig. 2 Distribution of the seismic stations (triangles) and landslides (circles). Landslides
identified by seismic records are marked with white circles. The shaded colors show the
probability of the location of Akatani landslide, obtained from the back-projection
analysis. The star shows the surface location of the Akatani landslide.

10

Fig. 3 Seismic records of the Akatani landslide. (a) 1-4Hz band-pass filtered records, (b)processed envelopes for back-projection analysis. Adjacent seismograms are shown
alternatingly in black and gray for clarity. The red line shows the predicted arrival time
for the location obtained from the back-projection analysis.

11

-6

10

Energy parameter

(a)
-7

10

-8

10

Akatani
KuridairaNagatono
Iya
Dontani

-9

10

-10

10

0

1

10

2

10
Distance(km)10-4Energy parameter at 1km

10

(b)

-5

10

-6

10

-7

10

E =8.5*10-20V2.06
l

-8

10

5

10

6

7

10
10
Landslide volume (m3)810Fig. 4 (a) Energy parameter of different landslides as a function of source-station
distance. The lines show regression function for each event. (b) Relationship between
volume of the landslide and the energy parameter for a distance 1 km from the source.
The line and equation in the figure show the results of a linear regression.

12

Table 1 List of landslides detected by seismic recordings. Time value shows
when the largest high-frequency energy was released. The first 4 digits of the
ID show the date of the event. Duration is the average time length of thelandslide signal of the high-frequency waveforms. 09031112, 09031114,
09032307, and 09032311 may be subevents of the previous landslide.
ID

Time
(UTC)

Back Projection

Surface Location

Lon.

Lon.

Lat.

Lat.

Error
(km)

Landslide Name

DurationVol.
(s)(106 m3)

βi

09030946

9:46:07135.723

34.169135.741

34.134

4.2

E.Akatani-1

2.1

30

-5.99

09031112

11:12:29

135.77834.138135.741

34.134

4.9

E.Akatani-2

(2.1)

30

-6.06

09031114

11:14:30

135.908

34.108135.741

34.134

15.6

E.Akatani-3

(2.1)

50

N/A

09031713

17:13:42

135.795

34.086135.780

34.128

4.9

Uguhara

1.6

40

-6.03

09031758

17:58:11

135.72134.15430-6.10

09031805

18:05:41

135.841

34.16350-5.8209032154

21:54:04

135.580

33.748135.63033.718

5.7

Iya

30

-5.73

09032206

22:06:46

135.801

34.174135.741

34.154

6.0

Ohto-Shimizu

0.9

30

-6.60

09032306

23:06:13

135.83634.088135.83634.075

1.4

Kuridaira-1

13.6

100

-4.03

09032307

23:07:20

135.825

34.086135.83634.075

1.6

Kuridaira-2(13.6)

30

N/A

09032311

23:11:44

135.826

34.054135.83634.075

2.5

Kuridaira-3(13.6)

20-6.08

09040002

0:02:13135.449

33.671135.469

33.656

2.5

Dontani

3.2

30

-6.14

09040006

0:06:04

135.915

34.28420

N/A09040140

1:40:00

135.649

34.12640-6.1609040145

1:45:54135.745

34.13009040558

5:58:06

136.380

34.37009040722

7:22:15135.715

34.133135.725

34.126

1.2

Akatani

09040825

8:25:39135.959

34.348135.949

34.338

1.4

Ohtaki135.75434.12913

0.8

Nagatono

5.2

4.1

60

-5.19

50

-5.63

8.2

70

-4.77

0.7

50

-7.14