international conference in commemoration of 10th anniversary of the chi-chi earthquake, 2009...

International Conference in Commemoration of 10th Anniversary of the Chi-Chi Earthquake, 2009

September 21~26, 2009

Statistical Approach to Model Spatial Statistical Approach to Model Spatial

and Temporal Variability ofand Temporal Variability of

Earthquake-Induced LandslidesEarthquake-Induced LandslidesChyi-Tyi Lee

Institute of Applied Geology, National Central University, Taiwan

The Next Generation of Research on Earthquake-induced Landslides

INTRODUCTIONINTRODUCTION

◆ The study of earthquake-induced landslide susceptibility on a regional scale commonly requires the employment of an analytical slope-stability method and the infinite-slope model (Wilson and Keefer, 1985; Jibson and Keefer, 1993; Harp and Wilson, 1995; Jibson et al., 1998, 2000; Liao, 2004). This method requires calculation to determine the limit-equilibrium of the slope stability given the strength parameters, failure depth, and groundwater conditions for every calculation point in the study area. This requirement causes immense problems in terms of data acquisition and control of spatial variability of the variables (Hutchinson, 1995; Guzzetti et al., 1999).

INTRODUCTIONINTRODUCTION 1/3

◆ In traditional landslide susceptibility analysis, it is most common to use a statistical approach where landslide inventories and causative factors are utilized to build a susceptibility model for the prediction of future landslides. Many different methods and techniques for assessing landslide hazards have been proposed and tested. These have already been systematically compared and their advantages and limitations outlined (Carrara, 1983; Varnes, 1984; Carrara et al., 1995; Hutchinson, 1995; Chung and Fabbri, 1999; Guzzetti et al., 1999; Chung, 2006; van Western et al., 2006). Most of these approaches require multi-temporal landslide inventories so that the susceptibility model can predict landslide occurrence over a given time period (Guzzetti et al., 1999).


◆ In the study of earthquake-induced landslides, the landslide inventory is naturally event-based; it is not possible to use a multi-temporal landslide inventory. Therefore, the temporal significance of a susceptibility model should incorporate the use of a triggering factor, like that used in the deterministic method.

◆ Lee et al. (2008) introduced a new approach using statistical method in building an earthquake-induced landslide prediction model. In this study, I demonstrate the new method using an example form the Yinlin quadrangle with two earthquake events, and the results are then cross-validated.


Geology Geology of of TaiwanTaiwanTaiwan orogeny started at Late Miocene and is presently active.

The Central Range consists of metamorphic complex and a Paleogene slate belt.

Bordering the Central Range is the Western Foothills, consisting of Neogene sedimentary formations, and the Eastern Coastal Range, which is also made of Neogene sedimentary strata.

The Longitudinal Valley located between the Central Range and the Eastern Coastal Range is the suture zone between two plates.

Terrain Division &Terrain Division &The Study AreaThe Study Area

For the purpose of producing a set of landslide susceptibility quadrangle maps for Taiwan. We classified whole Taiwan into 13 geomorphological and geological homogeneous zones for further study.

Study Study AreaArea

Topography of the Study AreaTopography of the Study Area

Hilly Terrain

Mountainous Terrain

The study area is divided into two homogeneous region for training the susceptibility model.

Geology of the Study AreaGeology of the Study Area

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METHODOLOGYMETHODOLOGY

◆ Since multi-temporal landslide inventories are not available for earthquake-induced landslides, I consider using an event-based landslide inventory and an event-based landslide susceptibility analysis (EB-LSA) which was proposed by Lee et al., (2008).

◆ In EB-LSA, an event-based landslide inventory must be constructed firstly. In parallel with this, the causative factors of the landslides are processed and the triggering factors determined. These factors are then statistically tested, and the effective factors selected for susceptibility analysis. Each selected factor is rated, and their weighting analyzed.

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λ

METHODOLOGYMETHODOLOGY

◆ Discriminant analysis allows us to determine the maximum difference for each factor between the landslide group and the non-landslide group, as well as the apparent weights of the factors.

◆ A linear weighted summation of all factors is used to calculate the landslide susceptibility index (LSI) for each grid point. The LSIs are used to establish a landslide ratio to LSI curve and determine the spatial probability of landslide at each grid point. The spatial probability of landslides is then used for landslide susceptibility mapping. For a more detailed description of EB-LSA, please refer to Lee et al. (2008).

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Data SourcesData Sources

• Multi-spectral (XS) and panchromatic (PAN) SPOT2 images from Satellite Receiving Station of Center for Space and Remote Sensing Research, National Central University.

• Digital terrain model (DEM) of 40x40m resolution from Aerial Survey Office, Forestry Bureau, Council of Agriculture, Taiwan.

• Geological map of 1 to 50,000 scaled from Central Geological Survey, Taiwan.

• Strong-motion data from the Seismological Center, Central Weather Bureau, Taiwan.

• Road map digitized from 1 to 5,000 scaled topographic maps.

Data ProcessingData Processing

• Both XS and PAN SPOT images were used. They were fused into a higher resolution (6.25m x 6.25m) false-color image for landslide interpretation.

• XS images taken prior to Jueili and Chi-Chi earthquakes were used for calculation of normalized differential vegetation index (NDVI).

• Digital terrain model (DEM) of 40x40m resolution was corrected for erroneous data and filtered for noises, and then the DEMs were interpolated to grid cells of 20x20m resolution using cubic spline interpolation.

• Slope gradient, slope aspect, slope height, etc. which are derived from DEM are also in 20x20m grid data format.

• Geological map, NDVI, etc. are transferred into 20x20m grid data for further use.

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Data ProcessingData Processing

• Strong-motion seismograms in and around the study area were collected by base-line correction and filtered for removing noises according to the standard procedure suggested by the Pacific Earthquake Engineering Research Center (PEER).

• The Arias intensity was then calculated from each corrected seismogram. The arithmetical mean of the Arias intensities of the N-S and E-W components were used to represent the earthquake intensity for a strong-motion station site. These values were interpolated on each grid point in the study area using the ordinary Kriging method.

• Tools used are MapInfo vector GIS and ERDAS Imagine raster GIS.

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Event-based Landslide InventoryEvent-based Landslide Inventory

Prior to JueiliPrior to Jueili

（ Blue color indicates cloud or shade in SPOT image ）

After JueiliAfter Jueili

1/2

Landslides triggered by Landslides triggered by the Jueili earthquake the Jueili earthquake

Event-based Landslide InventoryEvent-based Landslide Inventory2/2

Prior to Chi-ChiPrior to Chi-Chi After Chi-ChiAfter Chi-Chi

Landslides triggered by Landslides triggered by the Chi-Chi earthquake the Chi-Chi earthquake

（ Blue color indicates cloud or shade in SPOT image ）

Preliminary Selection of FactorsPreliminary Selection of Factors

• Lithology• Slope gradient• Slope aspect • NDVI • Terrain roughness• Slope roughness• Total curvature • Slope height• Total slope height• Topographic wetness

index

• Distance to road • Distance to fault• Distance to river bend • Distance to river head

• Peak ground acceleration (PGA)

• Peak ground velocity (PGV)

• Arias intensity (AI)

0 4 0 8 0 1 2 0 1 6 0 2 0 00 %

2 %

4 %

6 %

8 %

1 0 %

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

0 4 0 8 0 1 2 0 1 6 0 2 0 00 %

1 %

2 %

3 %

4 %

Selection of Effective FactorsSelection of Effective Factors

Discriminator Dj :

Prob. of F

ailure

Slope, %

Frequency

Landslide

Non-landslide

Slpoe, %Obs. Cum. Prob.

Exp. C

um.

Prob.

Portion of Landslide

Portion of Area

D=0.790

P-P Plot

Test of normal distribution of the factor.

Visual inspection of frequency distribution of the two groups, and calculation of discriminator D.

Examination of probability of failure curve to see if landslide probability increases with the factor value.

, where, is average of landslide group, jA

non-landslide group,

jB

is pooled standard deviation of two groups, j indicates j th factor.PjS

Examination of success rate curve to check the ability of interpreting landslides of the factor.

Success Rate Curve

AUC=0.767

Probability of Failure Curve

is average of

Effective Factors SelectedEffective Factors Selected

► The following factors are tested to be effective in interpreting landslides:

• Lithology

• Slope gradient

• Slope aspect

• Terrain roughness

• Slope roughness

• Total curvature

• Arias intensity (AI) (triggering factor)

0 100 2000%

16%

0 1 0 0 2 0 00 %

1 0 %

2 0 %

3 0 %

Hilly terrain Mountainous terrain Thick line: Landslide groupThin line: Non-landslide group

HillyTerrain

MountainousTerrain

%

524.55

0

%

Fre

quen

cy

%

(Chi-Chi for example)

%

Processing of Landslide Causative FactorsProcessing of Landslide Causative Factors

0 1 0 0 2 0 00 %

1 0 %

2 0 %

3 0 %

4 0 %

5 0 %

%

Land

slid

e ra

tio %

SLOPE GRADIENTSLOPE GRADIENT

0 100 200 3000%

4%

0 100 200 3000%

4%

Hilly TerrainMountainous Terrain

360

0

Land

slid

e ra

tio %


oo

0 1 0 0 2 0 0 3 0 00 %

1 %

2 %

3 %

4 %

5 %

o

Fre

quen

cy

%


Processing of Landslide Causative FactorsProcessing of Landslide Causative FactorsSLOPE ASPECTSLOPE ASPECT

0 2 40%

40%

0 2 40%

Mountainous Terrain

m

13.84

0

m

Hilly Terrain


m

40%

0 2 40 %

4 %

8 %

1 2 %

1 6 %

m

Land

slid

e ra

tio %

Fre

quen

cy

%


Processing of Landslide Causative FactorsProcessing of Landslide Causative FactorsTERRAIN ROUGHNESSTERRAIN ROUGHNESS

Land

slid

e ra

tio %

0 2 0 4 0 6 0 8 00 %

2 0 %

0 2 0 4 0 6 0 8 00 %

1 0 %

2 0 %

3 0 %

Mountainous Terrain

m162.61

0

Hilly Terrain


mm

0 2 0 4 0 6 0 8 00 %

4 %

8 %

1 2 %

1 6 %

2 0 %

m

Fre

quen

cy

%


Processing of Landslide Causative FactorsProcessing of Landslide Causative FactorsSLOPE ROUGHNESSSLOPE ROUGHNESS

- 6 - 5 - 4 - 3 - 2 - 10 %

8 %

Land

slid

e ra

tio %

- 6 - 5 - 4 - 3 - 2 - 10 %

8 %Mountainous Terrain

-0.65

-6.00

Log(radius/m) Log(radius/m)

Log(radius/m)

Hilly Terrain


Log(radius/m)

- 6 - 5 - 4 - 3 - 2 - 10 %

4 %

8 %

1 2 %

1 6 %

2 0 %

Fre

quen

cy

%


Processing of Landslide Causative FactorsProcessing of Landslide Causative FactorsTOTAL CURVATURETOTAL CURVATURE

Land

slid

e ra

tio %

0 4 80 %

1 %

2 %

3 %

4 %

5 %

Lithologic Unit

0 4 80%

40%

0 4 80%

80%

Mountainous Terrain

10

1 Lithologic Unit

Hilly Terrain

Fre

quen

cy

%

Lithologic Unit

1 Alluvium & Terrace Deposites 2 Lateritic Terrace Deposites 3 Toukoshan Formation (Conglomerate) 4 Toukoshan Formation (Sandstone &

Mudstone) 5 Cholan Formation 6 Chinshui Shale 7 Kueichulin Formation (Tawo Sandstone) 8 Kueichulin Formation (Shih Liufen Shale) 9 Kueichulin Formation (Kantaoshan

Sandstone)10 Nanchung Formation


Processing of Landslide Causative FactorsProcessing of Landslide Causative FactorsLITHOLOGIC UNITLITHOLOGIC UNIT

Hilly terrain Mountainous terrain

Topographic Correction of Arias IntensityTopographic Correction of Arias Intensity

• Get amplification factor of Ia by using the ratio of Ia from strong-motion data recorded at stations on ridge top and other stations.

• Establish the relationship between amplification factor and a topographic factor : (Lin and Lee, 2003)

where FF is amplification factoramplification factor, hh is relative height to relative height to riverbedriverbed, and Ia’ is Arias intensity after correction.

/ 93.799 0.287 0.464F h

'a aI FI

200000 205000 210000 215000 220000

2600000

2605000

2610000

2615000

2620000

2625000

1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 Arias Intensity OriginalArias Intensity Original

200000 205000 210000 215000 220000 225000

2600000

2605000

2610000

2615000

2620000

2625000

0 50 100 150 200 250 300 350 400 450 500 550 600Relative height to riverbedRelative height to riverbed

0 4 8 1 20 %

8 %

0 4 8 1 20 %

4 %

8 %

1 2 %

1 6 %

0 200 400 600 800 10000%

20%

0 200 400 600 800 10000%

1%

2%

3%

4%+F

requ

ency

%

Fre

quen

cy

%

Land

slid

e R

atio

%La

ndsl

ide

Rat

io %

m/s

m

m/s m/s

m

m

Chi-Chi EarthquakeChi-Chi Earthquake

Arias IntensityArias Intensity

( After correction)( After correction)

200000 205000 210000 215000 220000

2600000

2605000

2610000

2615000

2620000

2625000

1 2 3 4 5 6 7 8 9 10 11 12 13

0 4 8 1 20 %

8 %

0 2 4 6 8 1 00 %

2 %

4 %

6 %

8 %

m/s

Fre

quen

cy

%La

ndsl

ide

Rat

io %

m/s

m/s

Chi-Chi EarthquakeChi-Chi Earthquake

200000 205000 210000 215000 220000

2600000

2605000

2610000

2615000

2620000

2625000

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

Arias IntensityArias Intensity

( After correction)( After correction)

m/s

Fre

quen

cy

%La

ndsl

ide

Rat

io %

m/s

m/s

Jueili EarthquakeJueili Earthquake

0 1 2 3 40%

8%

0 0.4 0.8 1.2 1.6 20%

0.4%

0.8%

1.2%

1.6%

Result of the Discriminant AnalysisResult of the Discriminant Analysis

EQ Terrain Litho. Slope Gradient

Slope Aspect

Terrain Rough.

Slope Rough.

Total Slope

Arias Intensity

Jueili Hilly - .447 .025 - .268 .067 .193

Mountain - .164 .213 .105 .073 .089 .261

Chi-Chi Hilly .092 .111 .072 .091 .017 .041 .576

Mountain .105 .331 .145 .017 .068 .022 .311

Susceptibility map trained from the Jueili earthquake-induced landslides

Susceptibility map with no-triggering factor

Landslide Susceptibility MapsLandslide Susceptibility Maps

((JueiliJueili))

Susceptibility map trained from the Chi-Chi earthquake-induced landslides Susceptibility map with no-triggering factor

Landslide Susceptibility MapsLandslide Susceptibility Maps (Chi-Chi)(Chi-Chi)

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RESULTSRESULTS

a b c

d e fEvent-based landslide susceptibility maps at Yinlin quadrangle. (a) Landslides triggered by 1997 Jueili earthquake, (b) event-based landslide susceptibility map trained by Jueili data set, (c) background landslide susceptibility map for Jueili event. (d) Landslides triggered by 1999 CHI-CHI earthquake, (e) event-based landslide susceptibility map trained by Chi-Chi data set, (f) background landslide susceptibility map for CHI-CHI event.

0 0.2 0.4 0.6 0.8 1Portion of A rea

0

0.2

0.4

0.6

0.8

1

Suc

cess

Rat

e

Jueili AUC=0.9423Chi-Chi AUC=0.9099

0 0.2 0.4 0.6 0.8 1Portion of A rea

0

0.2

0.4

0.6

0.8

1

Suc

cess

Rat

e


Success rate curve

Predict rate curve

0 0.2 0.4 0.6 0.8 1Portion of A rea

0

0.2

0.4

0.6

0.8

1

Suc

cess

Rat

e

0 0 .2 0.4 0.6 0.8 1Portion of A rea

0

0.2

0.4

0.6

0.8

1

Suc

cess

Rat

e



Hilly Terrain

Hilly Terrain

Mountain Terrain

Mountain Terrain

Success rate curve and Predict rate curve in the Yinlin quadrangle

Jueili Earthquake Chi-Chi Earthquake

RESULTSRESULTS

475-year475-yearArias Intensity Arias Intensity Hazard Map for Hazard Map for TaiwanTaiwan

44 earthquakes and 5109 records are used.

Mw=7.0 Mw=7.6

36

Modeling Spatial ProbabilityModeling Spatial Probability

Hilly Terrain

Mountainous Terrain

(Chi-Chi event)

0 0.2 0.4 0.6 0.8

Landslide suscep tib ility index

0

0.1

0.2

0.3

Pro

ba

bili

ty o

f fa

ilure

0 0.2 0.4 0.6 0.8

Landslide susceptib ility index

0

0.05

0.1

0.15

0.2

0.25

Pro

babi

lity

of f

ailu

re

An example of 475-year earthquake-induced landslide hazard map for the Yinlin Quadrangle

Landslide Hazard MapLandslide Hazard Map

38

DISCUSSION AND CONCLUSIONSDISCUSSION AND CONCLUSIONS• An event-based landslide susceptibility map reflects landslide

distribution for a certain triggering event based on which the susceptibility model was trained. Therefore, there may have many different event-based landslide susceptibility maps for a given region.

• Spatial probability of landslides can be modeled by a landslide ratio curve, and temporal probability of landslides can be achieved via a probabilistic seismic hazard analysis. And a landslide hazard map may be constructed; similar to those have been done by Jibson et al. 2000.

• The advantage of this landslide hazard models is capable of predicting shallow landslides induced during an earthquake scenario with similar range of ground shaking, without requiring the use of geotechnical, groundwater or failure depth data.

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Thanks for your Thanks for your attention!attention!

international conference in commemoration of 10th anniversary of the chi-chi earthquake, 2009...

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