5
2. METHODOLOGY
Time and Location
This research was conducted from January to June 2013 including the data preparation, data processing, and developing Method. The processing of this data
was being conducted in the Tsunami and Earthquake Centre Agency for Meteorological, Climatological and Geophysical, Jakarta.
The study area is located at the Bangka Island between the latitudes of - 1.30° and -3.30° and longitudes of 105° and 107°, bordered by the following
areas: Bangka strait in the west, Karimata strait in the east, Natuna Sea in the north and java Sea in the south. The study area includes 5 regency totally and
partially. The total area is 11.676,05 km
2
or 1.167.605 Ha. Figure 3 illustrates the location of the study area.
Materials
The data in the research came from several sources. Some information were collected by conducting interview, questionnaire and discussion with expert from
Indonesia agency for Meteorological, Climatological and Geophysical BMKG and National Nuclear Energy Agency of Indonesia BATAN.
Figure 3 Location of study area
6 The basic materials which are used and considered in this research are listed
as follow: Earthquake Catalog from 1973 to 2012, source Indonesia Agency for
Meteorological, Climatological and Geophysical Microtremor Data From year 2011, source National Nuclear Energy Agency of
Indonesia Digital Elevation Model SRTM resolution of 90m produced by NASA source
downloaded from http:www.cgiar-csi.org Geological map from the year 1994 and 1995 At scale 1:250.000, source
Geological Research and Development Centre, Bandung. Administration boundary map scale 1:250.000 sources BAPPEDA Bangka-
Belitung and Geospatial Information Agency BIG.
Methods
Hazard is a dangerous phenomenon, substance, human activity or condition that may cause loss life, injury or other health impacts, property damage, loss of
livelihoods and services, social and economic disruption, or environmental damage Guzey, et.al, 2013 Determining earthquake hazard areas is a procedure
for estimating the total earthquake damage effect from ground shaking and related phenomena by taking into account the effects of local site condition. The
subsurface and topographic condition can amplify or reduce the ground shaking acceleration at a site with respect to what would be expected for feasibility site of
nuclear power plant ground at that location. In this research, the determining hazard areas associated with the damage in case of earthquake method was
presented for related to Nuclear Power Plant site selection. In this study, important factors closely related to earthquake hazards such as seismicity, geology, slope,
earthquake intensity, soil distribution and amplification factors data were compiled for spatial database using GIS, and ranked by relative susceptibility of
earthquake hazards.
To produce earthquake hazard map areas for Bangka Island, the Simple Additive Weighting technique was used. After the data was obtained from
multiple sources, the SAW procedure was followed by manipulating the data using the raster calculator function of Spatial Analyst. Figure 4 gives a general
overview of the study.
Data Collection
GIS analytical techniques include: overlays, distance calculation, buffering etc. The first step in GIS process is collection data. Data collection refers to the
process of identifying and gathering the data required for specific application. The data in the research came from several sources. Data collection Category is
divided into: 1 primary data obtained from field observation and interviewing some stakeholders who are chosen based on knowledge, expertise and position
and 2 secondary data which are collected from government institution`s documents and comprehensive literatures.
7
Figure 4. General Framework of this study
Data Preparation
In the preparation phase, all necessary geometric thematic editing was done on the original data sets and a topology was created. In the next step, all
vector layers were converted into raster format and the spatial datasets were processed in GIS software. In the following, the practical preparation steps for
creating criteria maps are described. In this study, important factors closely related to earthquake hazards such as seismicity, geology, slope, earthquake intensity, soil
distribution and amplification factors data were compiled for spatial database using GIS, and ranked by relative susceptibility of earthquake hazards Hereby, six
raster datasets were prepared for the weighted overlay analysis with the following steps:
8 1. Calculate Earthquake Intensity
Earthquake intensity is a measure of the degree of damage caused by an earthquake at a given place UNDP, 1994. It describes the effect of an earthquake
on the surface of the earth and integrates numerous parameters such as ground acceleration, earthquake duration and subsoil condition Munich Re, 2000. It
depends upon the strength of the earthquake, the distance of the location from the hypocenter and local subsoil condition. For historical event, for which there are no
instrumental records, but only information concerning the damage they caused, the intensity are either estimated directly from data, or taken from catalogues.
There are many empirical formula concerning the relationship of intensity, magnitude and hypocentral distance have been proposed by many investigator.
Earthquake intensity in the study area was determined using an official Earthquake and Tsunami center BMKG Modified Mercalli Intensity MMI scale
map. The MMI scale is divided into 12 continuous categories Wood and Neumann 1931. The lower degrees of the MMI scale generally deal with the
manner in which the earthquake was felt by people. The higher degrees are based on observed structural damage.
For estimation of earthquake intensity in this study was used empirical formula from the attenuation formula of Modified Mercalli intensity proposed by
Dowrick et.al 1998. Dowrick et.al developed Modified Mercalli MM intensity attenuation relationships from observed intensities in New Zealand earthquakes.
The MM intensity MMI scale measures the earthquake effects at a site in terms of the effect it has on the natural and built environment. The model calculates the
intensity using the Dowrick attenuation function and they are predict the intensity on the Modified Mercalli Intensity MMI scale as a function of the distance from
the earthquake.
The estimated earthquake intensity calculated for each point from the earthquake parameter and described as follows below:
a The first step is to set the range area interest to be estimated of the initial
calculation, in this study used the rectangular area of 2 x 2 degree per range area.
b Set calculation grid, we set the calculation grid at 0.25 degree.
c Area sources are geographical areas within which an earthquake of a given
magnitude is equally likely to occur at any time or location, d
Calculate the distance between two locations from epicenter to certain location each of grid point using the Spherical formula:
∆ = acossinlat
1
.sinlat
2
+coslat
1
.coslat
2
.coslong
2
−long
1
.R 1
Where: lat1 = Latitude of epicenter lat2 = Latitude of location x
long1 = Longitude of epicenter long2 = Longitude of location x
R = E arth’s radius mean radius = 6,371km
∆ = Distance between epicenter and location x in km e
Measure the hypocenter distance :
9
2 Where R is hypocentral distance, h is epicentral depth and ∆ Distance
between epicenter and location x in km. f
Calculate estimation of maximum intensity in location x using Dowrick Empirical formula:
I = 1.41 M - 1.18 ln R - 0.0044 R + 2.18
3 Where R is hypocentral distance, and M is magnitude we replace Ms with
M. Seismic intensity are estimated using an empirical attenuation relationship,
which is for describing the attenuation of intensity with distance. To derive maximum seismic intensity every location based on seismic intensity attenuation
formula used the visual basic program to calculate the hypocentral distance and intensity. All the spatial data were processed using GIS Tools software used to
plot contour of seismic intensity at the area interest. The analyses used in this research are descriptive analysis. The descriptive analysis were using super
imposed method of maps to describe the characteristic and facts of destructive earthquake based on distance to epicenter, seismic intensity value in the study
area. Earthquake intensity classification can be description in Table 1.
Table 1 Earthquake Intensity Classification FEMA,2001
MMI Description potential Damage
I, II, III, IV, V VI, VII
VIII, IX X, XI, XII
None to very light damage Moderate to Heavy damage
Heavy damage Very Heavy damage
2. Microtremor
Microtremor is also called ambient noise. Noise is the generic term used to denoted ambient vibration of the ground and floor caused by sources such as tide,
turbulent wind, effect of wind on trees or buildings, industrial machinery, cars and trains, human footsteps, oceanic wave, volcanic tremor, etc. Microtremor
measurement is one of the practical methods to estimate the effect of ground motion characteristics due to an earthquake. Although there are uncertain things
geophysically, this method can be a very useful tool in identifying seismic ground motion amplification for earthquake hazard assessment, because it is very simple
and economical in operation. The purpose of this study is to evaluate the characteristics and usage of microtremors. Those parameters are predominant
period, classification of soil conditions and amplification ratio in site location.
10 a. Microtremor Measurements
Microtremor measurements were carried out by using very high sensitive seismometers with servo system. The
Seismometer short period type DS-4A 3 components dan TDL 303 portable digital seismograph, with sampling rate
frequency100 Hz, can be observing ground motion for the period ranging from 0.1
Hz to 50 Hz. More than 19 points microtremor measurements were conducted in Bangka Island. For each point of measurement 20 minutes of ambient noise
recorded. We have conduct 2 to 3 records in each site for verifying the stability of microtremors. Figure 5 shows the locations of microtremor measurements
represented on administration map of Bangka Island.
b. Microtremor Processing In processing data we used Geopsy software,. This software contains
information of recording time, the amount of data and other supporting data. The result is a spectrum at each point will then be analyzed to obtain HVSR peak
value A and predominat frequency fo. The data processing to obtain the HVSR at each site was performed in the following way:
1.
Fourier transformation: firstly, Fourier spectra of the selected segments of two horizontal and the vertical components are calculated using the fast
Fourier Transform FFT algorithm. As the Fourier spectra of the two horizontal components looked alike, their horizontally combined spectra were
calculated to obtain the maximum Fourier amplitude spectrum as a complex vector in the horizontal plane, while the one vertical component provided the
vertical motion spectra.
2. Smooting of the spectra: Secondly, digital filtering has been employed on the
combined horizontal and vertical spectra applying a logarithmic window with a bandwidth coefficient equal to 15 . This filtering technique is applied to
reduce the distortion of peak amplitudes.
3. Calculation of transfer functions: the smoothed combined horizontal spectrum
are divided with the vertical spectrum using equation 1 given below, which provided the soil response in term of amplified periods of the investigated
portion 20.48s of records.
R f =
√
FNS f FEWf FUDf 4 4.
Where Rf is the horizontal to vertical spectral ratio and FNSf, FEWf and FUDf is the Fourier amplitude spectra in the North South componentsNS,
East West EW and Up Down Directions respectively. 5.
Normalizing the data set: After obtaining the HV spectra of the tree segments, the average of the spectra are obtained as the HV spectrum fror a
particular site as a relatively non-biased response. From this spectrum we can determine the value of the dominant frequency fo and peak spectral ratio
HV A at the measurement site microtremor. Based on the relationship T = 1fo then we will get the value of a dominant period in the measurement site
Sesame, 2004
11 Based on the microtremor measurements analysis HVSR determine the
predominant frequency fo and amplification factor Am. In addition to assigning a predominant period class to each recording site,
we also classify each site to the four classes as rock, hard soil, medium soil, and soft soil as defined by Molas Yamazaki 2005. See also Table 2, which shows
the approximately corresponding site classes defined by the Japan Road Association 1980 and the approximate correspondence with NHERP site classes
2000.
Table 2. Site Class Definition Based on Predominant period Molas et.al,2005
Description Natural Period
Rock Hard Soil
Medium Soil Soft soil
T 0.2 sec 0.2 ≤ T 0.4 sec
0.4 ≤ T 0.6 sec T
≥ 0.6 sec Soil amplification is a main factor influencing the distribution of damage and
causalities in urban areas when large earthquake occur. Thus, it is important to define how the seismic waves are affected by recent and non
–consolidated geological deposits to better quantify the ground motions at surface. The higher
amplification factors of a soil type under a certain frequency of seismic wave, the higher the degree of hazard for the structures of that frequency. Therefore, the
ranks of the seismic hazard have been differentiated into four classes based on the amplification factors which are given in Table 3.
Figure 5 Location of Microtremor measurements
12 Table 3 Ranking of ground shaking hazard based on amplification factors
Kamal,et.al,2006 Amplifications
Ranks 1.0
– 2.5 2.5
– 3.5 3.5
– 4.5 4.5 - 7
Very low hazard Low hazard
Moderate hazard Relative High Hazard
3. Slope
Slope is defined by a plane tangent to a topographic surface, as modeled by the DEM at a point Burrough, 1986. Slope presents the percent change in
elevation over a certain distance. The output slope can be calculated as either the percent or degree slope. In this study, percent of slope was chosen.
Slope is an important factor while considering the ease of engineering construction and susceptibility land sliding cause by earthquake. Slopes are
particularly vulnerable to bedrock failures. Keefer1984,1993 noted that more than 90 percent of earthquake induced slope failures on rock slopes were rock
falls and rock slides. The physical characteristics of the rock masses underlying steep slopes are of fundamental importance in evaluating their susceptibility to
slope failure. Therefore, the slope layer will only contribute to determine the earthquake hazard areas in ease of engineering constructions, since steep slopes
interfere with excavation processes. The slope map was prepared in degrees using DEM of the study area. Afterwards, the slope values were subdivided into four
main classes according AGS Sub-Commite Australian Geomechanics 2002 Slopes between 0 and 9 were assigned as the flat class, slopes between 10 and
15 were assigned as moderately steep class, slopes between 16 and 20 were assigned as very steep class and slopes greater than 20 were assigned as the
extremely steep class. Table 4 shows the degrees and description of slope class.
Table 4 Slope Classification Andrian, 2009 Percent of Slope
Information Hazard Categories
– 9 10
– 15 16
– 20 20
Flat Moderate Steep
Very Steep Extremely Step
Very Low Moderate
High Very High
4. Fault Distance A fault is a break in the rocks that make up the Earth’s crust, along which
rocks on either side have moved past each other. Fault have an important role in determine earthquake hazard areas, because the faults have a main effect to
movement of earth layers after earthquake.
13 Table 5 Classification fault based on distance Mohsen et.al., 2012
Distance Earthquake hazard
– 30 km 30
– 50 km 50 km
Very Damaging effect Damaging effect of moderate
Negative effect of poor Determining earthquake prone areas of useful measures to reduce the
severity of the damages is considered, it is hereby be limited use of high risk areas. Construction of some buildings for nuclear plant in these zones can prevent
by identifying risk zones in cities in low risk areas of vital arteries decided. Fault as seismic sources, including plate movement and withdraw are major factors,
thus, away from the faults could be considered as one of main parameters of determine earthquake hazard areas. Classification fault for significant level of
earthquake hazard based on distance from the fault defined by Mohsen et.al,2012 shown in Table 5.
5. Lithology
Lithology is the description of rock composition and texture. The geological formations type and condition are closely related to the landslide after earthquake
occurrence. Lithology data was extracted from geological map. This data had been prepared for common geological functions without take into account the special
needs of the earthquake hazard evaluation. The geological units were regrouped based on lithological attributes rather than their stratigraphic content and age.
Geology units strongly influences slope stability and it is clear that there exists an associated between types of lithology material. However, this
association may be strong or weak largely depending upon the type of lithology material. Lithology is one main factors influencing the type and the intensity of
the earthquake ground shaking. Based on the geological map scale 1: 250,000 published by Geological Research and Development Centre of Indonesia, various
rock formations in the study area have been grouped to prepared the lithology data layer. The lithology classification for determine earthquake hazard areas is shown
in table 6
Table 6 Classification of Lithology Bealand, 1996 Lithology Age
Hazard Level Quarternary 2.5 Myrs
Tertiary 2.5 ≤ Age ≥ 75 Myrs Basement Rock 75 Myrs
Least Hazardous Quite hazardous
Not very hazardous
Simple Additive Weighting SAW
After the data was prepped and ready to analyze, it was time to implement the steps of the SAW method in Spatial Analyst. This process was almost entirely
carried out using the raster calculator. Once the layers were reclassified, the next step was to standardize the values. In this method, all of the layers are
concurrently considered in assigning weight values, and all classes of each layer
14 are also concurrently considered while assigning rank values. As a result, six
weight values were assigned to the six layers. Simple Additive Weighting which is also known as weighted linear
combination or scoring methods is simple and most often used in multi attribute decision technique. The method is based on the weighted average. An evaluation
score is calculated for each alternative by multiplying the scaled value given to the alternative of that attribute with the weights of relative importance directly
assigned by decision maker followed by summing of the products for all criteria.
Simple Additive Weighting SAW is calculated using the following formula:
A
i
=
Ʃ
W
j
X
ij
5 Where X
ij
is the score of the ith alternative with respect to the jth attribute and W
j
is the normalized weight. The weights represent the relative importance of the attributes. The most preferred alternatives is selected by identifying the maximum
value of A
i
, i = 1, ….,m. The SAW methods can be implemented using GIS having overlay
capabilities. The GIS based Simple Additive Weighting method involves the following steps Malczewski, 1999:
1. Definition of the set of evaluation criteria map layers and the set of feasible
alternatives,, 2.
Standardization of each criterion map layer, 3.
Definition of the criterion weights; that is, a weight of “relative importance” is directly assigned to each criterion map; that is, multiply standardized map
layers by corresponding weights, 4.
Construction of the weighted standardized map layers, 5.
Generation of the overall score for each alternative using the overlay operation on the weighted standardized map layers,
6. Ranking of the alternatives according to the overall performance; the
alternative with highest score is the best alternative. Simple Additive Weighting method is the most often used method in multi
attribute decision rules. The method can be operationalized using any GIS system having overlay capabilities. The overlay techniques allow the evaluation criterion
map layers input maps to be aggregated in order to determine the composite map layer output map.
It should be emphasized that, there are two strong assumptions implicit in the SAW method; the linearity and additively attributes. The linearity assumption
means that the desirability of an additional unit of an attribute is constant for any level of that attribute. In many spatial decision situations these two assumptions
are very difficult to apply. Because of the complementarities between different attributes, the SAW method may lead to false results.
15
3. RESULTS
Kategori