dissertation report- pranshu ratre

8/10/2019 Dissertation Report- Pranshu Ratre

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INVERSION OF STRONG MOTION ACCELERATION DATA FOR

DETERMINATION OF 3D ATTENUATION STRUCTURES

A DISSERTATION

Submitted in partial fulfilment of the

Requirements for the award of the degree

Of

INTEGRATED MASTER OF TECHNOLOGY

In

GEOPHYSICAL TECHNOLOGY

By

PRANSHU RATRE

DEPARTMENT OF EARTH SCIENCES

INDIAN INSTITUTE OF TECHNOLOGY ROORKEE

ROORKEE- 247667 (INDIA)

JUNE, 2014


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CANDIDATES DECLARATION

I, here by, certify that the work which is being presented in this dissertation entitled Inversion

of Strong Motion Acceleration Data for determination of 3D attenuation structures in

partial fulfilment of requirement for the award of INTEGRATEDM.TECH Degree in

GEOPHYSICAL TECHNOLOGY submitted in the Department of Earth Sciences, Indian

Institute of TechnologyRoorkee, is an authentic record of my own work, carried out under the

supervision of Dr.Anand Joshi, Department of Earth Sciences, IIT Roorkee.The matter embodied

in this dissertation has not been submitted by me for the award of any other degree of this or any

other institution.

Date: PranshuRatre

Geophysical TechnologyPlace: IIT RoorkeeDepartment of Earth Sciences

Indian Institute of Technology Roorkee

This is to certify that the above statement made by the candidate is true to the best of my

knowledge.

Dr.Anand Joshi

ProfessorDepartment of Earth Sciences

Indian Institute of Technology Roorkee


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CERTIFICATE

I, PranshuRatreherebysolemnly declare that the dissertation entitled Inversion of Strong

Motion Acceleration Data for determination of 3D attenuation structuresbeing submitted

by me towards partial fulfilment of the requirement for the award of Integrated Masters of

Technology in Geophysical Technologydegree is a record of my own work and that I have not

copied the work of any other person(s) including published literature and material from any

website.

Wherever the work of other person(s) has been used, it has been duly acknowledged and quoted

with proper reference to the original work.

I fully understand the implications of plagiarism and that if at any stage the above statement

made by me is found to be incorrect, I shall be fully responsible for my act(s).

Place- Roorkee Name-PranshuRatre

Date- Geophyiscal Technology


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ACKNOWLEDGEMENT

Firstly, I would like to extend my deep gratitude to my supervisor Dr. AnandJoshi ,whose

constant guidance and encouragement throughout my thesis helped me generate a keen

interest and focus in my work. Through my interactions with him for one year, Ihave learnt

extensively from him on how to approach a geophysical problem. I am greatly thankful for

his efforts and patience without which I would not have accomplished my work.

In the past one year, I have learnt a lot not only about my subject but also I will carry many

life lessons from this experience. I am really thankful to the Department of Earth Science

for giving me this opportunity to explore my field of study.

I would also like to thank my friend Arpit who helped me throughout the project giving me

ideas to improve my results. I would also like to thank my friends Pulkit and Arushi who

were my constant moral support and helped me in writing this report.

Finally I would like to thank my family for giving me the extra push whenever I needed it.


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ContentsABSTRACT ...................................................................................................................................................... 1

CHAPTER 1: INTRODUCTION ......................................................................................................................... 2

1.1 Background ......................................................................................................................................... 2

CHAPTER 2: METHODOLOGY ........................................................................................................................ 4

CHAPTER 3: DATA DESCRIPTION ................................................................................................................. 12

CHAPTER 4: CASE STUDY-3D TOMOGRAPHY OF CENTRAL HONSHU (Kanto and Chubu regions), JAPAN . 15

4.1 Tectonic Setting of the Central Honshu Region ................................................................................ 15

4.2 Numerical Experiment for the Data Set ............................................................................................ 17

CHAPTER 5: CONCLUSIONS ......................................................................................................................... 19

5.1 Results and Discussions .................................................................................................................... 19

5.2 Conclusions ....................................................................................................................................... 32

REFERENCES ................................................................................................................................................ 33

APPENDIX .................................................................................................................................................... 34


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LIST OF FIGURES

Figure 1: Initial model and model obtained after first iteration having new quality factor value (Qijk) in

different blocks; i, j,k.

Figure 2: Flow chart of the process of inversion (After Joshi et. al. 2010)

Figure 3 : Topographic Map of Central Honshu Region (Ref. http://www.glgarcs.net/) Figure 4 : Contour of quality factor values for 1 Hz frequency obtained using the data set at the surface (0

km)

Figure 5 : Contour of quality factor values for 1-Hz frequency obtained using the data set at 5 km beneath

the surface

Figure 6 : Contour of quality factor values for 1-Hz frequency obtained using the data set at 10 km

beneath the surface

Figure 7 : Contour of quality factor values for 5-Hz frequency obtained using the data set at surface

Figure 8: Contour of quality factor values for 5-Hz frequency obtained using the data set at 5 km beneath

the surface

Figure 9 : Contour of quality factor values for 5-Hz frequency obtained using the data set at 10 km

beneath the surface

Figure 10 : Contour of quality factor values for 10-Hz frequency obtained using the data set at the surface

Figure 11 : Contour of quality factor values for 10-Hz frequency obtained using the data set 5 km beneath

the surface

Figure 12 : Contour of quality factor values for 10-Hz frequency obtained using the data set 10 km

beneath the surface

Figure 13 : Resolution matrix for 1Hz frequency


Figure 15 : Resolution matrix for 10 Hz frequency


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ABSTRACT

Three island arcs, the Northeast Japan Arc, the Southwest Japan Arc, and the Izu-Bonin Arc

meet in the Central Honshu (the Kanto and Chubu regions) region of Honshu Island in Japan.

This region has very complicated geomorphological and geological structures due to intensive

crustal movement. Three dimensional S-wave quality factor (Q) values are obtained for the

region through the inversion of strong motion acceleration data of twelve earthquakes in the

region (M 4 -5.4). The algorithm used is based on the method of inversion given by HASHIDAand

SHIMAZAKIfor determination of three-dimensional attenuation coefficients. It has also been used

and further modified by JOSHI et al. (2010).The earthquake data used was recorded by strong

motion stations of Kik-net network. Borehole data has been used as it has least possible site

effect and a high signal to noise ratio. The entire region has been divided into 25, 3-dimensional

blocks having the same thickness but different frequency dependent S-wave quality factor. A

comparison is made between the obtained attenuation structure and the geological features

present in the region and it shows that the obtained structure is capable of resolving relevant

tectonic features present in the region.


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CHAPTER 1: INTRODUCTION

1.1 Background

Seismic waves when passing through a medium decrease in amplitude due to various factors like

spherical spreading, mechanical and other loss mechanism. This loss in energy is measured in

terms of attenuation which can be mathematically defined as the inverse of Quality factor (Q)-

Quality factor is the ratio of stored energy to dissipated energy in one cycle of the wave (wave

length). The heterogeneity and inelasticity in the earth cause the attenuation of seismic waves as

they travel from the source to a particular site. It is observed that as a wave travels from the

source, its travel path and local site conditions effects the amplitude of the wave. It is the

attenuation property of the medium that affects the ground motion of a particular site during

tectonic movements.

The strong motion data is a record of the ground acceleration during an earthquake event. It is

observed that the peak ground acceleration recorded is connected with the S-wave arrival

(HADLEYand ORCUTT 1982). It is also demonstrated by MIDORIKAWA (1980) that the Quality

factor and shear wave velocity can be linked with each other empirically. Thus, by estimating the

Shear wave (S-wave) Quality factor (Q), we can directly estimate the rock properties and seismic

hazard in the region. In this dissertation, S-wave quality factor is utilized to determine the 3-D

attenuation structures for Chburegion in (Central) Honshu region of Japan.

HASHIDAAND SHIMAZAKI(1984) have developed an inversion algorithm to find the attenuation

structure which they used to determine the 3-D attenuation structure in northeastern part of Japan

beneath the Tohoku and Kanto regions (HASHIDAAND SHIMAZAKI, 1985, 1987). This algorithm

was further modified in JOSHI (2006, 2007) and JOSHI et al. (2010) for determination of 3-D


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attenuation structure. The algorithm proposed in JOSHI (2006), minimizes the error between

initial and final model to derive the attenuation structure.

Attenuation structures are used in seismological studies to construct seismic zonation map of a

region. High seismic attenuation properties signify low seismic hazard in a region.


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CHAPTER 2: METHODOLOGY

It is generally assumed while interpretation of strong motion record that peak ground

acceleration A is related to arrival of S-waves that are generated from a point source below the

ground. In order to obtain vertical and spatial distribution of shear wave quality factor Qb(f)

along three different perpendicular directions the study area is divided into 3 dimensional blocks

of different Q(f) values. This is the same as representing the entire study area into rocks having

varying attenuation properties. For computing spectral acceleration values at different

frequencies the relation given by HASHIDAand SHIMAZAKIis used:

, (1)Where:

A(f) : Spectral acceleration values

S(f) : Source spectral acceleration at frequency f

Gr: Geometrical factor (assumed as the inverse of hypocentral distance)

Tijk : time spent in the ijkth

block of attenuation coefficient Dijk

g : amplifying effects at the surface of earth ( = 2.0)

The coefficient of attenuation represents the property of the medium and it is the inverse of the

quality factor. Its values determine the attenuation characteristics of the medium; a high value

causing the wave to attenuate faster and low value allowing easier passage of the wave through

the medium. The parameters Qijk(f) and f represent quality factor and frequency respectively.

The subscripts i and j are used for identifying blocks in X and Y directions which are


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perpendicular to each other, in the horizontal plane, as shown in Fig.1.The subscript k is used

for identifying block in downward direction. In the iterative process of inversion, an initial

distribution of Qijk,o(f) is assumed within each block. The initial distribution is assumed from

available geophysical data of the region. The value of S(f) is required for forward modelling

using Eq.1. The S(f) is mean of several frequency dependent source strength (f) calculated atdifferent observation points op and is represented by following equation.

, (2)

In the above relation Aop(f) is the obtained spectral acceleration value at frequency f at the

observation point,opin the surface grid.


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Initial Model

Observation points for Acal

Figure 1: Initial model and model obtained after first iteration having new quality factor

value (Qijk) in different blocks; i, j,k.


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To compute(f) from Eq. 2, initial subsurface model defined by velocity and Q in everylayer is required. If the shear velocities are varied in different blocks to be used as independent

parameters, an extra set of data is required. So, to minimize the number of independent

parameter layered velocity model is used as an input model as represented by HASHIDA and

SHIMAZAKI(1984) and JOSHI (2007). The Scal

(f) is computed by using mean value of (f)obtained from all observation points. Initial model having attenuation coefficient Dijk,oand S

cal(f)

is used to compute Acal

(f) and is given by following relation:

, (3)

Assuming the observed spectral value at ij observation point to be (f), the relation betweenobserved spectral acceleration (f), actual source strength So(f) and actual attenuationcoefficient Dijkis given by the following expression.

, (4)The parameter Tijkis the time spent by the ray from source to the observation point in the 3-D

block given by subscript ijk. On dividing equation 4 and 3 we get:

(5)

In the above equation the parameter E defines the computational error which can be due to

inadequate model parameterization. Replace Dijk Dijk,o= Dijk in Eq. 5. Hence in Eq. 6 Dijk

represents the difference between initial and final attenuation coefficient in the ijkth block.


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(6)

The above equation is a non-linear equation which can be converted into linear equation by

taking its natural logarithm. This gives following form of above equation:

()

(7)

Assuming, , as acceleration residual, Eq. 7 can be written as:

(8)

In the above equation the LHS is known and the parameter T ijkis calculated by ray theory. The

analytical approach given by LEEand STEWART(1981) can be used for tracing ray from source to

observation point. Many equations have been obtained for particular earthquakes. The equation

representing data for first earthquake can be written as (JOSHI2007):

(9)

The parameter e in the Eq. 9 denotes error estimate. The parameter denotes time spent bythe ray from first event in various blocks to reach the station represented by superscript op from


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event 1. The data of N earthquakes can be represented in the below matrix given by (JOSHI2007).

the above matrix equation can be written as:

G m = d. (10)

Matrices m andd represents model parameters and data respectively. The variation of

subscripts i,j and k ranges from 1 to L, M and K respectively, and the variation of superscripts o

and p ranges from 1 to Lsand Msrespectively. The total number of blocks Nb=L*M*K and total

number of observation points are Ns= Ls*Ms. Hence we have Ns=Ne*N and Np=N+Nb as

equations and parameters respectively. Since matrix G is a rectangular matrix hence by using

Newtons method we get the following model matrix:

m = (GTG)

-1G

Td (11)

In practical case some of the eigenvalues of GTG are very small hence the variance of solution

becomes unacceptably large.Damped least square method of LEVENBERG(1944) is used to avoid


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the above difficulty because it does not require eigenvalues analysis. The inversion equation

using damped least square method is:

mest

= (GTG + I)

-1G

Td (12)

In the preceding equation is the damping factor and Iis a unit matrix. The above equation can

be solved by minimizing |d-Gm|+ mt(I)m instead of |d-Gm|

2, where (I) is a diagonal matrix

with damping factors. The is chosen based on method given by DIMRI(1992) and used by

JOSHI(2007).

The estimation of unknown quantities like and Dijk =(Dijk Dijk,o) can bedone by inversion algorithm. This is an iterative procedure in which after each inversion Qijk,ois

replaced by Qijkwhich can be represented by following expression:

Qijk(f)=Qijk(f)+Qijk,o(f) (13)

In above equation Qijk(f) represents small change in quality factor. In each consecutive iteration

the value of Qijk(f) is taken as initial model, replacing Qijk,o(f). Now themodified initial model

So(f) is again calculated at every observation point and its mean value is used to calculate Acal

(f).

This process againgenerates a new set of equations for inversion. The inversion is repeatedly

performed until solution with minimum error is reached. The entire inversion can be summarized

in the form of a flow graph in the Figure 2.


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Figure 2: Flow chart of the process of inversion (After Joshi et. al. 2010)


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CHAPTER 3: DATA DESCRIPTION

The nationwide seismic observation networks have been constructed by National Institute for

earth science and disaster prevention. The sub set of HI- net (high sensitivity seismograph

network) is kik net (KIBAN kyoshin network) and it consists of 660 strong motion observation

stations. This network also has surface as well as borehole data. The KiK-net stations have

borehole of 100-200m in depth. Records from borehole sensor at different KiK stations have

been used to use records which are free from site amplifications. Each KiK station is having a

digital strong motion seismograph having a wide frequency band and wide dynamic range of

measurable acceleration which is 2000 gal. The frequency of sampling the data is 200 Hz. The

Kik-net stations are equipped with double three component accelerometers of which one of them

is placed at the surface and other is placed in the borehole at every station. Hence strong motion

seismographs of Kik-net have 6 acceleration sensors. The acceleration data has been acquired

from official website of Kik-net. In the present work 12 earthquakes readings from 30 stations

across Honshu region have been used. The details of the 30 stations that have recorded these

earthquakes have been given in Table 1. Input velocity model has been taken from HONDA et. al.

(2005). The locations of earthquakes and recording stations are shown in Fig 3. The records have

been processed by procedure suggested by BOOREand BOMMER2005. The processing involves

frequency filtering, baseline correction and instrument scaling. A MATLAB script is developed

for the pre-processing of data. (Appendix 1). The size of study area is 110*110 km in central

Honshu region of Japan. For attenuation tomography the whole region is divided into 5, 5 and 3

rectangular blocks aligned along X, Y and Z axis. This provides with 36 points of observation. In

present scenario strong motion data of 12 earthquakes gives 432 equations and 87 parameters.


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Table 1 Detail of the recording stations used in present work (http://www.kik.bosai.go.jp).

STATION CODE LATITUDE (N) LONGITITUDE (E)

GIFH11 35.4865 137.2464

GIFH14 36.2493 137.5174

GIFH15 36.1338 137.2208

GIFH16 36.094 137.3438

GIFH18 35.8991 137.1495

GIFH19 36.0216 137.3906

GIFH20 35.7991 137.2531

GIFH22 35.6682 137.1054

GIFH24 35.6401 137.3187

GIFH27 35.4527 137.004

GIFH28 35.4571 137.4706

NGNH03 35.4786 137.7346

NGNH07 36.7434 138.376NGNH08 36.2541 137.8591

NGNH09 36.2859 138.2491

NGNH10 35.9632 137.7669

NGNH11 35.9157 138.3052

NGNH12 35.9696 138.4797

NGNH13 35.5143 137.8767

NGNH14 35.3095 137.6261

NGNH15 36.0088 137.9305

NGNH16 35.9465 138.1848

NGNH17 36.1425 138.5504

NGNH18 35.9324 137.595


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Table 2 :Hypocentral parameters of the events used in present work

EventsOrigin Time

(hh:mm:sec)Depth(km) Magnitude

Epicenter

Latitude(N) Longitude(E)2003/05/18 03:23:00 7.000000 4.5 35.866000 137.595000

2004/07/27 00:55:00 11.000000 4.5 35.758000 137.107000

2012/01/28 07:43:00 18.000000 5.4 35.488000 138.977000

2002/12/0408:09:00

8.0000004.2

35.870000 137.594000

2005/01/02 01:30:00 6.000000 4.2 35.868000 137.574000

2008/06/13 11:21:00 13.000000 4.7 35.910000 137.703000

2004/01/11 16:57:00 8.000000 4.0 36.398000 137.980000

2008/05/12 11:47:00 10.000000 4.0 35.792000 137.973000

2012/01/28 07:39:00 18.000000 4.9 35.492000 138.978000

2013/02/15 18:18:00 11.000000 4.2 35.777000 138.038000

2003/04/01 09:25:00 8.000000 4.1 35.941000 137.520000

2004/01/11 16:57:00 8.000000 4.1 35.940000 137.529000


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CHAPTER 4: CASE STUDY-3D TOMOGRAPHY OF CENTRAL HONSHU

(Kanto and Chubu regions), JAPAN

4.1 Tectonic Setting of the Central Honshu Region

Honshu Island is a mountainous and volcanic region, prone to frequent tectonic activities. The

Great Kantearthquake caused heavy damage in Tokyo in September 1923, and the Tohuku

earthquake of March 2011 moved the north-eastern part of the island as much as 5.3 m.

The broadest region of the Honshu Island is qualified as Central Honshu having the Kanto and

Chubu regions in it. It is characterized by the meeting of three island arcs namely, the North-east

Japan Arc, The South-west Japan Arc, and the Izu-Bonin Arc. The region is divided by the

Itoigawa-Shizuoka Tectonic Line (ISTL), the Median Tectonic Line (MTL), and the volcanic

front. The South-west and the North-east Japan Arc demarcatedby the ISTL, which is a fault-

zone running north-south in the Chubu region of Honshu. The MTL consists of right lateral

strike slip faults, running for over a 1000 km. The MTL divides the South-west Japan Arc into

two zones: Outer zone (lying on the Pacific side) and Inner zone (the continental side). The

volcanic front runs along the extension of the North-east Japan Arc turning towards the Izu-

Bonin Arc in front of the ISTL. Similar to ISTL, the volcanic front divides the North-east Japan

Arc into Inner (Northern side) and Outer (Southern side) Arcs.

On the eastern side of ISTL lies the Kanto Plain, which is the largest plain in Japan. A thick layer

of Quaternary sediments including huge amount of volcanic ash cover the plain. The volcanic

ash comes from the volcanoes like Hakone and the Fuji which lie on the western side of the

plain. ISTL serves as the western boundary of Fossa Magna, a tectonic depression zone, which

subsided when the Japanese islands displaced from the continent margin to the present


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locationand then uplifted. The Fossa Magna depression is very deep; the Neogene sediments

accumulated in the region are more than 5 km thick y over 10 km thick in some regions in the

southern part of the zone. Large uplift rate have generated lots of faults and folds in the

sedimentary rocks. During the expansion of Sea of Japan, intensive volcanism led to the

deposition of volcanic rocks in the Fossa Magna.

Figure 3 : Topographic Map of Central Honshu Region (Ref. http://www.glgarcs.net/)


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4.2 Numerical Experiment for the Data Set

The computer software named 3-D frequency dependent Q structure (JOSHIet. Al. 2010) has been

used in this thesis. This algorithm takes the following inputs: Initial velocity model, initial Q(f)

model, coordinated of the hypocentres of earthquakes, spectral acceleration values at observation

points and the reference frequency at which attenuation structure is estimated. The spectral

acceleration values at the grid points are obtained through the interpolation of observed values at

various recording stations. From the data of 12 earthquakes, 432 spectral acceleration values at

36 observation points for a particular frequency are obtained.

Ratio of the observed and calculated spectral acceleration at source, and final value of Q(f) at

different blocks is given as the output of the program. Root mean square error for data (rms) dat

and the model matrix (rms)modis also computed for each iteration. The resolution matrix and the

covariance matrix check the fitness of the model matrix which is found out using the damped

least square inversion scheme. The resolution matrix R and the covariance matrix C can be

represented by the expression below: (HAYDARand MITCHELL1990).

(14) (15)

Where, d2is the variance of error in data given by :

..(16) & (17)


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Resolution matrix provides the association between estimated and actual model. A Unique

solution will be obtained when R is a unit matrix. Deviation of R from unit matrix shows poor

resolution. In this thesis, the total number of parameters (Np) is 87 and amongst these, 12

comprises of the source acceleration spectra of the events in consideration and rest 75 signify the

attenuation coefficient of various blocks. When all errors are minimized simultaneously in a

model, a reliable solution can be obtained. To arrive at a model that simultaneously minimizes

rmsdatand rmsmod, normalized value of the two variables is used :

(18)


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CHAPTER 5: CONCLUSIONS

5.1 Results and Discussions

Three dimensional attenuation structures have been found for frequencies 1, 5 and 10 Hz. The

subject are consists of the ISTL and MTL. On the eastern side of ISTL, Quaternary sediments

along with huge amount of volcanic ash cover the region. High quality factor values were

determined on the eastern side as compared to the western side of ISTL, which is because of the

existence of sediments in the depressed area on the eastern side. Also, it is observed that the

surface layer has low quality factor values as compared to the subsurface layers. This is due to

the presence of lose sediments in the upper layer. As we go deeper, the trend of contour lines

become parallel to the ISTL, revealing that the Q structure obtained matches with the geological

structure at different depths from 0-10 km.


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Figure 4 : Contour of quality factor values for 1 Hz frequency obtained using the data set

at the surface (0 km)


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Figure 5 : Contour of quality factor values for 1-Hz frequency obtained using the data set

at 5 km beneath the surface


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at surface


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Figure 8: Contour of quality factor values for 5-Hz frequency obtained using the data set at

5 km beneath the surface


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Figure 10 : Contour of quality factor values for 10-Hz frequency obtained using the data

set at the surface


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set 5 km beneath the surface


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set 10 km beneath the surface


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Figure 15 : Resolution matrix for 10 Hz frequency


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5.2 Conclusions

The 3 Dimensional attenuation structure is obtained for the Central Honshu region on the basis

of Q(f) at varying frequencies by using strong motion data recorded by KiK-net, Japan network

for 12 earthquakes.

The attenuation structure thus obtained gives comparable trends with the geological structure of

the study area. The results, obtained prove the robustness of the inversion algorithm developed

for the determination of attenuation structure in a region.


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REFERENCESBOORE, D. M., and BOMMER, J. J. (2005), Processing of strongmotion accelerograms: needs, options and

consequences, SoilDynEarthq Eng. 25, 93115.

DIMRI, V. P. (1992), Deconvolution and inverse theory: applicationto geophysical problems. Elsevier

Science, Amsterdam. 230.

HASHIDA, T., and SHIMAZAKI, K. (1984), Determination of seismicattenuation structure and source

strength by inversion of seismicintensity data: method and numerical experiment, J Phys Earth.

32, 299316.

HASHIDA, T., and SHIMAZAKI, K. (1985), Seismic tomography: 3-Dimage of upper mantle attenuation

beneath the Kanto district,Japan, Earth and Planet. Science letters, 75(4), 403409.

HASHIDA, T., and SHIMAZAKI, K. (1987), Determination of seismicattenuation structure and source

strength by inversion of seismicintensity data: Tohoku district, the northeastern Honshu, J. Phys.Earth.35, 5792.

HAYDAR, J., SHUKRI, A. l., and MITCHELL, B. J. (1990), Threedimensional attenuation structure in and

around the new Madridseismic zone, Bull Seismol Soc. Am. 80, 615632.

HONDA, R., AOI, S., SEKIGUCHI, H., MORIKAWA, N., KUNUGI, T., andFUJIWARA, H. (2005), Ground

motion and rupture process of the2004 mid Niigata Prefecture earthquake obtained from strongmotion

data of K-NET and KiK-net.

JOSHI, A. (2006), Three dimensional attenuation structure of thecentral seismic gap region of Himalaya

obtained from inversionof seismic intensity data, Curr Sci. 90, 581

585.

JOSHI, A. (2007), Inversion of seismic intensity data for the determinationof three-dimensional

attenuation structures in thecentral gap region of Himalayas, Nat Hazards. 43, 129146.

JOSHI, A., MOHANTY, M., BANSAL, A. R., DIMIRI, V.P., and CHADHA,R.K. (2010), Use of spectral

acceleration data for determinationof three-dimensional attenuation structure in Pithoragarh regionof

Kumaon Himalaya, J. Seismol. 14, 247272.

LEE, W. H. K., and STEWART, S. W. (1981), Principles and applicationsof microearthquake

networks.Academic, New York.293.


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APPENDIX

MATLAB PROGRAM FOR PRE-PROCESSING OF DATA

%%%%%%%%%%%%%%------%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%SPEC() function used in this program to find the elastic response

spectra was developed by @ MostafaTazarv, Carleton University, May

2011

clear

clcext=[{'*.NS1'} {'*.EW1'}];

%%directory containing the strong motion data files for all

earthquakes and different stations

eq_dir='D:\Dissertation\eqdata\';

%% directory containing the processed valuescreat_dir='D:\Dissertation\eqdata\data\processedValues\';

if(exist(creat_dir,'dir')==0)mkdir(creat_dir);end

k=0;

l=1;n=1;

%%reading coordinates from file

eq_file=dir([eq_dir,ext{:,1}]);file{:,1}=eq_file(1).name(7:16); %% for checking which files we haveread already so as to avoid writing multiple values of eqcoord

eq_filename=[eq_dir,eq_file(1).name];

socoord(1,1:2)=textread(eq_filename,'%*s %f',2,'headerlines',1);socoord(1,3)=textread(eq_filename,'%*s %*s %f',1,'headerlines',3);

for i=1:length(eq_file)eq_filename=[eq_dir,eq_file(i).name];

chk=eq_file(i).name(7:16);mat=strcmp(chk,file);


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if max(mat)==0l=l+1;

file{:,l}=eq_file(i).name(7:16);n=n+1;

socoord(n,1:2)=textread(eq_filename,'%*s

%f',2,'headerlines',1);socoord(n,3)=textread(eq_filename,'%*s %*s

%f',1,'headerlines',3);end

c=textread(eq_filename,'%*s %*s %f',2,'headerlines',6);

%% Considering stations only in the study area : Lat-35N-37N and%% Long-137E-139Eif 35


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%%corrections simultaneously--taking geometric mean and thus final%%acceleration values!!

for i=1:length(eq_file)

eq_NS=[eq_dir,eq_file(i).name];

eq_EW=[eq_dir,eq_file(i).name(1:16),'.EW1'];c=textread(eq_NS,'%*s %*s %f',2,'headerlines',6);

disp(['Scanning ',num2str(i), ' out of',num2str(length(eq_file))]);

if 35


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37

%%Finding Spectral Acceleration using SPEC()

[TNS,SpaNS]=SPEC(ftNS(1),acc_filteredNS,5,9.81,2);[TEW,SpaEW]=SPEC(ftEW(1),acc_filteredEW,5,9.81,2);

Spa(:,1)=TNS;

Spa(:,2)=sqrt(SpaNS.^2 +SpaEW.^2);for n=1:length(Spa)fprintf(spa_fid,' %g \t %g \t \r\n',Spa(n,:));end

end

end

spa_file=dir([creat_dir,'*.dat']);

%%Directory that will contain the spectral acceleration files for%%different frequencies

Hzdir='D:\Dissertation\eqdata\data\processedValues\final\';if(exist(Hzdir,'dir')==0)mkdir(Hzdir);end

a1fid=fopen([Hzdir,'acc1HZ.dat'],'w');a5fid=fopen([Hzdir,'acc5HZ.dat'],'w');a10fid=fopen([Hzdir,'acc10HZ.dat'],'w');

%%Spectral acceleration files have been prepared for 1Hz, 5Hz, 10Hz%%frequencies

for i=1:length(spa_file)

spafilename=[creat_dir,spa_file(i).name];original=[eq_dir,spa_file(i).name(1:16),'.NS1'];coord(1,1:2)=textread(original,'%*s %f',2,'headerlines',1);coord(1,3)=textread(original,'%*s %*s %f',1,'headerlines',3);coord(1,1)=coord(1)-35;coord(1,2)=coord(2)-137;stc(1,1)=stcoord(i,1)-35;stc(1,2)=stcoord(i,2)-137;

t=textread(spafilename,' %f %*[^\n]');s=find(t==1);a1=textread(spafilename,'%*f %f ',1,'headerlines',(s-1));

fprintf(a1fid,' %g \t %g \t %g\t%g \t %g \t %g\r\n',coord(1,:),stc(1,:),a1);

s=find(t==0.2);a5=textread(spafilename,' %*f %f ',1,'headerlines',(s-1));

fprintf(a5fid,' %g \t %g \t %g\t%g \t %g \t %g\r\n',coord(1,:),stc(1,:),a5);


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s=find(t==0.1);a10=textread(spafilename,'%*f %f ',1,'headerlines',(s-1));

fprintf(a10fid,' %g \t %g \t %g\t%g \t %g \t %g\r\n',coord(1,:),stc(1,:),a10);endfclose(a1fid);

fclose(a5fid);fclose(a10fid);

freq=[1 5 10]; %%Frequencies considered

for i=1:3

acc=load(['D:\Dissertation\eqdata\data\processedValues\final\acc',num2str(freq(i)),'HZ.dat']);

[A,B]=sort(acc(:,1));accnew=acc(B,:);

fid=fopen([Hzdir,'a',num2str(freq(i)),'final.dat'],'w');eqs = unique(accnew(:,1));

for j = 1:length(eqs)eq = eqs(j)data_for_eq = accnew(accnew(:,1) == eq,:);

[xq,yq] = meshgrid(0:.4:2, 0:.4:2);

aq = griddata(data_for_eq(:,4),data_for_eq(:,5),data_for_eq(:,6),xq,yq,'nearest');

size=numel(aq);

aq=reshape(aq,[size 1]);

for n=1:length(aq)fprintf(fid,' %g \r\n',aq(n));endend

end

dissertation report- pranshu ratre

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