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On the computation of H/V and

its application to micro zonation and seismic

Jos Antonio

MARTNEZ

-

GONZLEZ

Mathieu

Perton,

Javier

Lermo

, and F

. J.

Snchez

-

SesmInstituto

de Ingenieria,

UNAM

2nd AXA UNAM Workshop

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Traditionally H/V:

- allows good estimation of fundamental frequency of

- has a bad recovery of amplification factor

- Is not reliable

- INTRODUCTION

Objectives

Methodology

Data acquisition

Data processing

Validation of results

Synthetic time-

series

- SHAKEMAPS

- CONCLUSIONS

Introduction

Why?

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Subduction

Introduction Seismic Hazard in Mexic Sources Paths Site effects

1D effect

3D effec

The earthquakes in Mexico are of 3 types

Subduction Zonemotion mainly composed of surface waves

Intraplate Zonemotions are composed of surface & body wa

Local Events ground shaking mainly composed of body wave

De

intra

- INTRODUCTION

Objectives

Methodology

Data acquisition

Data processing

Validation of results

Synthetic time-

series

- SHAKEMAPS

- CONCLUSIONS

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We also distinguish the events inrecording site. Mexico City has tygeotechnical zones :

Former Lake surface waves (loamplification,

Transition body waves (high framplification,

Hill Zone response sensitive to

Introduction: path and site ef- INTRODUCTION

Objectives

Methodology

Data acquisition

Data processing

Validation of results

Synthetic time-

series

- SHAKEMAPS

- CONCLUSIONS

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Introduction

Objectives

- Compare H/V from earthquakes (HVSR) with H/V from noise

- Establish shakemaps for average peak ground accelerations for the maximum acceleration structural period and 5% dam

Outline

- Data acquisition for seismic noise

- Data processing applied to seismic noise

- Data processing applied to earthquakes

- INTRODUCTION

Object ives

Methodology

Data acquisition

Data processing

Validation of results

Synthetic time-

series

- SHAKEMAPS

- CONCLUSIONS

Introduction

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We record seismic noise at 8 accelerometric stationsCity

One at Hill Zone,

Another at Transition Zone,

Six more at the former Lake Zone

Use of broad-band seismograph

Two months of continuous recording

- INTRODUCTION

Objectives

Methodology

Data acquis it ion

Data processing

Validation of results

Synthetic time-

series

- SHAKEMAPS

- CONCLUSIONS

Data acquisition for noise

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Noise processing:Classical Method (i.e. Geopsy)

This formula is appropriate

to remove source effectsin any window

Processing Parameters

Baseline correction

Filter Passband: 0.1 30 Hz,

Window length: 81.92 s

Disable tapper and

smoothing options

Z

N

E

1

1[ / ] [ / ]( )n

H Hn

V V i

Window length

[ / ]H V i

H

- INTRODUCTION

Objectives

Methodology

Data acquisition

Data processing

Validation of results

Synthetic time-

series

- SHAKEMAPS

- CONCLUSIONS

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Noise processing:Diffuse-Field Method [ / ] NS EW

ZZ

E EH V

E

Based on Greens function retrieval

so we apply usual data processingAdvanced filters (Bensen, 2007)

Processing Parameters

Baseline correction

Passband filter: 0.1 30 Hz

NO tapper, NO smoothing.

Window length: 81.92 s

11ImG NN SE U

Window length

- INTRODUCTION

Objectives

Methodology

Data acquisition

Data processing

Validation of results

Synthetic time-

series

- SHAKEMAPS

- CONCLUSIONS

ImGE W

E

Z ZE

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Applying advanced filters (Bensen, 2007)

- Spectral Whitening:spectral source deconvolutionin each time window

- 1 bit: this filter removestransitory effects (mainlybody

body waves)

- Earthquake has the signature ofbody waves

- INTRODUCTION

Objectives

Methodology

Data acquisition

Data processing

Validation of results

Synthetic time-

series

- SHAKEMAPS

- CONCLUSIONS

Typical stati

Noise processing

b l l

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* Spectral whitening

0.1 1 1

1

10

20

30

SPECTRALRATIO

FREQUENCY [Hz]

DIFFUSSE-FIELD PROCESSING

0.1 1 10

1

10

20

30

SPECTRALRATIO

FREQUENCY [Hz]

CLASSICAL PROCESSING

1

2

3

- INTRODUCTION

Objectives

Methodology

Data acquisition

Data processing

Validation of results

Synthetic time-

series

- SHAKEMAPS

- CONCLUSIONS

*

Noise processing: Variability along

h k

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The coda is a representation of a diffuse field

For each earthquake type we apply the DFM processing

Signals are cut into windows

We add the density energy for each horizontal and vertic

Finally we compute the H/V spectral ratio

Processing Parameters:

Baseline correction

Filter Passband: 0.1 30 Hz,

Window length: 40.96, 81.92 s

Diffuse Field Method (DFM)

Z

N

E

- INTRODUCTION

Objectives

Methodology

Data acquisition

Data processing

Validation of results

Synthetic time-

series

- SHAKEMAPS

- CONCLUSIONS

Earthquake processing

HVSR i M i Ci f i

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Eq type Site FORMER LAKE ZONE TRANSITION ZONE

SUBDUCTION

seismnoise

(3d scatteringmainly surface

waves)

INTERPLATE

(surface & body

waves)

LOCAL

(mainly body

waves)

- INTRODUCTION

Objectives

Methodology

Data acquisition

Data processing

Validat ion of

results

Synthetic time-

series

- SHAKEMAPS

- CONCLUSIONS

HVSR in Mexico City as function o

0.1 1 10

1

10

2030

S

PECTRALRATIO

CJ03

0.1 1 10

1

10

2030

SPECTRALRATIO

FREQUENCY [Hz]

CJ03

0.1 1 10

1

10

2030

SPECTRALRATIO

CJ03

0.1 1 10

1

10

2030

SPECTR

ALRATIO

ES57

0.1 1 10

1

10

2030

SPECTRALRATIO

ES57

0.1 1 10

1

10

2030

SPECTRALRATIO

FREQUENCY [Hz]

ES57

0.1

100

101

SPECTRALRATIO

0.

100

101

S

PECTRALRATIO

0.

100

101

SPECTRALRATIO

0.1 1 10

1

10

2030

S

PECTRALRATIO

CJ03

0.1 1 10

1

10

2030

SPECTRALRATIO

FREQUENCY [Hz]

CJ03

0.1 1 10

1

10

2030

SPECTRALRATIO

CJ03

0.1 1 10

1

10

2030

SPECTRALRATIO

ES57

0.1 1 10

1

10

2030

S

PECTRALRATIO

ES57

0.1 1 10

1

10

2030

SPECTRALRATIO

FREQUENCY [Hz]

ES57

0.

100

101

SPECTRALRATIO

0.

100

101

SPECTRALRATIO

0.

100

101

SPECTRALRATIO

R d Vib i Th

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Fourier + Amplification + Duration = Response spe- INTRODUCTION

Objectives

Methodology

Data acquisition

Data processing

Validation of results

Synthet ic t ime-

series

- SHAKEMAPS

- CONCLUSIONS

Random Vibration Theory

100

200

300CJ03

Sa[cm/s

2]

CO47 CO56 CP28

100

200

300CS78

Sa[cm/s

2]

CT64 DM12 DR16

100

200

300DX37

Sa[cm/s

2]

EO30 ES57 FJ74

1 2 3 4

100

200

300GC38

PERIOD [s]

Sa[cm/s

2]

1 2 3 4

GR27

PERIOD [s]

1 2 3 4

HJ72

PERIOD [s]

1 2 3 4

IB22

PERIOD [s]

A

E

L li ti f H/V M i

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140 H/V spectralratios to generate

shakemaps

- INTRODUCTION

Objectives

Methodology

Data acquisition

Data processing

Validation of results

Synthetic time-

series

- SHAKEMAPS

- CONCLUSIONS

Localization of H/V on Mexico

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Subductionearthquake:Mw8.1

background- INTRODUCTION

Objectives

Methodology

Data acquisition

Data processing

Validation of results

Synthetic time-

series

- SHAKEMAPS

- CONCLUSIONS

Downtown

1990 - 2013

H, To are two variables that

become with time

Downtown

Subsidence veloci

1992 - 2000

4o

HT

Vs

(s)

Co

S bd ti

th k

M 8 1

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Subductionearthquake:Mw8.1Spectral acelerations (T=2s ~ 16-20 floors)- INTRODUCTION

Objectives

Methodology

Data acquisition

Data processing

Validation of results

Synthetic time-

series

- SHAKEMAPS

- CONCLUSIONS

1990 2013

L l

E th k

M4 5

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LocalEarthquake:M4.5- INTRODUCTION

Objectives

Methodology

Data acquisition

Data processing

Validation of results

Synthetic time-

series

- SHAKEMAPS

- CONCLUSIONS

Spectral acelerations (T=0.1s ~ 1-2 floors)

Interplate

earthq ake

M 7 1

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Interplateearthquake:Mw7.1- INTRODUCTION

Objectives

Methodology

Data acquisition

Data processing

Validation of results

Synthetic time-

series

- SHAKEMAPS

- CONCLUSIONS

Spectral acelerations (T=1s ~ 8-10 floors)

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CONCLUSIONS

Significant variability of H/V is observed in terms of ehypocenter location (subduction, interplate, local) wdifferent illumination regime

The H/V from noise (HVNR) can almost represent allearthquakes (HVSR) in Mexico City

It is possible to use microtremores to get trustworth

amplification factor (aproximately an Empirical Trans

In sites with low seismicity, microtremors now are uestimate the amplification factor obtained with eart(under certains considerations)

- INTRODUCTION

Objectives

Methodology

Data acquisition

Data processing

Validation of results

Synthetic time-

series

- SHAKEMAPS

- CONCLUSIONS