Thesis: Geophysical Investigation of Carrizo Formation by Using Two- Dimensio...
Master_Thesis_HO_Final
1. i
MAPPING SITE RESPONSE ON CAL POLY POMONA CAMPUS USING
SPECTRAL RATIO ANALYSIS
A Thesis
Presented to the
Faculty of
California State Polytechnic University, Pomona
In Partial Fulfillment
Of the Requirements for the Degree
Master of Science
In
Geological Sciences
By
King Yin Kennis Ho
2015
2. ii
SIGNATURE PAGE
THESIS: MAPPING SITE RESPONSE ON CAL POLY POMONA
CAMPUS USING SPECTRAL RATIO ANALYSIS
AUTHOR: King Yin Kennis Ho
DATE SUBMITTED: Summer 2015
Geological Sciences Department
Dr. Jascha Polet _________________________________________
Thesis Committee Chair
Geological Sciences
Dr. Nick Van Buer _________________________________________
Geological Sciences
Ernest Roumelis PG, EG _________________________________________
Geological Sciences
3. iii
ACKNOWLEDGMENTS
I would like to express my deep gratitude to Professor Jascha Polet, my research
mentor throughout the years of undergraduate and graduate, for her patient guidance,
enthusiastic encouragement and useful critiques of this thesis research. I would like to
thanks my parents for continuous support and encouragement. My sincere thanks go for
my mentor student: Nicole Gage. My grateful thanks are also extended to my peers, Terry
Cheiffetz, Kevin Chantrapornlert, Raymond Ng, Julie Leiva, Michael Vadman, Dandan
Zhang, Mikey Herrman. Last but not the least, I would like to give special thanks to
Rachel Hatch and Rosa Nguyen for helping me to go through the time in the graduate
room.
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ABSTRACT
Site characteristics have a significant influence on earthquake hazard. To better
understand site response differences on a small scale, as well as the seismic hazard of the
area, we developed site response parameter maps of the Cal Poly Pomona campus.
We applied the Horizontal-to-Vertical Spectral Ratio (HVSR) technique, which is
an empirical method that can be employed in an urban environment with no
environmental impact. We installed broadband seismometers throughout the Cal Poly
Pomona campus, with a total number of 46 sites. The sites are spaced approximately 50
to 150 meters apart and about two hours of waveforms were recorded at each site. The
Geopsy software was used to produce measurements of fundamental frequency and
minimum site amplification factor. The reliability and quality of these measurements
were assessed using criteria from the Site Effects Assessment Using Ambient Excitations
(SESAME) guidelines.
Significant variability in both amplification and fundamental frequency is seen
across campus. Our measurements show some correlation with surface geologic units.
Sites on the alluvial deposits generally have a high minimum amplification of 4, whereas
amplification is around 2 at the hillside sites. The variation in resonance frequency on the
Southeast side of campus may be interpreted as indicating the existence of a shallowly
dipping (less than 5 degrees) impedance contrast at about 100 meters depth, likely
between alluvial deposits and bedrock. Given the measured resonance frequency of
around 1 Hz, buildings located on the alluvium that are between 6-15 stories in height
could experience soil-structure resonance.
5. v
TABLE OF CONTENTS
Signature Page .............................................................................................................. ii
Acknowledgements....................................................................................................... iii
Abstract......................................................................................................................... iv
List of Tables ................................................................................................................ vii
List of Figures............................................................................................................... viii
Chapter One: Introduction ............................................................................................ 1
Chapter Two: Region of Interest................................................................................... 6
Chapter Three: Methodology........................................................................................ 15
Chapter Four: Equipment and Software ....................................................................... 26
Equipment ........................................................................................................ 26
Software ........................................................................................................... 29
Choice of Experiment Parameters .................................................................... 39
Chapter Five: Results and Interpretation ...................................................................... 50
Data Analysis ................................................................................................... 50
Site Characteristics Classification......................................................... 55
Comparison with Surface Geology....................................................... 81
Lateral Variations in Peak Amplitude & Peak Frequency ................... 86
Interpretation of Peak Amplitude ............................................. 89
Interpretation of Peak Frequency ............................................. 90
Estimated Site Response at CLA Building ........................................... 96
Future Work..........................................................................................100
6. vi
Chapter Six: Conclusions..............................................................................................101
References.....................................................................................................................102
Appendix A: Standard Deviation and Number of Windows Selected for Different
Window Lengths ....................................................................................108
Appendix B: Change of Standard Deviation and Number of Windows Selected
Over Time...............................................................................................109
Appendix C: Standard Deviation of Peak Frequency and Peak Amplitude for
Different Parameter Sets Used to Select Windows..................................115
Appendix D: All Reliable H/V Curves .........................................................................119
Appendix E: Results of Evaluation of the SESAME Criteria for a Clear Peak
for All Reliable H/V Curves ...................................................................129
7. vii
LIST OF TABLES
Table 1 Approximate Relationship between Building Height and Natural Period.. 5
Table 2 Different Parameter Sets for Waveform Selection ..................................... 47
Table 3 Reference Shear Wave Velocity around San Gabriel Valley ..................... 94
8. viii
LIST OF FIGURES
Figure 1 Simple illustration of site amplification...................................................... 1
Figure 2 ShakeMap of the Northridge earthquake .................................................... 2
Figure 3 Collapse of Freeway I-10 in Santa Monica................................................. 3
Figure 4 Map of 1985 “Mexico City” Earthquake.................................................... 4
Figure 5 Tectonic setting of the Cal Poly Pomona campus ...................................... 7
Figure 6 Closer look at tectonic setting around the Cal Poly Pomona campus ........ 7
Figure 7 Detailed map of the Cal Poly Pomona campus........................................... 9
Figure 8 Earthquakes from the past 45 years within 15 km of the campus .............. 10
Figure 9 Geologic map of the Cal Poly Pomona campus.......................................... 11
Figure 10 Liquefaction and landslide hazard map of the Cal Poly Pomona campus.. 12
Figure 11 ShakeMap of the Chino Hills earthquake, 2008, colored according to
shaking intensity ......................................................................................... 14
Figure 12 A simple seismogram of noise.................................................................... 16
Figure 13 Comparison of ambient vibration and earthquake based measurements
of fundamental frequency (top) and amplification (bottom) ...................... 18
Figure 14 Simple diagram of ground motions used to illustrate H/V method ............ 20
Figure 15 Simple diagram of HVSR method.............................................................. 22
Figure 16 Simple H/V curve ...................................................................................... 23
Figure 17 K-factor versus distance from coastline...................................................... 25
Figure 18 Picture of seismometer and its connections ............................................... 26
Figure 19 Equipment................................................................................................... 27
Figure 20 H/V Toolbox first tab, General................................................................... 30
9. ix
Figure 21 H/V Toolbox first tab, Raw signal.............................................................. 31
Figure 22 H/V Toolbox second tab, Processing ......................................................... 32
Figure 23 H/V Toolbox third tab, Output.................................................................... 33
Figure 24 H/V Toolbox, dropdown menu................................................................... 34
Figure 25 An example of auto selected windows........................................................ 35
Figure 26 An example of selected data windows after calculation of the spectral
ratio curve .................................................................................................. 37
Figure 27 An ideal example of an H/V curve ............................................................. 38
Figure 28 Criteria for a reliable H/V curve and criteria for a clear H/V peak ............ 39
Figure 29 Three selected locations for pre-experiment............................................... 41
Figure 30 H/V curve for station KGH......................................................................... 42
Figure 31 H/V curve for station FMG ........................................................................ 43
Figure 32 H/V curve for station MBX ........................................................................ 44
Figure 33 Comparison of window selection between day- and night-time at station
FMG. ......................................................................................................... 45
Figure 34 Comparison of window selection between day- and night-time at station
KGH ........................................................................................................... 46
Figure 35 Comparison of window selection between day- and night-time at station
MBX. .......................................................................................................... 46
Figure 36 Comparison of windows selected for different parameter sets for
station FMG. ............................................................................................... 48
Figure 37 Comparison of windows selected for different parameter sets for
station KGH. ............................................................................................... 48
Figure 38 Comparison of windows selected for different parameter sets for
station MBX................................................................................................ 49
Figure 39 Google Earth map of Cal Poly Pomona campus with all reliable
H/V curves .................................................................................................. 52
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Figure 40 Peak amplitude for the Cal Poly Pomona campus overlaid on
geological map............................................................................................ 53
Figure 41 Peak frequency for the Cal Poly Pomona campus overlaid on
geological map............................................................................................ 53
Figure 42 K-factors for the Cal Poly Pomona campus overlaid on seismic
hazard map.................................................................................................. 54
Figure 43 Selected windows for Site-44’s seismogram .............................................. 55
Figure 44 H/V graph for Site-44 ................................................................................. 55
Figure 45 Settings for H/V calculation for Site-44 ..................................................... 56
Figure 46 Selected windows for Site-30’s seismogram .............................................. 59
Figure 47 H/V graph for Site-30 ................................................................................. 59
Figure 48 Settings for H/V calculation for Site-30 ..................................................... 60
Figure 49 Selected windows for Site-33’s seismogram .............................................. 63
Figure 50 H/V graph for Site-33 ................................................................................. 63
Figure 51 Settings for H/V calculation for Site-33 ..................................................... 64
Figure 52 Selected windows for Site-34’s seismogram .............................................. 69
Figure 53 H/V graph for Site-34 ................................................................................. 69
Figure 54 Settings for H/V calculation for Site-34 ..................................................... 70
Figure 55 Selected windows for Site-13’s seismogram .............................................. 73
Figure 56 H/V graph for Site-13 ................................................................................. 73
Figure 57 Settings for H/V calculation for Site-13 ..................................................... 74
Figure 58 H/V Rotate results for Site-13..................................................................... 77
Figure 59 Selected windows for Site-2’s seismogram ................................................ 78
Figure 60 H/V graph for Site-2 ................................................................................... 78
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Figure 61 Settings for H/V calculation for Site-2 ....................................................... 79
Figure 62 Total site distribution of groups for the entire campus............................... 82
Figure 63 Site classification for geologic unit sand alluvial deposits (Qyfa) ............. 83
Figure 64 Site classification for geologic unit silt alluvial deposits (Qyfs) ................ 83
Figure 65 Site classification for geologic unit clay alluvial deposits (Qyfc). ............. 84
Figure 66 Site classification for geologic unit La Vida Member (Tpl)....................... 85
Figure 67 Site classification for geologic unit Topanga Formation (Ttc)................... 85
Figure 68 Peak amplitude versus peak frequency graph for measurements from all
reliable curves ............................................................................................. 87
Figure 69 Peak amplitude versus peak frequency for different colored groups.......... 88
Figure 70 Peak amplitude versus peak frequency graph with only Group Yellow
and Green.................................................................................................... 88
Figure 71 Peak amplitude of the spectra ratio curves for all campus sites, overlain
on a topographic map.................................................................................. 90
Figure 72 Topographic profile across the Cal Poly Pomona campus on Google
Earth............................................................................................................ 91
Figure 73 Geological map overlaid on Google Earth map showing topographic
profile along the red line............................................................................. 91
Figure 74 Estimated depth to interface on topographic map....................................... 92
Figure 75 Estimated dip of interface using 314 m/s as shear wave velocity............... 94
Figure 76 Estimated dip of interface using 502 m/s as shear wave velocity............... 95
Figure 77 A dipping structure could be caused by deformation due to the San Jose
thrust fault on campus................................................................................. 96
Figure 78 Picture of the CLA building........................................................................ 97
Figure 79 Location of the CLA building (dark outline) on fault map from
Geocon ........................................................................................................ 97
Figure 80 Stations around the CLA building .............................................................. 98
12. xii
Figure 81 H/V curve of Site-50................................................................................... 98
Figure 82 Location of the replacement building with yellow dot indicating
the closest seismometer site........................................................................ 99
Figure 83 H/V curve for Site-33..................................................................................100
13. 1
CHAPTER ONE
INTRODUCTION
Throughout history, earthquakes have caused much destruction to urbanized
areas, and have been responsible for the loss of many lives and major economic damages.
Surface ground motion is one of the contributing factors that can affect the level of
damage experienced during an earthquake. Various types of surface layers can influence
ground motion due to differences in soil hardness and thickness. In general, soft soil sites
tend to have lower shear wave velocities and to amplify ground motions relative to hard
rock sites (Figure 1).
Figure 1. Simple illustration of site amplification. Earthquake
waves propagate from lower left corner to ground surface
with one seismometer on a hard rock site and one seismometer
on a soft soil site showing ground motion (Ammon, 2001).
The 1994 Northridge earthquake is a key example of the effects of site
amplification in Southern California. Figure 2 shows the ShakeMap for the Northridge
earthquake, where the shaking intensity level is indicated in different colors, with warmer
colors representing higher levels of shaking. The city of Santa Monica, as shown by the
white dot in Figure 2, especially suffered heavy shaking, while other areas at similar
14. 2
distances from the epicenter experienced much smaller ground motions. Moreover, the
collapsed Interstate 10 highway (Figure 3) was built on top of a drained wetland, which
experienced amplified ground shaking. Results from Boore et al. (2003) show that the
ground motions in the collapsed Interstate-10 highway area, which was 2.3 kilometers
away from the epicenter, were a factor of 1.2 to 1.6 higher than in the surrounding area.
Figure 2. ShakeMap of the Northridge earthquake, (USGS, 1997). Red
lines outline faults in the region. The black star shows the epicenter.
Colors represent the intensity, with red the highest intensity, and white the
lowest. Black dots show the main cities. White dot indicates Santa Monica.
15. 3
Figure 3. Collapse of Freeway I-10 in Santa Monica, (U.S.
Department of Transportation, 2002).
The damage level may also be associated with a combination of building height
and shallow subsurface velocity structure. When earthquakes occur, columns of ground
materials may vibrate stronger in a certain frequency range. Buildings may also vibrate at
a higher amplitude in a certain frequency range. When both frequencies are similar, soil
structure resonance will occur and the potential damage to the building will be increased.
The magnitude 8.0 “Mexico City” earthquake on September 19, 1985 is another
example of increased earthquake damage due to site response. The epicenter of this
earthquake was located 300 kilometers Southeast of Mexico City (Figure 4), but
considerable damage was still sustained in the capital of Mexico. Normally, ground
motions due to seismic waves are significantly attenuated at large distances and are of
relatively small amplitude. However, the center of Mexico City is located on a dry
lakebed, Lake Texcoco, where the soil resonance has similar frequency as the surface
waves from the offshore earthquake at this location, namely 0.5 Hz (2 seconds period).
Many buildings that were between eight stories and eighteen stories in height collapsed
during this earthquake. These buildings also had a 0.5 to 1 Hz natural frequency (see
16. 4
Table 1). Both the soil and some of the buildings therefore experienced resonance, which
led to major damage in Mexico City (Flores, 1987). As this example shows, determining
site amplification and fundamental frequencies can help mitigate seismic hazard.
Figure 4. Map of 1985 “Mexico City” Earthquake. Cities that experienced violent
shaking are denoted with red dots. Note that the earthquake occurred along the coast,
with Mexico City located 300 kilometers inland (“Mexico City earthquake of 1985”,
n.d.).
17. 5
Table 1
Approximate Relationship between Building Height And Natural Period
(MCEER, 2010).
To completely understand the soil structure of a site, it is necessary to drill and
retrieve soil core samples. Depending on the depth and drilling area, this can be
expensive and can cause permanent damage to the environment. Another option to
consider is the use of geophysical methods, which are a cost-effective and non-
intrusive approach for site investigations. Traditional geophysics commonly employs the
refraction or the reflection method to determine the seismic velocity structure of a site.
These methods require a significant amount of equipment and personnel. For a more
efficient approach, we use records of ground motion of noise to measure site response
parameters.
The main goal of this thesis is to enhance our understanding of the seismic
response of the area of the campus of California State Polytechnic University, Pomona.
We therefore carried out numerous experiments to determine site response parameters at
many locations across campus and created maps to show the lateral variation of these
parameters.
18. 6
CHAPTER TWO
REGION OF INTEREST
Our region of interest for this research is a university campus in Southern
California. The campus, known as California State Polytechnic University, Pomona, will
henceforth be referred to as Cal Poly Pomona. Several previous studies have been carried
out within this area. GeoCon (2001) performed borehole borings, trenching, and gamma-
ray spectrometer surveys on the campus. Oliver (2010) applied the refraction
microtremor technique to estimate shallow S-wave velocity profiles at several sites on
campus. Pazos (2011) and Potter (2011) generated gravity profiles across traces of the
San Jose Fault through the campus. Figure 5 shows the tectonic setting of the campus.
Cal Poly Pomona is located on the West side of the freeway intersection of the I-10 and
57 (as shown by the blue dot on Figure 5). To the North are the Indian Hill Fault and San
Gabriel Mountains. Located to the South are the Puente Hills, with the Whittier Fault to
the Southwest. To the West is the San Gabriel Basin. The campus is located on top of the
San Jose Fault (Figure 6), which has a shallow to moderate dip to the North and the
campus is within 40 kilometers South of the San Andreas Fault Zone.
19. 7
Figure 5. Tectonic setting of the Cal Poly Pomona campus. Grey color indicates
area of higher elevation, such as hills and mountains. Exposed faults are shown
in dark black lines, covered faults with dotted lines. Dashed lines with numbers
show the location of freeways. The extent of drainages is indicated with dash-dot
lines. The campus is shown as a blue dot (adapted from Yeats, 2004).
Figure 6. Closer look at tectonic setting around the Cal Poly Pomona campus. White
lines indicate folding. Dotted lines show the location of buried faults. Black-white line
indicates the suggested San Jose Fault trace line (Yeats, 2004).
20. 8
To develop a better understanding of the local area, Figure 6 provides a closer
look at the tectonic setting. On the Southeast side of campus is Elephant Hill and the
Chino Basin (located in the Southeast corner of Figure 6), while the rest of the area is
more hilly. To the West, there are a few synclines and anticlines. The figure also shows a
simplified fault trace of the San Jose Fault.
Figure 7 is a detailed campus map, also showing the 10 Freeway to the North.
This figure shows the San Jose Fault trace as determined by GeoCon (2001) and color
coded by Pazos (2011). The red line is the trace of the San Jose Fault, which intersects
the complete campus. According to GeoCon (2001), the San Jose Fault is a regional
listric thrust fault with two shallowly to moderately North-dipping thrust faults in the
central campus and it merges to the Southwest with a secondary fault steeply dipping to
the South. Based on findings from the Southern California Earthquake Center (SCEC),
the San Jose Fault was involved in two recent earthquakes: the 1988 and the 1990 Upland
earthquakes. Hauksson (1991) determined that the 1988 earthquake had a magnitude of
4.7, with minor damage in the area closest to the epicenter. The 1990 earthquake had a
magnitude of 5.4 and caused minor injuries to thirty-eight people and considerable
damage near the epicenter (Person, 1990). These two earthquakes have shown that the
San Jose Fault should be considered an active fault. In addition to the San Jose Fault,
there exist numerous other faults that are capable of producing strong ground motions on
the Cal Poly Pomona campus.
22. 10
Figure 8. Earthquakes from the past 45 years within 15 km of the campus. White circles
represent earthquakes located by USGS. Black circle indicates the location of the Cal
Poly Pomona campus. The size of circles represents the earthquake magnitudes. Red lines
represent faults and their names (explained in main text) in black. White lines indicate
roads (USGS, 2015).
Figure 8 shows a map of the local seismicity generated by the USGS tool located
at Search Earthquake Archives (USGS, 2015), for the past 45 years within 15 kilometers
of Cal Poly Pomona. Within this timeframe, this area has had a total of 218 earthquakes
with 37 earthquakes having a magnitude higher than 3, with 7 magnitude 4+ earthquakes
and 1 magnitude 5+ earthquake. Most of the earthquakes are less than 15 kilometers
deep, which is considered shallow. In addition to the San Jose Fault (SJF) that across the
23. 11
campus, this figure also shows several regional active faults surrounding the Cal Poly
Pomona campus. To the North of the campus, there are the Sierra Madre Fault (SMF)
zone and the Indian Hill Fault (IHF). To the Southwest is the Elsinore Fault zone
(Whittier section, WF) and to the Southeast are the Central Avenue Fault (CAF) and
Elsinore Fault zone (Chino section, CF). All these faults can cause significant ground
motions on the Cal Poly Pomona campus. Therefore, it is important to understand the
local site characteristics of the campus.
Figure 9. Geologic map of the Cal Poly Pomona campus. Qyf-alluvial fan and valley
deposits; a=sand, s=silt, c=clay. Tpl-platy siltstone interbedded with sandstone,
conglomerate, limestone and tuff. Tpy-platy siltstone with interbeds of sandstone,
limestone and marl. Ttc-pebbly sandstone and conglomerate. Black lines indicate the
location of contacts between units; a solid black line shows an accurately located contact
and a dashed line shows an approximately located or inferred contact. Grey color
indicates buildings and freeways. Thick black and white line indicates roads (adapted
from Tan, 1997).
To have a better understanding of the site characteristics, we need to gather more
background information on the study area. Figure 9 shows the surface geology map of
24. 12
the greater campus area. The San Jose Creek runs along the right side of South Campus
Drive in an area of alluvial sand deposits (Qyfa). Most campus buildings are built on top
of silt and clay alluvial deposits (Qyfs and Qyfc) at the center of the figure. The
Southwest side of campus is built on siltstone, whereas the Northwest side of campus is
mainly built on top of sandstone and conglomerate. The underlying topography map in
Figure 9 indicates flatter land on the East side of the campus, and hills to the North and
West.
Figure 10. Liquefaction and landslide hazard map of the Cal Poly Pomona campus.
Green – Liquefaction hazard areas. Blue – Landslide hazard areas. Black - Surface
buildings and roads (adapted from Davis, 1999).
Earthquakes can also cause liquefaction and landslides. Figure 10 shows a hazard
map of campus with the shaking inputs based on a 10% probability of exceedance in 50
years. A green color is used for potential liquefaction hazard areas, which underlie most
25. 13
of the campus. The hills to the Northwest show potential hazard for earthquake induced
landslides.
We use the ShakeMap of the Chino Hills earthquake in 2008 as a reference for the
level of shaking produced by a magnitude 5.5 earthquake in the local area (Figure 11).
The measured intensity for the closest station to the epicenter is about intensity VI, which
is approximately the same intensity measured by the station (21 kilometers away from
epicenter) that is closest to Cal Poly Pomona. Although there are numerous earthquakes
in the local area, most of them are aftershocks with low magnitude. Only one Southern
California Seismic Network station was located on campus and this instrument was only
active for a few years. Therefore, earthquake based data is not sufficient for studies of
site characteristics at Cal Poly Pomona campus, since the few available waveforms are
not adequate for such a study. Cal Poly Pomona will experience high frequency, short
wave length, ground motion if a local earthquake occurs, such as on the San Jose Fault.
On the other hand, the campus will experience low frequency, long wave length, ground
motion from any earthquake that occurs at regional distances on faults such as the San
Andreas. Numerous faults surround the Cal Poly Pomona campus at both local and
regional distances. Therefore, we will focus on a broadband frequency range for this
research, covering a large spectrum of possible ground motion frequencies.
26. 14
Figure 11. ShakeMap of the Chino Hills earthquake, 2008, colored according to shaking
intensity. Red star indicates epicenter. Black dots show the location of major cities.
Purple dot shows the Cal Poly Pomona campus (adapted from USGS, 2008).
27. 15
CHAPTER THREE
METHODOLOGY
The best way to understand subsurface geology is through applying invasive site
assessment techniques such as drilling and trenching. Although Geocon (2001) obtained
geologic borehole data from the Cal Poly Pomona campus, these boreholes only reached
about 80 feet (25 meters) in maximum depth below the surface. Borehole geology has a
great impact on the environment and involves other logistical issues that are associated
with drilling in developed urban areas. An alternative to this approach is to use a passive
geophysical method such as the Standard Spectral Ratio (SSR) approach (Abbott, 2006).
This method uses data recorded by seismometers and determines the site response
differences between a soft soil site and a reference site, usually located on hard rock
material. This method requires active seismicity with large earthquakes to be able to carry
out its data analysis. For a site like the Cal Poly Pomona campus that has experienced
little to no strong ground motion and has not been well instrumented, this method is not
appropriate. Instead we chose to apply another passive method that is based on the use of
background noise, called the Horizontal-to-Vertical Spectral Ratio (HVSR) approach.
This is an empirical method that was first applied by Nogoshi and Igarashi (1970, 1971)
to determine site response parameters such as fundamental frequency and site
amplification. This well-established method is based on a computation of the ratio of
horizontal ground motion over vertical motion. Numerous studies have been conducted
successfully (Lacave, 1999 and references therein) using HVSR and have compared its
results to those obtained by other geophysical methods. In general, this method is capable
of determining accurate estimates of resonant fundamental frequency and may provide a
28. 16
lower bound of the amplification factor of a site. Based on these two parameters, we can
also estimate the minimum depth to the first significant subsurface impedance contrast.
An additional parameter, the k-factor, may also be derived and used as an estimate for the
susceptibility to damage from liquefaction. To help mitigate earthquake effects, we can
determine these site response parameters, so that they can be taken into account when
designing and constructing buildings.
The HVSR method analyzes ambient noise from vertical and horizontal ground
motion to determine site characteristics. Ambient noise is also referred to as microtremor.
It is a low amplitude background vibration that is caused by local movement such as
people walking, wind blowing, and car movement. Figure 12 shows an example of
microtremor recorded on a seismogram. The figure shows random background noise in 3
different orthogonal directions, vertical (Z), north-south (N), and east-west (E).
Figure 12. A simple seismogram of noise. Recorded with ground motion
amplitude in three directions on the y-axis and time on the x-axis.
29. 17
A European project called Site EffectS assessment using AMbient Excitations
(SESAME) conducted extensive research on the application of the HVSR method
(SESAME, 2004). One of their projects compared the results of microtremor based
HVSR versus earthquake based SSR at different sites. Figure 13 (top) shows a
comparison of the fundamental frequencies and Figure 13 (bottom) shows that of the
amplification. In Figure 13 (top), most of the data plots on a straight line, showing a
linear relationship between the fundamental frequency determined using the two
methods. Thus, this result of the SESAME project suggests that the fundamental
frequency measured from ambient noise corresponds well with the actual site response.
The bottom diagram shows ground motion amplification measured by ambient vibrations
plotted against ground motion amplification determined from earthquake data. Most of
the data plot below the 1-to-1 ratio line, suggesting that amplification measured from
ambient vibrations can be considered to be a lower bound on the site response
amplification due to earthquakes. However, most of the data points do plot close to the 1-
to-1 ratio line. In this thesis project, we will therefore assume that the HVSR peak
frequency provides an estimate of the site’s fundamental frequency and the HVSR peak
amplitude may be considered to represent a lower bound of the true site amplification
factor.
30. 18
Figure 13. Comparison of ambient vibration and earthquake based
measurements of fundamental frequency (top) and amplification (bottom).
Y-axis shows results obtained by the HVSR method; x-axis show those
of the SSR method (SESAME, 2004).
31. 19
The HVSR method is empirical and was originally developed using observations
from earthquakes in Japan. Several theoretical explanations have been developed to try to
address why the HVSR method works (e.g. Jerez et al., 2004 and Fäh, 2001). In general,
most researchers (e.g. Lane Jr, 2008) use ground motion predictions based on a 1-D
model with a homogeneous soft soil layer overlying hard rock, as shown in Figure 14.
We here describe the explanation from Lermo and Chavez-Garcia (1994) and originally
from Nakamura (1989). They assume the microtremor originates from a local source and
that the microtremor mainly consists of Rayleigh waves. As we are interested in how
much a surficial soft layer can amplify ground motion compared to bedrock, Equation 1
shows our desired result: the ratio of the surface horizontal movement to the bedrock
horizontal movement. But, the horizontal movement of bedrock is difficult to determine
and SE includes a source effect. To compensate SE for the source spectrum, a modified
site effect spectral ratio SM with the relative vertical motion (Equation 2) is computed as
shown in Equation-3. Then, we assume that bedrock doesn’t amplify the horizontal
movement as shown in Equation 4. Substituting Equation 4 into Equation 3, we obtain
Equation 5, the horizontal movement divided by the vertical movement, which is the
basic for the HVSR method.
32. 20
Figure 14. Simple diagram of ground motions used to illustrate H/V method.
Z-Thickness of first layer. VS-Vertical movement of surface. HS-Horizontal movement of
surface. VB-Vertical movement of base rock. HB-Horizontal movement of base rock
(from Nakamura, 1989).
Equation 1. Ideal equation to calculate site effect.
Equation 2. Site vertical motion relative to bedrock.
Equation 3. Modified site effect equation to compensate for any source effect.
33. 21
Equation 4. Assumption that there is no horizontal amplification on bedrock.
Equation 5. Equation for HVSR.
Figure 15 illustrates that the horizontal ground motion is generally larger than the
vertical ground motion in soft soil, while both motions are similar at a hard rock site. The
right side of the figure shows that by dividing the horizontal movement by the vertical
movement, a standout peak is generated.
34. 22
Figure 15. Simple diagram of HVSR method. H is horizontal motion. V is vertical
motion. Blue arrows indicate motion on hard rock site. Red arrows indicate motion on
soft soil layer. Fo is the fundamental frequency (Nakamura, 2008).
This method produces an H/V curve as shown in Figure 16. We mainly focus on
two parameters that may be measured from this curve: the peak spectral ratio frequency
(f0) and the peak amplitude (A0). These two values can be interpreted in the context of the
fundamental frequency and amplification factor. We will explain these two values in
more depth in a later section.
35. 23
Figure 16. Simple H/V curve. f0 denotes the frequency of the highest peak. A0 is the
amplitude of the highest peak.
Once we have a peak frequency and an estimate of the local shallow shear wave
velocity, we can calculate the minimum depth to the impedance contrast using
Equation 6.
hmin ≈
𝑉𝑠 𝑠𝑢𝑟𝑓
4𝑓0
Equation 6. hmin is the minimum depth to the impedance contrast. Vssurf is the top soft soil
layer shear wave velocity. f0 is the fundamental frequency (SESAME, 2004).
In addition to the two most frequently used HVSR parameters, a derived
liquefaction parameter, the k-factor (Equation 7), involves the fundamental frequency and
site amplification factor and was used by Nakamura (1996) to estimate the potential for
damage by earthquake liquefaction. This parameter was developed using an empirical
approach that is based on observations from the 1989 Loma Prieta Earthquake in the San
36. 24
Francisco Bay area. Liquefaction is failure of soil strength. When an earthquake happens,
shaking causes the water pressure inside saturated soil to increase, which decreases the
strength of the soil, causing buildings on the surface to sink.
k=
𝐴0
2
𝑓0
Equation 7. Equation for the k-factor, k. A0 is the site amplification. f0 is fundamental
frequency (Nakamura, 1996).
As shown in Figure 17, in the Loma Prieta earthquake the reclaimed land area
suffered severe damage and the k-value calculated for sites in this region had the highest
value. The seaside area was also damaged by liquefaction and sites there had a k-factor
higher than 20. As the distance from the coastline increases, both the amount of damage
and the k-factor decrease. In the hillside area, where there was no damage, the k-value
had decreased to 5 and below. The author concluded that when k is greater than 20,
liquefaction is likely to occur when strong ground motions are experienced. Therefore,
we also calculated the k-factor (Equation 7) and compared our results to existing
liquefaction maps.
37. 25
Figure 17. K-factor versus distance from the coastline. Y-axis shows the value of
the k-factor and the x-axis shows the distance from the coastline. Each dot is a
measured value from a site (Nakamura, 1996).
Numerous experiments have used the HVSR method. Panou et al. (2005) and
Konno and Ohmachi (1998) both show good correlation of both fundamental frequency
and amplification with the thickness of the top soil layer. Konno and Ohmachi (1998) and
Huang and Teng (1999) also show that H/V ratio data agrees with measurements based
on earthquake data. Parolai et al. (2002), Fairchild (2013) and Lane (2008) confirm that
the HVSR approach works well in areas that have a significant impedance contrast
between the sediment layers and underlying bedrock. However, Castellaro and Mulargia
(2009) concluded that the low frequency results are weather dependent and not accurate.
Delgado et al. (2000) argue that HVSR is not an appropriate method to use in areas where
there is no strong impedance contrast at depth or where the shear wave velocity changes
irregularly with depth.
38. 26
CHAPTER FOUR
EQUIPMENT AND SOFTWARE
Equipment
We used seismometers manufactured by Guralp, model CMG-6TD as shown in
Figure 18. It is a broadband, force-feedback instrument measuring ground motions in
three directions: vertical (Z), north-south (N), and east-west (E). The sampling rate is
0.01 second (100 Hz). It includes a Global Positioning System (GPS) unit that can
synchronize its time and location using satellites.
Figure 18. Picture of seismometer and its connections. Left figure shows a simple
diagram of equipment set up. Right photo shows the actual size of the seismometer.
39. 27
Figure 19. Equipment: top row, from left to right: hard drive, laptop computer, GPS unit,
seismometer, marine battery, data extraction cable, computer data cable, GPS cable,
battery cable, and breakout box cable.
Setting up the experiment is straightforward and can easily be done by one
person. We installed seismometers throughout the Cal Poly Pomona campus following
the guidelines suggested by SESAME:
In Situ Soil-sensor Coupling
A thin cover of asphalt or concrete does not affect H/V results in the main
frequency band of interest
It is not recommended to put the seismometer on grass since the blowing wind
can lead to perturbed results below 1 Hz
Avoid setting the sensor on superficial layers of "soft" soils, such as mud,
plowed soil, or artificial covers like synthetic sport covering
40. 28
Avoid recording on water saturated soils, for example after heavy rain
Avoid recording on superficial cohesionless gravel, as the sensor will not be
correctly coupled to the ground resulting in strongly perturbed curves
Sensor Setting
The sensor should be set up on the ground horizontally as recommended by
the manufacturer
Do not put any load on the sensor
Recording near structures may influence the results: movements of the
structures due to the wind may introduce strong low frequency perturbations
in the ground
Avoid measuring above underground structures such as car parks, pipes,
sewer lids, etc., these structures may significantly alter the amplitude of the
vertical motion
Weather Conditions
Avoid measurements during windy days
Measurements during heavy rain should be avoided, while slight rain has no
noticeable influence on H/V results
Extreme temperatures should be treated with care
Disturbances
All kinds of short-duration local sources (footsteps, car, train, etc) can disturb the
results
o Fast highway traffic influences H/V ratios if they are within 15-20 meters
o Slow inner city traffic influences H/V ratios when they are much closer
41. 29
Avoid measurements near monochromatic sources like: construction machines,
industrial machines, pumps, etc.
The recording team should not keep its car engine running during recording
Software
We use the Geopsy software program (http://www.geopsy.org/) to generate the
spectral ratio curves. We will illustrate our workflow and choice of input parameters by
describing the use of this software on the waveform data from one of our sites. First, we
input the 3 component seismograms. Then, we set our parameters in the H/V Toolbox.
When we open the H/V toolbox, as shown in Figure 20, the first tab will show, Time.
Within this tab, we can narrow the data to a certain time period for analysis in Global
Time Range. We can also set the length of each window for H/V analysis in Time
Windows.
42. 30
Figure 20. H/V Toolbox first tab, General.
Within a sub-tab of Time, Raw Signal (Figure 21), we can control what kind of
waveform we want to use for analysis. As we mentioned in a previous section, HVSR
uses ambient noise. Therefore, we set the parameters to help us select waveforms that are
low amplitude background noise and also eliminate large sudden peaks. Detailed
explanations on what parameters we use for Raw Signal will be discussed in the Pre-
experiment section.
43. 31
Figure 21. H/V Toolbox first tab, Raw signal.
The second tab is Processing (Figure 22), which controls how Geopsy processes
data and combines the horizontal components, N-S and E-W, into one component. For
this section, we use the default setting.
44. 32
Figure 22. H/V Toolbox second tab, Processing.
The third tab is the Output (Figure 23), which controls the frequency range,
appearance, and the output folder. We chose a broadband frequency sampling range
between 0.1 Hz and 20 Hz.
45. 33
Figure 23. H/V Toolbox third tab, Output.
Once we set the parameters, we return to the Time tab and then click on the
dropdown menu marked with Select in the lower right corner and choose Auto as shown
in Figure 24.
46. 34
Figure 24. H/V Toolbox, dropdown menu.
This will generate a set of pre-selected windows on the seismograms in a green
color as shown in Figure 25. From this step, we can add or remove any of these windows
manually to prepare for H/V data processing. After that, we click Start and the software
runs the H/V calculation.
48. 36
The program then colors the selected windows as in Figure 26. Each colored
window undergoes the H/V calculation and is used to generate an H/V curve. Then, all
the H/V curves are plotted together as shown in Figure 27. The black colored H/V curve
is the average of all colored H/V curves, while the dashed black lines indicate the
standard deviation. The vertical grey bar shows the auto-selected peak, which is the
highest amplitude peak. Figure 27 shows an ideal situation where there is a single clear
peak. Based on this figure, the frequency of the peak (f0) is about 1.074 Hz with standard
deviation of 0.137 and the peak amplitude (A0) is about 4.515 with a standard deviation
of about 1.209. We can then use the criteria list shown in Figure 28 to determine whether
this H/V curve is reliable and its H/V peak is clear. The criteria for the reliability of the
H/V curve verify that there are enough windows selected for the targeted frequency with
low standard deviation. The criteria for a clear H/V peak check that the peak stands out
from the background H/V curve with small standard deviation and fulfills thresholds of
peak frequency and peak amplitude. If both sets of criteria are met, we consider, based on
the empirical results shown in Figure 13, the peak frequency as an estimate of the
fundamental frequency of the site and the peak amplitude as the lower bound on the site
amplification.
50. 38
Figure 27. An ideal example of an H/V curve. X-axis indicates frequency (in Hz). Y-
axis indicates spectral ratio amplitude. Each colored line is an H/V curve in each
selected window of the same color. Solid black line indicates the average H/V curve.
Dotted lines represent the standard deviation of the H/V curve. Grey bars indicate the
selected peak frequency and its standard deviation.
51. 39
Figure 28. Criteria for a reliable H/V curve and criteria for a clear H/V peak
(SESAME, 2004).
Choice of Experiment Parameters
As we mentioned in the REGION OF INTEREST chapter, Cal Poly Pomona
will experience different frequencies of ground motion depending on the distance to the
earthquake rupture and the magnitude of the event. Therefore, we are interested in the site
response over a broadband frequency range from 0.1 Hz to 20 Hz. For an H/V curve to be
considered reliable, we need at least ten full cycles of the targeted frequency as shown by
Equation 8 (SESAME, 2003).
Window Length = 1 / frequency *10
Equation 8. Appropriate minimum window length.
52. 40
If the targeted frequency is 20 Hz, one cycle is 0.05 seconds. Ten cycles will give
us 0.5 seconds as window length. If the targeted frequency is 0.1 Hz, one cycle is 10
seconds and ten cycles will give us 100 seconds. Therefore, we use a 100 second window
length to cover our frequency range of interest. An additional benefit of a longer window
length is that it generates measurements with a lower standard deviation as shown in
Appendix A.
The standard deviation of our measurements can also be affected by the number
of windows. Geopsy uses Equation 9 to calculate the standard deviation of the H/V curve.
Equation 9. Equation used in the Geopsy software to compute σH/V, the standard deviation
of the H/V curve. nwindows is number of windows selected (SESAME, 2003).
We initially collected waveform data at a few sites to empirically estimate the
time when the standard deviation would stabilize and no longer decrease significantly
with time. In general, about one hour of seismometer data was needed for a stable
standard deviation of peak frequency, while there was no clear correlation between the
standard deviation of the peak amplitude and the duration of the available data. The
results of these tests are shown in Appendix B. To guarantee that we had sufficient data
for our analysis, we decided to have at least 2 to 3 hours recording time at each site.
We installed three seismometers in three different locations for three months as a
preliminary experiment. These sites were used as our references for this thesis project.
The locations are shown in Figure 29.
53. 41
Figure 29. Three selected locations for pre-experiment. Map generated with Google
Earth.
The results of our preliminary experiment are shown in Figure 30 to Figure 32
and each figure was based on the analysis of a one full day (24 hours) of waveform data.
The H/V curve for station KGH (Figure 30) shows a peak frequency at 0.387 Hz with
standard deviation of 0.073 and a peak amplitude at 2.375 +- 1.289. The H/V curve for
station FMG (Figure 31) shows a peak frequency at 1.075 Hz with standard deviation of
0.132 and a peak amplitude at 4.637 with a standard deviation of 1.205. The H/V curve
for station MBX (Figure 32) shows two small peaks. The first peak frequency is at 0.165
Hz with standard deviation of 0.035, with a peak amplitude of 2.019 and a standard
deviation of 1.419. The second peak frequency is at 0.494 Hz with a standard deviation of
0.075. Its associated amplitude is 2.121 with a standard deviation of 1.238. This initial
analysis gave us a general idea of the site characteristics on the Cal Poly Pomona campus.
54. 42
Figure 30. H/V curve for station KGH. Peak frequency at 0.387 Hz with standard
deviation of 0.0731. A peak amplitude at 2.375 with standard deviation of 1.289.
Graph lines, colors and axes as described for Figure 27.
55. 43
Figure 31. H/V curve for station FMG. Peak frequency at 1.075 Hz with standard
deviation of 0.132. A peak amplitude at 4.637 with standard deviation of 1.205.
Graph lines, colors and axes as described for Figure 27.
56. 44
Figure 32. H/V curve for station MBX. First peak frequency at 0.165 Hz with
standard frequency of 0.035. An amplitude of 2.019 with standard deviation of
1.419. Second peak frequency at 0.494 Hz with standard deviation of 0.075. Its
associated amplitude is 2.121 with standard deviation of 1.238. Graph lines, colors
and axes as described for Figure 27.
To determine the best time of day for the experiments, we compared the
difference between results obtained for waveforms recorded during the daytime and
nighttime. Figure 33 to Figure 35 show the number of windows selected for analysis at
each site. We chose one week of data and compared the three stations. Daytime is
considered to be 07:00 to 19:00 and nighttime is considered from 19:00 to 07:00 the next
57. 45
day. We used the parameter set suggested by SESAME: STA: 1, LTA: 30, Min
STA/LTA: 0.3, and Max STA/LTA: 2.0 with 100 seconds window length. These three
data sets do not shown a correlation between number of windows selected in weekday
and number of windows selected in weekend. For the data for station FMG (Figure 33), a
similar number of windows was selected between daytime and nighttime. For the data for
station KGH (Figure 34), a very small number of windows was selected during the
nighttime. For the data for station MBX (Figure 35), generally a higher number of
windows was selected during the daytime and a very small number of windows was
selected in the night. We could explain these numbers by an increase in the ambient noise
needed for this analysis during the daytime. Since it is important to have sufficient
windows selected for analysis for the relatively short deployment time, we decided to
install the seismometers during the daytime.
Figure 33. Comparison of window selection between day- and night-time at station
FMG. Red squares indicate weekend.
0
50
100
150
200
250
300
350
400
Numbersofwindowsselected
Date
FMG_Day
FMG_Night
58. 46
Figure 34. Comparison of window selection between day- and night-time at station
KGH. Red squares indicate weekend.
Figure 35. Comparison of window selection between day- and night-time at
station MBX. Red squares indicate weekend.
0
50
100
150
200
250
3-Oct 4-Oct 5-Oct 6-Oct 7-Oct 8-Oct 9-Oct 10-Oct
Numberofwindowsselected
Date
KGH_Day
KGH_Night
0
50
100
150
200
250
300
350
400
3-Oct 4-Oct 5-Oct 6-Oct 7-Oct 8-Oct 9-Oct 10-Oct
Numberofwindowsselected
Date
MBX_Day
MBX_Night
59. 47
We also tested different parameter sets for window selection: the default
parameters from Geopsy, Project SESAME, Set-C, Set-B, and Set-A are shown in Table
2, all with a 100 seconds window length. We picked two days of waveform data for our
three initial test stations and applied each parameter set to the same dates for comparison.
As shown in Figure 36 to Figure 38, Set-C and the Geopsy parameter set led to a higher
number of windows selected and Set-C has the highest number. Therefore, we choose
parameter Set-C: STA: 1, LTA: 15, Min STA/LAT: 0.2, Max STA/LTA: 2.5, as the main
parameter set for our noise window selection. On a side note, Appendix C shows that the
different sets of parameters did not have a significant influence on the standard deviation
of either peak frequency or peak amplitude.
Table 2
Different Parameter Sets Tested for Waveform Selection.
STA LTA MinSTA/LTA MaxSTA/LTA
Geopsy 1 30 0.2 2.5
Sesame 1 25 0.5 2
C 1 15 0.2 2.5
B 1 30 0.3 2
A 1 20 0.5 2.2
60. 48
Figure 36. Comparison of windows selected for different parameter sets for
station FMG.
Figure 37. Comparison of windows selected for different parameter sets for
station KGH.
0
50
100
150
200
250
300
350
400
Geopsy Sesame C B A
NumberofWindows
Parameter Sets
10-Nov
22-Nov
0
50
100
150
200
250
300
350
400
Geopsy Sesame C B A
NumberofWindows
Parameter Sets
19-Oct
18-Oct
61. 49
Figure 38. Comparison of windows selected for different parameter sets for
station MBX.
0
50
100
150
200
250
300
350
400
Geopsy Sesame C B A
NumberofWindows
Parameter Sets
10-Nov
28-Oct
62. 50
CHAPTER FIVE
RESULTS AND INTERPRETATION
Data Analysis
We collected broadband waveform data from 46 sites located across the Cal Poly
Pomona campus with the sites spaced about 50 to 150 meters apart as shown in Figure
39. This figure also shows the 34 graphs that were determined to be reliable H/V curves
using the SESAME guidelines. Larger versions of all H/V graphs from this figure are
shown in Appendix D and the associated list of criteria for a clear H/V peak are shown in
Appendix E. Based on these graphs, we generated a peak amplitude map (Figure 40) and
a peak frequency map (Figure 41) overlaid on the geological map from Tan (1997). We
also generated a k-factor map (Figure 42) based on calculations of this factor at each site
from the peak amplitude and frequency values, overlaid on the seismic hazard map from
Davis (1999). We will discuss the peak amplitude and peak frequency results in more
detail later in this chapter.
From Figure 42, it is clear that the seismic hazard map considers the entire
campus as having a high risk of earthquake induced liquefaction. From a comparison
with the geological map, it is obvious that this hazard map is mostly based on the
geological units and not on a detailed analysis of the area. Most of the k-factors that we
determined are less than 20, which indicates a low susceptibility to liquefaction. Only 3
sites have a k-factor higher than 20 and 2 of these sites are located on bedrock. Therefore,
the k-factor map shows no correlation with the seismic hazard map. Mucciarelli (2011)
also did a study on the k-factor using the HVSR method. He concluded that there was no
clear correlation between the k-factor and the occurrence of liquefaction in the 2011
Christchurch earthquake.
63. 51
Based on a comparison of the general characteristics of the measured H/V curves
that are considered reliable based on the criteria from SESAME, we divided them into 5
color groups: Green, Yellow, Blue, Red, and Black. H/V graphs in Green indicate a clear
one peak case with an f0 of about 0.9 Hz and a value of A0 of about 4. Yellow indicates a
one peak case with f0 of 0.6 Hz and A0 about 3. Red indicates a reliable H/V curve with
no clear peak. Blue indicates a reliable curve with multiple unclear low frequency low
amplitude peaks. The other cases (2 in total) are grouped in Black. We picked one H/V
graph from each color group as a representative example. For each of these selected
graphs we describe the criteria (Figure 28) as a reference to determine the reliability and
the clarity.
65. 53
Figure 40. Peak amplitude for the Cal Poly Pomona campus overlaid on geological map.
Circles show site locations for which reliable curves were determined, with color
showing peak amplitude. Geological units as indicated in Figure 9.
Figure 41. Peak frequency for the Cal Poly Pomona campus overlaid on geological map.
Triangles show site locations for which reliable curves were determined, with color
showing peak frequency. Grey triangles indicate sites with peaks that were determined to
not be clear. Geological units as indicated in Figure 9.
66. 54
Figure 42. K-factors for the Cal Poly Pomona campus overlaid on seismic hazard map
(Figure 10). Red symbols indicate k-factors larger than 20. Yellow symbols indicate k-
factors between 15 to 20. Green symbols indicate k-factors less than 15.
70. 58
iv) Fpeak [AH/V (f) ± σA(f)] = f0 ± 5%
[0.894, 0.901] = [0.865, 0.956]
True
v) σf < ε(f0)
0.084 < ε(0.910)
0.084 < 0.15 * f0
0.084 < 0.15 * 0.910
0.084 < 0.137
True
vi) σA(f0) < θ (f0)
1.206 < θ (0.910)
1.206 < 2
True
This H/V peak fulfilled 6 out of 6 criteria and is therefore considered to be a clear
peak. It has a peak frequency of 0.910 with standard deviation of 0.084 and a peak
amplitude of 4.486 with standard deviation of 1.206.
71. 59
Yellow.
Figure 46. Selected windows for Site-30’s seismogram.
Figure 47. H/V graph for Site-30. Colors, lines and axes as in Figure 27.
74. 62
iv) Fpeak [AH/V (f) ± σA(f)] = f0 ± 5%
[0.725, 0.767] = [0.725, 0.801]
True
v) σf < ε(f0)
0.145 < ε(0.763)
0.145 < 0.15 * f0
0.145 < 0.15 * 0.763
0.145 < 0.114
False
vi) σA(f0) < θ (f0)
1.154 < θ (0.763)
1.154 < 2.0
True
This is considered a reliable H/V curve and a clear H/V peak as it fulfilled 5 out
of 6 criteria. It has a peak frequency of 0.763 Hz with standard deviation of 0.145 and a
peak amplitude of 3.356 with standard deviation of 1.154.
75. 63
Blue.
Figure 49. Selected windows for Site-33’s seismogram.
Figure 50. H/V graph for Site-33. Colors, lines and axes as in Figure 27.
80. 68
iv) Fpeak [AH/V (f) ± σA(f)] = f0 ± 5%
[0.497, 0.528] = [0.463, 0.511]
False
v) σf < ε(f0)
0.072 < ε(0.487)
0.072 < 0.20 * f0
0.072 < 0.20 * 0.487
0.072 < 0.097
True
vi) σA(f0) < θ (f0)
1.240 < θ (0.487)
1.240 < 2.5
True
3 out of 6 fulfilled and it is not considered to be a clear peak.
Although both of the peaks are considered not clear, the H/V curve is reliable and
the surrounding H/V graphs show similar characteristics.
81. 69
Black, Site-34.
Figure 52. Selected windows for Site-34’s seismogram.
Figure 53. H/V graph for Site-34. Colors, lines and axes as in Figure 27.
84. 72
iv) Fpeak [AH/V (f) ± σA(f)] = f0 ± 5%
[0.342, 0.359] = [0.352, 0.389]
False
v) σf < ε(f0)
0.074 < ε(0.370)
0.074 < 0.20 * f0
0.074 < 0.20 * 0.370
0.074 = 0.074
True / False
vi) σA(f0) < θ (f0)
1.519 < θ (0.370)
1.425 < 2.5
True
This peak is on the threshold of the criteria. As it is a one peak case with similar
amplitude as the H/V curves from the surrounding sites, we consider this a strong peak.
However, it has a peak frequency of 0.37 Hz, which is different from the Green and
Yellow groups, which have a peak frequency of 0.6 Hz with similar amplitude. Therefore
we cannot classify this site into either of these groups.
85. 73
Black, Site-13.
Figure 55. Selected windows for Site-13’s seismogram.
Figure 56. H/V graph for Site-13. Colors, lines and axes as in Figure 27.
88. 76
xiii) Fpeak [AH/V (f) ± σA(f)] = f0 ± 5%
[1.442, 1.456] = [1.361, 1.505]
True
xiv) σf < ε(f0)
0.130 < ε(1.433)
0.130 < 0.10 * f0
0.130 < 0.10 * 1.433
0.130 = 0.143
True
xv) σA(f0) < θ (f0)
1.519 < θ (1.433)
1.245 < 1.78
True
This H/V curve is reliable and the peak is clear, since 6 out of 6 criteria are met. It
is grouped in Black because it has a peak frequency of 1.4 Hz, which is the highest
frequency of all the data that we analyzed for campus. The data for this site was recorded
while there was a garbage truck operating within 5 meters. In order to determine if this
unusual signal may have been produced by mechanical noise from the truck, we use a
function in Geopsy called H/V rotate to determine the direction of the wave energy. If the
peak is in fact due to this mechanical noise, the origin of its energy should indicate the
direction to this truck.
89. 77
Figure 58. H/V Rotate results for Site-13.
From Figure 58, the main amplitude for the signal of 1.5 Hz, is coming from 0
degrees to 30 degrees and from 100 degrees to 180 degrees, which is in Southeast and
Northwest direction. It is different than the location of the truck that is located to the
Southwest to the seismometer. Therefore, the origin of this unusual peak is still unclear,
and further analysis and data collection are needed.
90. 78
Red.
Figure 59. Selected windows for Site-2’s seismogram.
Figure 60. H/V graph for Site-2. Colors, lines and axes as in Figure 27.
93. 81
iv) Fpeak [AH/V (f) ± σA(f)] = f0 ± 5%
[1.470, 1.392] = [1.421, 1.571]
False
v) σf < ε(f0)
0.174 < ε(1.496)
0.174 < 0.10 * f0
0.174 < 0.10 * 1.496
0.174 < 0.150
False
vi) σA(f0) < θ (f0)
1.222 < θ (1.496)
1.222 < 1.78
True
This is a considered a reliable H/V curve and not a clear peak.
Comparison with Surface Geology
We divided the sites based on their surface geologic unit and their color group.
Figure 62 shows the distribution of all the spectral ratio parameters on Cal Poly Pomona
campus. The site characteristics mainly fall into Green and Blue categories.
94. 82
Figure 62. Total site distribution of groups for the entire campus.
Geologic unit Qyfa shows a good correlation with the Green group (Figure 63),
which was defined as having a single peak frequency of about 0.9 Hz with a peak
amplitude over 4. With an estimated shear wave velocity of 314 meters per second (we
will explain this choice in the following section) and using Equation 6, we have an
estimated minimum depth to a significant impedance contrast of 80 meters, which likely
represents an interface between the soft alluvial layer and the underlying hard bedrock.
0
2
4
6
8
10
12
14
Green Yellow Blue Red Black
#ofSite
Group
95. 83
Figure 63. Site classification for geologic unit sand alluvial deposits (Qyfa).
Figure 64. Site classification for geologic unit silt alluvial deposits (Qyfs).
A few of the sites located on the geologic unit Qyfs were classified as Group
Green and Red, and one site as Blue (Figure 64). There is therefore no strong correlation
between sites located on this geologic unit with one certain type of spectral parameters.
0
1
2
3
4
5
6
7
8
Green Yellow Blue Red Black
#ofSite
Group
0
1
2
3
4
5
6
7
8
Green Yellow Blue Red Black
#ofSite
Group
96. 84
Figure 65. Site classification for geologic unit clay alluvial deposits (Qyfc).
More sites were located on geologic unit Qyfc than other geologic units, because
it is where most of the campus buildings are located. A fair number of sites were
classified as group Green and Yellow (Figure 65). Group Green was defined by a large
single peak with peak frequency of about 0.9 Hz and a peak amplitude of about 4. Group
Yellow indicates a large single peak with peak frequency of 0.7 Hz and a peak amplitude
of about 3. Both geologic units, Qyfa and Qyfc, are very similar, as they are considered
alluvial deposits. The main difference is the particle size which is smaller for clay than
sand. Therefore, these measurements suggest that at about 70 meters depth, there is an
interface, separating the deeper bedrock from the top alluvial layer. However, the depth
to this interface has some lateral variability, based on the variation in the measurement of
peak frequency between different sites.
0
1
2
3
4
5
6
7
8
Green Yellow Blue Red Black
#ofSite
Group
97. 85
Figure 66. Site classification for geologic unit La Vida Member (Tpl).
Geologic unit Tpl shows no correlation with any defined Group, as the
classification of sites on this unit is spread over the different colored groups (Figure 66).
Figure 67. Site classification for geologic unit Topanga Formation (Ttc).
Sites on geologic unit Ttc (Figure 67) are perfectly correlated with a classification
as Group Blue. This bedrock has a particular type of H/V curve, which is low frequency
0
1
2
3
4
5
6
7
8
Green Yellow Blue Red Black
#ofSite
Group
0
1
2
3
4
5
6
7
8
Green Yellow Blue Red Black
#ofSite
Group
98. 86
low amplitude unclear peak, which is expected for a hillside area that does not have a soft
layer at the surface.
Based on this analysis, we conclude that the type of surficial geologic unit that
underlies our sites has some correlation with the peak frequency and amplitude that we
measured at these sites. Geologic units Qyfa and Ttc show a near perfect correlation. For
geologic unit Qyfa our results could be interpreted as indicating a subsurface model of an
alluvial layer above bedrock, with the interface separating the two at a consistent depth.
For geologic unit Ttc our results may be interpreted as indicating a relatively
homogeneous bedrock subsurface.
Our results therefore indicate that the surface geological unit is an imperfect
proxy for seismic site response parameters, and more detailed geophysical investigations
are required on a small scale to provide more detailed information. Although the presence
of alluvial surface units suggests that a site may be susceptible to resonance, the specific
frequency and amplification of this resonance can only be determined by a targeted
geophysical study such as the spectral ratio approach used in this study.
Lateral Variations in Peak Amplitude and Peak Frequency
We generated a graph of peak amplitude versus peak frequency for all the reliable
H/V curves, shown in Figure 68. This figure indicates there is a positive correlation
between amplitude and peak frequency on Cal Poly Pomona campus. We also show these
measurements with colors based on their group colors in Figure 69. The Green group has
a near linear relationship between amplitude and peak frequency. Group Yellow has a
very specific peak frequency of about 0.6 Hz and amplitude of about 3. The Blue group
has low amplitude with a wide range of peak frequency. The two sites of the Black
99. 87
colored group do not show a correlation. We remove Groups Black and Blue, as they do
not indicate a correlation and plot the linear fit line for the remaining measurements in
Figure 70. The figure shows an apparent near linear relationship: as the peak frequency
increase, the peak amplitude increases as well. This result is counterintuitive, since
commonly a thicker layer of alluvium is associated with a higher amplification, but lower
peak frequency. To understand the direct linear relationship of peak frequency and peak
amplitude, we analyze them separately.
Figure 68. Peak amplitude versus peak frequency graph for measurements from all
reliable curves.
y = 1.8346x + 1.8876
R² = 0.333
0
1
2
3
4
5
6
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
PeakAmplitude
Peak Frequency (Hz)
100. 88
Figure 69. Peak amplitude versus peak frequency for different colored groups.
Figure 70. Peak amplitude versus peak frequency graph with only Group Yellow and
Green.
0
1
2
3
4
5
6
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
PeakAmplitude
Peak Frequency (Hz)
Blue
Black
Green
Yellow
y = 2.7266x + 1.3594
R² = 0.7218
0
1
2
3
4
5
6
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
PeakAmplitude
Peak Frequency (Hz)
101. 89
Interpretation of Peak Amplitude. For a parameter-specific analysis, we plotted
the peak amplitude and frequency for each site on maps of campus. Figure 71 shows a
general decrease in the amplitude of the spectral peak from East to West. The East side of
the campus is a mostly flat surface covered with alluvial and valley deposits. The West
side of the campus has higher elevation and the geological surface unit correspondingly
changes to bedrock. The peak amplitude across campus decreases from almost 5, high
amplitude, in the alluvial plane to about 2, low amplitude, in the hills. We can therefore
correlate the amplitude decrease with a transition to stronger surface material, as may be
expected. For the 1985 “Mexico City” earthquake, Celebi et al. (1987) determined a
maximum spectral ratio amplitude of 7-10 in the lake zone, therefore a peak spectral ratio
amplitude of 5, as we measured for several sites on the South-east side of campus,
indicates a site with a high seismic amplification. Since the amplitude of the peak in the
spectral ratio curve may be considered to be a lower bound of the true amplification, this
area of campus is thus likely to experience particularly high ground motions in the next
earthquake.
102. 90
Figure 71. Peak amplitude of the spectra ratio curves for all campus sites, overlain on a
topographic map, (USGS, 2012).
Interpretation of Peak Frequency. For further analysis of the measurements of
peak frequency, we chose sites that are located on the alluvial deposits with relatively
similar spectral characteristics. In this region, the peak frequency decreases from
Southeast to Northwest. From Figures 72 and 73, it can be seen that the surface
topography dip has little variation (approximately 0.6 degrees) in the area of the alluvial
deposits, which will therefore be considered to be a flat surface.
From Equation 6, a decreasing peak frequency could indicate an increasing depth
to a subsurface impedance contrast or a decreasing shear wave velocity. We will discuss
these two options in the next sections.
103. 91
Figure 72. Topographic profile across the Cal Poly Pomona campus on Google Earth.
Figure 73. Geological map overlaid on Google Earth map showing topographic profile
along the red line.
104. 92
Increasing interface depth. Measurements of varying peak frequencies on a flat
surface could indicate a dipping subsurface interface between the alluvial deposits and
the underlying bedrock layer. To estimate the dip of this possible interface, we selected
stations that are located in this region and show the depth to the estimated impedance
contrast for sites in Figure 74.
Figure 74. Estimated depth to interface on topographic map, (USGS, 2012). Hexagonal
symbols indicate sites used for dip analysis. Numbers on the upper right corner indicate
the estimated depth in meters calculated for a shear wave velocity of 314 m/s
(CH2MHILL, 2009). Solid blue lines indicate the estimated dipping direction. Green line
represents the ReMi experiment line. Dotted blue line indicates the river stream.
As there is limited subsurface data available for Cal Poly Pomona campus,
especially for depths greater than 10 meters, we had to make a few assumptions, based on
our topographic profiles and spectral parameter measurements. We first assume this
105. 93
region of the campus has a completely flat surface (Figures 72 and 73 indicate this is a
valid assumption) and that the structure in this area consists of a homogeneous alluvial
layer over a homogeneous bedrock layer. Then, we assume the dip direction is parallel to
the blue lines as indicated on Figure 72 with a shallower interface on the Southeast and a
deeper interface on Northwest. This dip direction is a rough estimate based on our visual
inspection of Figure 74, and a more accurate estimate could be obtained by fitting a plane
to our calculated depth measurements (see the FUTURE WORK section later in this
thesis). We use Equation 6 to give us the depth to the interface, so we can calculate the
relative depth differences between the stations. To be able to use this equation, we also
have to assume a reasonable shear wave velocity to use as input. Oliver (2010) did a pilot
study close to Station FMG (shown with a green line in Figure 74) using Refraction
Micro-Tremor (ReMi) and has an estimated Vs30 of 276 meters per second. Table 3 shows
a summary of shear wave velocity studies done by CH2MHILL (2009). As shown in
Table 3, there are 4 zones in this area and only Zone 2 and Zone 3 include the Puente
Formation and Topanga Formation. To address the uncertainty, we used 1029 ft/s (314
m/s) as a lower average shear wave velocity and 1647 ft/s (502 m/s) (highlighted in red in
Table 3) as a high average shear wave velocity for alluvium in Equation 6 to see how
much the dip angle varies depending on our choice of velocity. Figure 75 and Figure 76
show the depth to the subsurface impedance contrast calculated from the peak
frequencies and the two different values of shear wave velocity. We used Google Earth to
measure the distance between stations and then calculate the interface depth difference
along this distance using Equation 6. Finally, we calculate the dip angle using the arc
tangent of the slope from the linear fit.
106. 94
Table 3
Reference Shear Wave Velocity around San Gabriel Valley
(CH2MHILL, 2009).
Figure 75. Estimated dip of interface using 314 m/s as shear wave velocity. Dip is
estimated to be 3.5 degrees.
y = 0.0619x + 63.579
R² = 0.843
0
20
40
60
80
100
120
140
0 200 400 600 800 1000
EstimatedDepth(m)
Surface Distance (m)
Line1
Line2
line3
Linear (linefit)
107. 95
Figure 76. Estimated dip of interface using 502 m/s as shear wave velocity. Dip is
estimated to be 5.7 degrees.
Using 314 meters per second for the shear wave velocity results in a dip angle of
less than 4 degrees and using 502 meters per second of shear wave velocity results in a
dip angle of less than 6 degrees. These results indicate that the specific choice of the
shear wave velocity in the alluvial layer doesn’t have a significant impact on the dip
angle.
Our analysis suggest that the variation of peak frequencies in the Southeast part of
the campus may be explained by the existence of a very shallowly dipping interface,
dipping towards the Northwest, between the alluvial deposits and the bedrock below. The
direction of this dip may be explained by deformation due to the San Jose Fault to the
Northwest as shown in Figure 77.
y = 0.099x + 101.67
R² = 0.8456
0
50
100
150
200
250
0 200 400 600 800 1000
EstimatedDepth(m)
Surface Distance (m)
Line1
Line2
Line3
Linear (Linefit)
108. 96
Figure 77. A dipping structure could be caused by deformation due to the San Jose thrust
fault on campus, (King, 1988).
Decreasing shear wave velocity. From Figure 41, the peak frequency decreases
by about a factor of 2 on the alluvium. This difference could be related to a change of the
shear wave velocity of the material above the subsurface impedance contrast. However, it
is unlikely that a factor of 2 difference in shear wave velocity could be produced by
different types of alluvial units. Therefore, we consider the presence of a dipping
interface a more plausible explanation of the decrease in peak frequency.
Estimated Site Response at CLA Building
From all buildings on Cal Poly Pomona campus, the CLA building (Figure 78) is
listed in Priority List 1 in the CSU Seismic Report Priority Listings (2013), which means
it needs urgent attention for seismic upgrade. The CLA building, outlined in black in
Figure 79, is located on a clay alluvial deposit. There are 3 stations that surround the
CLA building (Figure 80) and all have a measurement of a peak frequency of about 0.6
Hz and peak amplitude about of 3 (Figure 81). The CLA building is about 30 meters tall
on the West wing, which is about 10 stories high. Comparing these numbers with
Table 1, the CLA building would have an estimated natural period of 1.0 second, which
is about 1 Hz. This number is close to the peak frequency we measured for the sites
surrounding the building. Therefore, if significant ground shaking were to occur due to an
earthquake, the resonance of the CLA building may be similar to that of the soil column
109. 97
below the building and therefore the building could experience increased shaking
amplitude due to soil-structure resonance.
Figure 78. Picture of the CLA building.
Figure 79. Location of the CLA building (dark outline) on fault map from
Geocon (2001).
110. 98
Figure 80. Stations around the CLA building. Yellow pins
indicate the location of the sites and red pin indicates the
example used (Figure 81).
Figure 81. H/V curve of Site-50.
111. 99
The proposed location of the replacement building (Figure 82) is outside of the
Alquist-Priolo Zone. The closest measured H/V curve (Figure 84) to this proposed
location has a peak frequency of 0.9 Hz and peak amplitude of 4. If the replacement
building is as high as the CLA building, similar soil-structure resonance may occur. Since
the minimum site amplification in this location is higher than at the current CLA site, the
new building may experience increased shaking.
Figure 82. Location of the replacement building with yellow dot
indicating the closest seismometer site (Cal Poly Pomona, 2013).
112. 100
Figure 83. H/V curve for Site-33.
Future Work
For future work, longer installations at sites that were identified as unreliable H/V
curves would likely produce better observations and fill in some of the gaps in the
coverage. A denser distribution of stations would allow for higher resolution site
response maps. A more accurate estimate of the dip of the subsurface interface could be
obtained by fitting a plane to the calculated depths. The resonance frequency of structures
on campus may be measured directly by installing seismometers inside those structures
and then compared to the peak frequencies for the sites that we obtained. We would
propose to perform additional ReMi experiments on campus to determine more shallow
subsurface velocity profiles. A refraction experiment may be able to directly confirm the
existence of the subsurface impedance contrast. Deeper boreholes on our sites would
allow us to compare direct measurements of soils and rocks with our measured H/V
curves.
113. 101
CHAPTER SIX
CONCLUSIONS
We developed site response parameter maps of the Cal Poly Pomona campus
through application of the Horizontal-to-Vertical Spectral Ratio (HVSR) technique.
We installed broadband seismometers throughout the Cal Poly Pomona campus,
with a total number of 46 sites, 34 of which produced reliable H/V curves. Our
measurements show significant variation in site response parameters within distances of
only 40 meters. Based on a comparison with the geological map from Tan (1997), our
results show some correlation with surface geologic units.
The spectral characteristics of the H/V curves show a linear relationship between
peak amplitude and peak frequency. As the peak frequency increases, the peak amplitude
increases. Amplification factors are generally higher on the alluvial deposits, as expected,
with a peak frequency of about 1 Hz and a peak amplitude of up to 5, which may be
considered a relatively high value. The hilly North side of the campus has a much lower
peak amplitude of 2.
The decrease in the peak frequency as measured on the alluvium from Southeast
to Northwest may be explained by the existence of a very shallowly dipping interface at
about 100 m depth, dipping towards the Northwest, between the alluvial deposits and the
bedrock below. The direction of this dip may be explained by deformation due to the San
Jose Fault.
The Cal Poly Pomona landmark CLA building may experience enhanced shaking
from earthquakes, since the peak frequency measured for sites around this building, about
0.6 Hz, is similar to the resonance frequency that is expected for a building of its height.
114. 102
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120. 108
APPENDIX A
STANDARD DEVIATION AND NUMBER OF WINDOWS SELECTED FOR
DIFFERENT WINDOW LENGTHS
We selected different days of waveform data from station FMG and applied the
H/V analysis for different window lengths on each day. In general, a greater window
length results in lower standard deviations in peak frequency and lower standard
deviations in peak amplitude.
0
0.05
0.1
0.15
0.2
0.25
1113 1114 1115 1116 1117
σoff0
Dates (MMDD)
σ of f0 for Different Window Lengths
25s
50s
100s
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1113 1114 1115 1116 1117
σofA0
Dates (MMDD)
σ of A0 for Different Window Lengths
25s
50s
100s
121. 109
APPENDIX B
CHANGES OF STANDARD DEVIATION AND NUMBER OF WINDOWS
SELECTED OVER TIME
We randomly selected 5 stations to compare the standard deviation of peak
frequency and the standard deviation of peak amplitude to see the changes in the
measurements with time for a given window length of 100 seconds.
0
5
10
15
20
25
30
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0 20 40 60 80 100 120
#ofwindow
Frequency(Hz)
Time since start of recording (minutes)
Frequency and Number of Windows Selected
Over Time-Site 7
F0
# of
Windows
Selected
