Worked under Professor Walter D Mooney in the Earthquake Research Science Center researching how sediments impact seismic wave transmission as detected by surface seismometers.

1. Influence of Sediments on Recorded Seismic Amplitudes for Different Geological
Locations around the San Francisco Bay Area
Rohan Karunaratne, Stanford University
Summer Internship at the United States Geological Survey
Earthquake Science Center
345 Middlefield Road
Menlo Park, CA 94026
August 2022
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2. Table of Contents
Table of Contents………………………………………………………………………………….2
Abstract……………………………………………………………………………………………3
Introduction
Background……………………………………………………………………………..4, 5
Definitions and Terminology…………………………………………………………...6, 7
Research Objectives & Predictions……………………………………………………..…8
Methodology
Approach & Design………………………………………………………...…9, 10, 11, 12
Testing Methods…………………………………………………………………….……13
Results…………………………………………………………..……………14, 15, 16, 17, 18, 19
Discussion and Conclusions
Interpretation…………………………………………………………………………20, 21
Limitations……………………………………………………………………………….22
Future Improvements…………………………………………………………………….23
Recommendations…………………………………………………………………………….….24
Acknowledgements…….………………………………………………………………………...25
Citations……………………………………………………………………………………….…26
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3. Abstract
This project investigates how different sediments below a seismometer impact the
recorded ground magnitude of an earthquake. To test this, a shake table is constructed, and
Raspberry Pi accelerometers measure the difference in amplitude between a seismometer placed
in sediment, with a control seismometer that was mounted directly to the shaking platform. A
total of six sediment samples are tested at three different frequencies of shaking. The results
show that sediments abundant with sand, rocks, and dirt tend to have increased ground
acceleration amplitudes as frequency increases, whereas sediments with mud tend to have
relatively constant amplitudes. These results are because the samples are non-Newtonian in
nature, and thus have a variable viscosity when different forces are applied. This phenomena
leads to the samples acting more like solids when under greater forces, and liquids at rest. To
conclude, we recommend that seismometers be placed in regions that do not have high mud,
water, or vegetation content, and instead be placed in areas with partially water-saturated dirt and
rocks.
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4. Background
The San Francisco Bay Area in California, is one of the most well-known geological regions in
the world due to its high seismic activity. Major fault lines, including the San Andreas and
Hayward faults, flank the Bay Area, which is the region between the Santa Cruz Mountains to
the East and the Diablo Range towards the West. This poses a compelling opportunity for
geologists to gather seismic data on this region through the use of seismometers, which are
instruments used to record movements on the surface, or under ground if the seismometer is in a
borehole.
Each region of the Bay is unique when it comes to the composition of that particular
environment’s sediment. In the Bay, thick sediments of mud and clay are the predominant
materials. Coastal locations like San Gregorio Beach and Half Moon Bay commonly contain
sand, coral, and smaller rock particles. Along the inner edge of the Bay, there are substantial
mineral deposits, specifically salt (halite), and marshes in parks such as the Coyote Hills and
Don Edwards.
This setup creates a challenge for geologists who are interested in seismic research. The various
microclimates and geologically diverse regions within the confines of the Bay Area make it such
that soil and sedimentation have substantially different compositions over short ranges. This
project seeks to solve how varying sediments impact the transmissibility of an earthquake wave
detected on a surface seismometer.
Figure 1: San Francisco Bay Area, California, Fault Lines
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5. Earthquake waves usually arrive in two prominent phases, P waves and S waves. P waves
compress the ground and arrive first, whereas S waves will shake the ground from side to side
and arrive after the initial P waves. For a seismometer to be able to detect these waves and
aggregate accurate, robust data successfully, concrete housings are often built bolted to the
ground to ensure that the waves can transmit to the seismometer without being lossy.
Seismometers around shores, specifically in the Bay, pose a unique challenge due to the varying
types of sediment that comprise the accompanying baylands. Thick mud sediments that
aggregate in these regions appear to dampen the waves as they are transmitted through the crust,
with sediments like dirt and rocks having some effect on the amount of energy, and
proportionally, the amount of movement a seismometer can detect.
Figure 2: Visual depiction of the direction of shaking
caused by corresponding P and S waves
We expect to find some difference in magnitude detected by the seismometer and the magnitude
that the simulator generates. Buildings that are built to withstand earthquakes make use of shock
absorbers and fluid incompressibility to absorb waves, which is a principle that reinforces our
expectation. The marshy and viscous nature of the sediment in theory, will absorb some of the
energy released during an earthquake, printing a lower magnitude on the seismometer compared
to what is produced by the simulation.
This project can solve for two significant findings. As related to Bay Area geology, it is useful to
know the locations where seismometers are most efficient such that they aggregate the most
useful data possible, and be able to calibrate to adjust for the damping that may occur due to
aggregated sediment. If a mathematical model can be used to predict the transmissibility of
earthquake waves based on metrics of the soil, it could be helpful in civil engineering
(construction of new buildings, and how the waves are absorbed by the surrounding land), and
seismologists trying to place instruments.
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6. Definitions and Terminology
Seismic Sensor (accelerometer)
For the purposes of this project, the seismic sensor makes use of accelerometers. Traditional
seismometers often use some form of writing instrument suspended from an arm that oscillates
when motion is applied. This causes seismic waves to be drawn on a rotating drum that is
measured to determine magnitude on the Richter scale. In consideration of computerized data
analysis, a Raspberry Shake 4D is utilized, which uses a Raspberry Pi computer with an
accelerometer shield mounted on top.
Sediment
Considering design constraints on this project, the term “sediment” refers to the top soil
collected anywhere from the surface level to around 1 ft in depth, assuming that the seismometer
will be mounted relatively close to the surface.
Waveform Generator
A waveform generator is an electronic instrument that allows for a signal to be outputted. At a
base level, this signal can be manipulated by pattern (square wave, sine wave, etc.), amplitude,
and frequency. The amplitude ranges from around 0-12V, and the frequency range is quite large,
from as low as, but not limited to, 0.5Hz, all the way to 1000Hz and beyond.
Figure 3: Waveform Generator made by Stanford Research Systems which was used in this project
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7. Fourier Transform
A Fourier Transform is a mathematical operation that converts a time domain waveform into a
set of pure frequencies, which, when summed, yields the time domain waveform.
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8. Research Objectives and Predictions
How does each type of sediment impact the amplitude of the recorded earthquake waves
produced by the simulator?
The first goal is to note any differences in earthquake wave transmissibility that may be a result
of sedimentation below the seismometer, by comparing magnitudes recorded by the
seismometers for differing sediment, and frequencies.
Are there quantifiable, or qualitative metrics that we can use to establish a relationship
between the type of sediment used, and the difference between the observed and actual
magnitudes of the earthquakes produced?
Determining a mathematical or qualitative relationship allows for predictions to be made in areas
outside of the research sites. When determining placement of seismometers or other geological
instruments, this relationship is important to consider as part of the preparation before
deployment.
Predictions
We predict that there is an inverse relationship between the amplitude recorded and the
frequency. As the frequency increases from 1 to 3 Hz, the relative transmissibility of the wave, as
recorded by the control accelerometer and the accelerometer in the sediment, should decrease.
When the amplitude of shaking increases, more energy is used to shake the sediment, as opposed
to increased motion recorded on the seismometer, thus resulting in a greater difference between
the control and sediment seismometers.
We also predict that mud will have the greatest wave transmissibility, and sediments such as dirt
and rocks, will have the lowest transmissibility. Mud is fairly viscous, and thus will retain its
sedentary state unless acted upon by a substantially larger force, as opposed to dirt and rocks,
which can be more easily moved comparatively.
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9. Approach and Design
The Shake Table
The design of the testing fixture needs to solve two challenges - to be able to house two
seismometers that are collecting values simultaneously, and have the capability of varying
waveforms displayed on the shake table.
To achieve these design goals on a shake table, a speaker is used as the source of the vibrations.
The speaker is approximately 1 ft in diameter, which for the purposes of this project, was
salvaged from an old bass speaker. The speaker has a rod glued in the middle, and is connected
to a plastic sheet that sits on a moveable rail. The rail is from a standard kitchen ball bearing
drawer slide, and has a relatively low amount of friction. The plastic sheet accommodates a fixed
seismometer (the control) and an additional plastic tray. The plastic tray holds the sediment being
tested, as well as a seismometer which lies atop the sediment.
The speaker is powered by a waveform generator that can create different waveforms with
varying amplitude, frequency, and shape. For the purposes of this project, an amplitude of 8V,
and frequencies ranging from 1-3 Hz are used in the sine wave pattern. Manipulating frequencies
is an important design consideration when building the shake table. At different frequencies, the
way energy is transferred to the seismometer varies. For instance, if the vibrations are small, it is
assumed that energy is transferred with significantly less dissipation, compared to larger
vibrations, where the sediment may shake more.
Figure 4: Image of the Shake Table used in this project, which makes use of an old bass speaker connected to the
shaking surface containing two seismometers via metal rod
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10. The seismometers are powered using a standard USB C charging cable, and interfaced via
Ethernet. A hub is used to allow for multiple ethernet devices (the seismometers) to be plugged
into the back of the router. The router is then remotely accessible for real-time data via Wifi.
Sediment Collection
In order to reach a broad conclusion about how waves are dissipated through different materials,
sediment is collected from different regions around the Bay Area. The 2 places of interest are the
Baylands in Palo Alto, which contains a large marshland primarily consisting of mud and various
plants, and Don Edwards in Newark, which has muddy riverbanks, sandy shores by the Bay, and
various dirt trails. The areas selected allow for the collection of different sediments that are
composed of various materials.
Figure 5: Map of the southern region of the San Francisco Bay, containing the two surveying sites used in this
project: Don Edwards in Newark, and Baylands Nature Preserve in Palo Alto
A total of 6 samples were taken:
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11. 1. Dirt Sediment primarily consisting of Tree Bark and Decomposed Leaves (Don Edwards)
2. Fine Sand and Rock Composite (Don Edwards)
3. Fine Sand and Dirt with Slate Rock (Don Edwards)
4. Fine Dirt with sizeable Rock Clusters (Don Edwards)
5. Mud Sediment with Roots and Plants mixed in (Baylands)
6. Mud Sediment with a high Water Content (Don Edwards)
Figure 6: Image of the six samples used in this project
Each of these samples are stored in sealed plastic containers at room temperature to retain as
much of the original properties of the sediment (water content, etc.). After containment, the
samples are tested 2-3 days later, and the data for each type of sediment is collected on the same
day, to avoid inconsistencies with the testing procedure.
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12. Figures 7 and 8: USGS Intern Emilio Cardenas collects sediment to be used on the Earthquake Shake Table
Figure 9: A stork in Don Edwards Wildlife Refuge surrounded by mud sediment that comprises most of the sea floor
and surrounding baylands in the Bay Area
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13. Testing Methods
To test the transmissibility of the waves through each type of sediment, a testing procedure is
devised to keep as many variables controlled as possible. Two seismometers are used
simultaneously, and record values at the same time, which ensure that minor irregularities in the
waves produced impact each seismometer’s measurements equally. Each seismometer is
accessed via ethernet, through the Swarm software program. The Swarm software records the
seismicity of the waves detected at a sample rate of 10 times per second, which are then exported
as a Matlab readable text file, and uploaded into Excel for processing.
To begin, the sediment is poured into the tray and leveled. A seismometer fixed to a foam sheet
is lowered into the tray, to avoid any sediment impacting the hardware of the seismometer. The
waveform generator is turned on, and the data is then recorded from each seismometer. The
frequencies are changed to 1Hz, 2Hz, and 3Hz; both amplitude (8V), and shape (sine wave) are
held constant. After all three frequencies are recorded, the sediment is substituted. This
procedure is repeated for the six sediment samples collected. In total, 18 readings are recorded.
Figure 10: SWARM software developed by the USGS used to record data
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14. Results
Equation for Calculating the Coefficient
To solve how close each of the seismometer’s values are to one another, divide the seismometer
with the smaller value, which is typically seismometer 110E, by the seismometer with the larger
value, seismometer F08. When this number is greater than 1, this indicates that 110E is greater
than F08, or the recorded value of the control seismometer is greater than the seismometer’s
recorded value in sediment. A value that is less than 1 indicates that the seismometer in the
sediment gives a greater value than that of the control seismometer’s reading.
To determine the coefficient, complete this calculation for 120 values; approximately equivalent
to one cycle on the Fourier Transform graph. The calculation allows enough data points to
account for any minor sampling errors. An average is taken of the 120 values to determine the
average coefficient for each sediment and frequency. The only additional adjustment pertains to
Sample #4 at the 1Hz Frequency, in order to remove outlier points 102, 103, 107, 119, and 120,
which all had quantities over 3, well over any reasonable value for the division between
seismometers. Without adjustment, this value would have been 1.07642051.
Sample Type and Frequency Average Coefficient
Sample #1 Dirt 1Hz 0.37134244
Sample #1 Dirt 2Hz 0.78352988
Sample #1 Dirt 3Hz 0.93511146
Sample #2 Fine Sand Rock Composite 1Hz 0.48216621
Sample #2 Fine Sand Rock Composite 2Hz 0.53527357
Sample #2 Fine Sand Rock Composite 3Hz 0.71108591
Sample #3 Fine Sand and Dirt with Rock Sheets 1Hz 0.32135605
Sample #3 Fine Sand and Dirt with Rock Sheets 2Hz 0.42496322
Sample #3 Fine Sand and Dirt with Rock Sheets 3Hz 0.87580838
Sample #4 Fine Dirt with Rock Clusters 1Hz 0.66939443
Sample #4 Fine Dirt with Rock Clusters 2Hz 0.71443843
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15. Sample #4 Fine Dirt with Rock Clusters 3Hz 0.84236821
Sample #5 Mud with Roots 1Hz 0.24973391
Sample #5 Mud with Roots 2Hz 0.62876419
Sample #5 Mud with Roots 3Hz 0.18340193
Sample #6 Mud with Water 1Hz 0.5870516
Sample #6 Mud with Water 2Hz 0.44195688
Sample #6 Mud with Water 3Hz 0.70701911
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16. Figure 10-15: Plots of the Different Frequencies tested for each Sample against the Coefficient calculated
The Charts
Below are charts that compare the Fourier Transforms of each seismometer for each sediment
type and frequency. In order to derive each graph, a series of computations is done using raw
data gathered from the seismometers, with values that correspond to the same time, allowing
simultaneous interpretation of values (recorded in UTC). When plotted, the values approximately
resemble sinusoidal motion, which is expected due to the pattern given to the waveform
generator. Below is an example chart of time recorded versus raw values.
Figure 16: Raw Values collected by the Accelerometer depicting Time (s) against Amplitude detected
Each value has an assigned placement instead of a time, ranging from 1 to 400. Over 6000 values
are collected from the seismometers, but for simplicity, only 400 values were used.
The 400 values are passed through a Fourier Transform, which converts Time versus Amplitude,
into Frequency versus Amplitude. This helps to negate some of the potential sampling errors that
may occur due to one of the values being abnormally high or low. Passing the values through the
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17. Fourier Transform results in a complex value output. The complex value includes the magnitude
and the phase. Each of the values is passed through an IMABS function in Excel, which converts
each of the complex values into an absolute value. The final plot displays the change in time
between the values recorded (10 samples per second, so 0.1) against the IMABS of the Fourier
transform. The charts below are two diagrams overlaid, where the blue series represents
seismometer F08 (the seismometer in the sediment), and the orange series represents
seismometer 110E (the control).
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18. 18

19. Figures 17-34: Fourier Transforms for each Sample type, and each Frequency, with the blue series representing
seismometer F08 (sediment) and the orange series representing seismometer 110E (control). The Frequency is
plotted on the X axis in Hertz (Hz), and the Amplitude is plotted on the Y axis.
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20. Interpretation
The Testing Procedure yields results that are quite different from the original predictions.
Samples 1, 2, 3, and 4 all have increasing coefficients when frequency increases, as opposed to
Samples 5 and 6 which have coefficients that stay relatively flat. Samples 1, 2, 3, and 4 also
have smaller deviations, compared to Samples 5 and 6 which tend to have results that vary more.
These results are not supportive of our prediction that a greater frequency would lead to a greater
difference between the readings from the two seismometers. Alternatively, the data shows a
tendency for greater frequencies to make the seismometers output similar values.
Samples 1, 2, 3, and 4 (predominantly dirt, sand, and rocks)
Samples 1-4 show relatively the same sample trends, where, as the frequencies increase from
1Hz to 3Hz, the corresponding coefficient tends to increase. One explanation for this is the idea
that sediments collected for Samples 1, 2, 3, and 4 are non-Newtonian. A non-Newtonian fluid is
a substance that does not follow the traditional Newtonian laws, particularly when it comes to
viscosity. Some examples of non-Newtonian fluids are quicksand, and paints, but also include
slurries. Slurries are mixtures that contain water, such as concrete, or even sand with water. Due
to numerous samples being collected around aqueous environments (Samples 2, 3, 4), it can be
argued that these samples fall under the category of slurries.
Some non-Newtonian characteristics include changing viscosity when a force is applied. Under
normal conditions, non-Newtonian fluids act like liquids, and exhibit much of the same
properties of liquids. However, when a force is applied, they tend to change viscosity. The
samples, when at rest, have a high water content, and when combined with the sediment, makes
it so that the control seismometer and the sediment seismometer have very different values,
displaying properties of a liquid. When an increased force is applied through a greater amount of
shaking, the properties then resemble a jelly, and thus allows for the transmission of waves in a
more uniform manner. This is supported by the 3Hz reading tending to have a coefficient closer
to 1, meaning the 2 seismometers observed readings approximately the same value.
Samples 5, and 6 (mud and vegetation)
Samples 5 and 6 display a different trend, where the coefficients tend to have a greater variance,
but remain relatively constant. Mud can also be categorized as non-Newtonian, as depending on
how much force is applied to the mud, this changes how viscous the mud is. An example of this
is during a mudslide, where mud can remain fairly solid when small amounts of force are
applied, but then when pressure is built up, and larger forces are applied, the mud tends to cave.
Due to testing only up to 3Hz, it may be possible that for the non-Newtonian effects to be
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21. observed, larger frequencies and greater amplitudes need to be tested. At the small intervals that
are being observed in this project, there is not a substantial amount of motion that could lead to
an observable difference in the behavior of the mud.
Figure 35: Non-Newtonian Fluids versus Newtonian Fluids as a measure of Shear Rate and Shear Stress
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22. Limitations
Despite the interesting findings, there are several limitations to this project that should be
acknowledged. First, only 2 sites were sampled from around the Bay Area, of which, only 6
samples were taken. Therefore the scope of the conclusions drawn should remain within a
reasonable range.
The testing fixture was also built from scratch, thus minor human errors during sampling may
have impacted the results. While we took precautions and performed the testing to the best of our
abilities, use of a professional shake table with standard seismometers is recommended to
confirm the results.
The Raspberry Pi utilized accelerometers as the primary method of collecting magnitude data.
While many seismometers are increasingly digital, which comes with the added advantage of
being smaller, it is still important to acknowledge that errors in the code, sampling data, and the
low sample rate, may contribute to shortcomings of the data.
There are minor inconsistencies and errors in the testing fixture that cause the measured input
waveform not to be uniform in nature. As shown by the Fourier Transform graphs, there are
multiple frequencies beyond just the input, which are inconsistent with the waveform generated,
which consists of a uniform frequency. The exact cause of this phenomenon is unknown,
however, by dividing simultaneous seismometer values against each other, most of this effect is
removed, and the comparative nature of the findings still remains valid. A follow-up idea is to
construct a more precise shake table and repeat the testing procedure to see if the error pertains
to the seismometers or the quality of the shake table.
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23. Future Improvements
As discussed in the results from Samples 5 and 6, one possible improvement is to test greater
frequencies and amplitudes on the mud samples. Because there is a high vegetation content, and
higher water content in the samples, increasing the frequency and amplitude to provide greater
data points may lead to greater insight into how mud behaves.
The results should be confirmed by gathering sediment from more sites around the Bay, along
with additional testing to ensure that the trends confirmed by the results can be replicated and
observed in other geological regions. If the results differ, an investigation into the reasoning
behind the discrepancies should be launched.
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24. Recommendations
Seismometers should generally be placed in regions along the Bay that do not contain substantial
mud sediments. As confirmed by the results, mud sediment tends to behave differently compared
to other materials such as sand and rocks. The volume of mud at the bottom of the bay, and
surrounding baylands makes it not ideal for seismic readings. Finding a site that is not
predominantly aqueous (but has substantial, non-zero water content), preferably in dirt or rocks
will ensure high seismic transmissibility. The rocks and dirt help the seismometer make flush
contact with the ground, and increase reliability in terms of the transmission of the wave.
These principles may even extend beyond Earth and apply to other planetary bodies. If
seismometers are to be placed for data collection on Earth’s Moon or Mars, finding a region of
solid contact that contains a balance between fine sediment and rocks may be ideal to ensure the
best transmission of waves. Placing seismic instruments near poles is to be avoided due to the
probability of increased water content, or in surrounding regions where water may mix
substantially with sediment.
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25. Acknowledgements
I would like to thank Dr. Walter D Mooney of the Earthquake Science Center of the United
States Geological Survey in Menlo Park, California for the opportunity to carry out this project.
His guidance, valuable insight, and assistance was what made this research possible, and I am
grateful for all of his continual support.
I would also like to thank all of my fellow Junior Research Interns at the United States
Geological Survey, including Emilio Cardenas, Juliette Kilgore, and Nikhil Chaudary for helping
me with data collection, and providing valuable feedback and insights into my project. I would
also like to thank Senior Research Interns Rachel Chidlow, Karimah Comstock, and Rebekah
Medley for their feedback and review on my project.
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26. Citations
“Fourier Transform.” Wikipedia, Wikimedia Foundation, 23 Aug. 2022,
https://en.wikipedia.org/wiki/Fourier_transform.
“Seismic Wave.” ONO SOKKI,
https://www.onosokki.co.jp/English/hp_e/patio/seismicwave.htm.
“Map of Known Active Geologic Faults in the San Francisco Bay Region.” Map of
Known Active Geologic Faults in the San Francisco Bay Region | U.S. Geological
Survey,
https://www.usgs.gov/media/images/map-known-active-geologic-faults-san-francisco-ba
y-region.
“Non-Newtonian Fluid: What Is It, Characteristics, Uses, Viscosity, Examples.”
Euston96, 2 Dec. 2021, https://www.euston96.com/en/non-newtonian-fluid/.
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