Citation: Pineda, K., T. Hayashida (2022), S-wave velocities estimation using seismic ambient noiseanalysis at San Miguel Volcano, El Salvador, Synopsis of IISEE-GRIPS Master’s Thesis.

S-WAVE VELOCITIES ESTIMATION USING SEISMICAMBIENT NOISE ANALYSIS AT SAN MIGUEL
VOLCANO, EL SALVADORKevyn Enrique Pineda Ortiz1
MEE21703

Supervisor: Takumi HAYASHIDA2

ABSTRACTSan Miguel volcano is one of the most active volcanoes in El Salvador. However, its structural properties
are not fully understood. Four broadband seismometers were deployed by the Ministry of Environmentof El Salvador from February 2014 to April 2014. We analyzed continuous ambient noise data (>0.2Hz) using the spatial autocorrelation (SPAC) method and seismic interferometry technique, assuming
the ambient noise is uniform both in time and space. The SPAC technique enabled us to obtain the phasevelocity of surface waves from 0.2 to 1.0 Hz. We also determined Rayleigh-wave group velocities withseismic interferometry, which exploits Green's function from the cross-correlation of ambient noise
recordings for each sensor-to-sensor pair. The combined use of the two methods offered ways to gain
information about the shallow to deep seismic velocity structure from the same dataset. Through a joint
inversion procedure, which included phase and group velocities, we estimated a velocity structurecomposed of four layers with shear wave velocities in the range of 1.0 km/s – 2.8 km/s. We located 15
volcano-tectonic earthquakes using the velocity model, resulting in a better-constrained hypocenter
location. The hypocenter locations coincide with a deformation zone, known as the San Miguel Zone
Fault, on the volcano's northern flank.
Keywords: SPAC method, Seismic Interferometry, Velocity structure, volcano.
1. INTRODUCTIONEl Salvador is located in the northern part of Central America and extends along the Pacific coast,including part of the convergent Cocos-Caribbean plate boundary. The tectonic setting of El Salvador
is controlled by the movements of the North American plate, Cocos plate, and Caribbean plate, but theseismic activity in El Salvador is associated mainly with the subduction of the Cocos plate beneath theCaribbean plate.
The volcanic chain in El Salvador runs parallel to the subduction zone. Six volcanoes in El Salvador are
considered active due to their recent eruptions and fumarolic activity: Santa Ana, Izalco, San Salvador,Ilopango, San Vicente, Berlin, and San Miguel volcanoes. San Miguel volcano is located in the easternregion of El Salvador and is considered one of the most active volcanos in the country. The volcano, its
height is 2130 meters, is a high symmetrical stratovolcano that extends over approximately 190 km2. Its
main crater measures 800 meters in diameter and 340 meters in depth. Several cones had been built
during its eruptions.
In recent years, the volcanic activity of the San Miguel volcano has been presented as scatter events,
including ash and gas plumes emissions. Despite the recent volcano activity, the lack of investigations
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2

Ministry of Environment and Natural Resources (MARN), El Salvador.
International Institute of Seismology and Earthquake Engineering, Building Research Institute.

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on the structural properties of the volcano makes it hard to consider strategies for risk reduction. To
determine the seismic velocity structure of the San Miguel volcano for determining accurate seismicevents associated with its activity, we applied seismic interferometry and the Spatial Autocorrelation(SPAC) method. We analyzed the ambient seismic noise recorded by four broadband stations to estimate
a local S-wave velocity structure model beneath the volcano and interpreted the seismicity associatedwith the volcano and its location.
2. DATAWe used seismic ambient noise data recordedat the San Miguel volcano broadband stations.
After the last eruption in December 2014, theMinistry of Environment and Natural
Resources of El Salvador (MARN)
established a temporary seismic network tomonitor the seismic activity associated withvolcanic activity. The temporary network waslocated mainly on the northern flank of theSan Miguel volcano (Figure 1), andcontinuous observation was performed fromFebruary 2014 to April 2014. The interstation
distance is from 1.5 km to 5.5 km.
Since the sensors were installed on the groundsurface, theirrecord wasaffectedconsiderably by signals with high-frequency
content, such as wind, vehicles, or evenvolcanic signals

Figure 1. Distribution of the seismic station in theSan Miguel volcano. The dotted white line
represents the interstation path3. METHODOLOGY
3.1. Microtremor analysis
The microtremor method studies the behavior of the surface waves and their spectra to infer theproperties of the earth's shallow surface. Seismic sensors measure the velocity of the seismic waves
propagating on the surface, and from the microtremor records, the derivation of the shear-wave velocitydepth relation is obtained. The microtremor analysis seeks to measure the dispersion curve of the surface
waves, which represents the relation between the velocity of the wave at a certain depth, to obtain alayered-earth model of the velocity properties by performing an inverse analysis (Asten et al., 2018).
3.3.1. Seismic ambient noise
Actual broadband seismic sensors are sensitive enough to measure background ambient seismic noise
and weak seismic signals caused by earthquakes. The ambient field vibrations are produced by several
sources, including human activity and natural phenomena. The lack of recognizable content in thevibration signals has led the ambient field to be classified as 'seismic ambient noise' (Nakata et al., 2019).
3.3.2. Spatial autocorrelation method
The Spatial Autocorrelation (SPAC) method (also known as Aki's spatial autocorrelation method)
provides a robust process to infer the properties of the subsurface structure using the relation between
the spectrum of the seismic waves in time and their spectrum in space. Aki (1957) explained that the2phase-velocity of the surface waves could be obtained from the azimuthal average of the crosscorrelation of microtremor measurements considering an azimuth average.
Aki (1965) defined the spatial autocorrelation coefficient 𝜌(𝑟, 𝜔), also known as a theoretical SPAC
coefficient, as a function of the frequency for a given interstation distance (𝑟) and an angular frequency
over an azimuth averaged:
2𝜋1𝜌(𝑟, 𝜔) =∫ Φ(𝑟, 𝜃, 𝜔) 𝑑𝜃(1)2𝜋 Φ(r = 0, ω) 0
The SPAC method proposes a technique to determine the phase velocity 𝑐(𝜔) of the dispersion curvefrom the analysis of ambient seismic noise using Eq. 2, which presents the relation of the phase velocity
and Bessel function of the first kind and order zero 𝐽0𝜌(𝑟, 𝜔) = 𝐽𝑜 (𝑟𝜔)𝑐(2)3.3.3. Seismic interferometry
While the SPAC method studies the microtremor in the frequency domain, the seismic interferometry
method analyzes the microtremor in the time domain. The term 'seismic interferometry (SI)' refers to theprinciple of generating a virtual seismic signal by the cross-correlation of seismic waveforms (codawaves, seismic ambient noise) recorded at two different receivers (Wapenaar et al., 2006). The primary
purpose of SI is to obtain information about the Green's function (𝐺) (i.e., ground property) between
two seismic stations using the correlation (𝐶) of the recorded waveforms using Eq. 3.
𝐶(𝑥𝑎 , 𝑥𝑏 ) =𝛼2 𝑑[𝐺(𝑥𝑎 , 𝑥𝑏 , 𝑡) − 𝐺(𝑥𝑎 , 𝑥𝑏 , −𝑡)]
2 𝑑𝑡

(3)

where 𝛼 represents the normalization factor assuming the equipartition of the ambient wavefield.

4. ANALYSIS AND RESULTS
4.1. Spatial autocorrelation method
The SPAC coefficients (also called 'coherence') (Eq. 1) were calculated using only the verticalcomponents of the ambient noise and for every individual station pair. The procedure was conducted for
all the frequencies from 0.1 to 10 Hz. A marked decrease in the SPAC coefficients at high frequencies
was observed. At high frequencies, the wavelength becomes much smaller than the interstation distancecausing an increased attenuation. Moreover, at high frequencies, the scattering is higher, which causes
a low signal-to-noise ratio (SNR). The maximum coherence of the curves occurs at frequencies smaller
than 0.4 Hz. Thus, the phase velocity measurement is limited at low frequencies. However, we applied
the approach Ekström et al. (2009) established, which allowed us to increase the frequency range ofphase velocity measurements considering the SPAC coefficients' zero-crossing points. As a result, thephase velocity was calculated at frequencies from 0.1 to 1 Hz.
4.2. Seismic interferometry
Green's function was constructed by cross-correlating between all component combinations, including
the radial (R) and traverse (T) components, as well as the vertical (Z) component. The data processing
was done using a software package developed by T. Hayashida, and it followed the methodology
proposed by Bensen et al. (2007).

3To increase the power of the surface wave inour cross-correlations, we used the proceduredeveloped by Takagi et al. (2014) to separate
the body and the Rayleigh wave using the ZR
and RZ components of the cross-correlations.
Finally, Green's function was retrieved fromthe Rayleigh wave cross-correlation, using
Eq. 3. The group velocity was measured using
the Rayleigh wave Green's function. The
group velocity was obtained using themultiple-filter technique of Dziewonski et al.
(1969), which consists of applying a narrow
band-pass filter to determine the groupvelocity. Figure 2 shows the Rayleigh wave
dispersion curve obtained from applying theSPAC method and seismic interferometry

Figure 2. Estimated Rayleigh wave dispersion
curve.
4.3. Velocity model estimation

We applied a joint inversion technique (Hayashida et al., 2019) to solve the obtained dispersion curve,
which includes the phase and group velocity, as an iterative method to solve a nonlinear problem.
In the inversion process, we used different initial models inwhich the P-wave velocity, the number of layers, andthickness of the layers were considered. The mismatchvalue (misfit) was determined for each calculated profile.
the shear wave velocity was calculated from the dispersion
curve, while the P velocity was defined using a linear
relation from the S-wave velocity. And the density of thelayer was obtained using the relation established by
Kitsunezaki (1990).
The final velocity model is composed of five layers;
overlying a homogeneous half-space. The model depth
extends 3.5 km below the surface of the San Miguelvolcano with P-wave velocities from 2.3 to 7.1 km/s and an
S-wave velocity of 0.9–5.5 km/s (Figure 3). The velocityof the deepest layer is consistent with the local velocity
model of El Salvador established by Marroquín (1998). The
calculated velocity model presents a higher resolution in theshallower layers than the local velocity model of ElSalvador. The effects of the sedimentary layers in thevolcanic structure are observed, which causes low
velocities in the shallow layers in our velocity model.

Figure 3. P-wave and S-wave velocity
model computed for the San Miguelvolcano. The dotted line represents thelocal velocity model of El Salvador4.4 Earthquake location
We located some earthquakes associated with the volcano. The waveform shape was considered todiscriminate seismicity associated with tectonics sources.

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We focused the analysis on Volcano-Tectonic(VT) earthquakes due to their impulse P-wave
arrivals. Considering the frequency content of theVT earthquakes and the waveform shape, we
selected from our data three earthquakes and usedthem as templates to determine similarearthquakes.
The events were chosen using the open-source
Repeating Earthquakes Detector in Python(RedPy) tool (Hotovec-Ellis et al., 2016). The
tool detected 15 signals considered repeating
earthquakes from one day-length data.
We manually picked the P-phase arrivals in thedetected events using SEISAN software(Havskovet al., 1999) and the obtained velocity
model. The bottom layer in our estimatedvelocity model is a half-space with low
resolution, so we combined the bottom of thefourth layer of our velocity model with the top of
the local velocity model of El Salvador toeliminate this ambiguity. Figure 4 shows thecombined velocity structure.

Figure 4. Velocity model used in the earthquake
locationIn the location procedure, some difficulties were found due to ambiguous phase arrivals and complex
waveform patterns. The complexity augmented considerably for distant stations, in which the SNR waslow. Due to epicenter determination being carried out with only four stations, a phase misinterpretation
could result in significantly significant errors.

Figure 5. Located seismicity around San Miguel volcano. Inverted triangles represent seismicstations. The circles represent the location of seismic events using the proposed velocity model; thediamonds used the local velocity model proposed by Marroquín (1998). The dotted line indicatesthe San Miguel Fault Zone

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The epicenter locations showed a tighter clustering of events located on the northern flank of the San
Miguel volcano. The epicenters show a focus distribution; all are beneath the volcanic building with ashallow depth of about 1.5–3.0 km (Figure 5). The location of earthquakes in the volcano's northernflank is consistent with the deformation zone in the San Miguel volcano, known as San Miguel Fault
Zone. We also located the selected events using the velocity model proposed by Marroquín (1998) todiscuss the location differences. While there is no difference between epicenter locations, the maindifference is observed in depth. There is an increment of the scatter for the events calculated with theprevious velocity model, possibly due to the difference of shallow layers of the models. The lack ofclusters is more evident in depth.
5. CONCLUSIONSIn this investigation, seismic ambient noise records in the San Miguel volcano were studied; the analyzed
data included four months of continuous measurements recorded by four broadband stations covering a
distance of 1.5 to 5.5 km around the volcano. We investigated the structural properties of the San Miguelvolcano by estimating the Rayleigh-wave dispersion curve using the SPAC method and SI. Our seismic
ambient noise analysis provided the first velocity model for the San Miguel volcano, in which few
previous geophysical studies exist. The obtained velocity model is composed of four sedimentary layers(S-wave velocities of 0.9–2.8 km/s; P-wave velocities of 2.3–4.4 km/s) overlying a half-space layer.
Implementing the obtained velocity model resulted in better-constrained locations and depths in theanalyzed seismicity. The high seismicity found in the northern flank of the San Miguel volcano isconsistent with the location of the San Miguel Fault Zone. The information about this seismicity canrepresent an improvement in monitoring the San Miguel volcano, in which an accurate seismic-event
location can represent critical information to its precursory and eruptive activity

ACKNOWLEDGEMENTS
Foremost, I would like to express my sincere gratitude to my research supervisor, Dr. Takumi Hayashida,for providing me with invaluable guidance throughout this research. It was a great privilege to develop
this investigation under his direction. I want to share my gratitude to all the professors of IISEE/BRI for
their continuous guidance and valuable assistance during my one-year study in Japan. Thanks for theacademic advice and the teachings about scientific research and life.

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