Citation: Nadimpally, B., T. Hayashida (2020), Application of seismic interferometry to broadband
ambient noise recordings in and around the Philippines islands, Synopsis of IISEE-GRIPS master report.

APPLICATION OF SEISMIC INTERFEROMETRY TO
BROADBAND AMBIENT NOISE RECORDINGS IN
AND AROUND THE PHILIPPINES ISLANDS
Bryan Austria NADIMPALLY1,2

Supervisor:

Takumi HAYASHIDA3

ABSTRACTThis study examines the possibility of ambient noise seismic interferometric applications to thePhilippines, consisting of many large and small islands. We explored the use of conventional dataprocessing methods and their effectiveness in generating dispersion measurements in the period from 5s to 50 s. We used 10 months of data for 27 broadband seismic stations. The component rotation wasalso performed for the extraction of Rayleigh waves from body wave contaminated cross-correlation
functions. Dispersion measurements were conducted, although there were still several challenges to theapplication of interferometry. Parameters such as signal-to-noise ratio, stack number, interstation
distance, power spectral density plots and cross-correlation symmetry were analyzed in relation to thefeasibility of obtaining dispersion measurements. The Rayleigh-wave phase velocities were stably
estimated for selected station pairs in comparison to group velocities. The phase velocities were also
obtained regardless of azimuth, indicating that the directional dependency of seismic ambient noise issmall.
Keywords: ambient noise, interferometry, dispersion measurements, Rayleigh wave, the Philippines.
1.INTRODUCTION
The omnipresent signal present in seismograms known as seismic ambient noise is a superposition ofbody waves, surface waves, microseisms and micro tremors. Seismic interferometry is a popular tool
aimed at extracting a deterministic information from ambient noise. The deterministic signal remains
concealed within a superposition of body waves and surface waves. The pioneering work of Aki andClaerbout initiated the idea that the abundant ambient noise often seen in seismic records may holdvaluable information. Claerbout (1968) later demonstrated it experimentally and proved theoreticallythrough rigorous investigation and mathematical derivations. Several studies have been made ininterferometry leading up to the first geophysical application by Campillo and Paul (2003), whoprovided details on how the impulse responses between receiver pairs are derived from cross-correlationof seismic coda waves.
Several assumptions need to be made for the results to hold. One of them, equipartition,
generally means that energy arrives equally in all directions. If diffusivity exists within the earth it cancreate ideal conditions for equipartitioning. The diffusivity exists under conditions of multiple scatteringand random distribution of uncorrelated noise sources. Wapenaar (2001) assumed that the earth structureis heterogeneous, lossless, and anisotropic medium to be able to retrieve elastodynamic Green’s function.
Based on these theories and considering that cross-correlation of ambient noise corresponds to an inter1

Philippine Institute of Volcanology and Seismology, Philippines.
IISEE-GRIPS master course student.
3International Institute of Seismology and Earthquake Engineering, Building Research Institute.
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station Green’s function, we conduct seismic interferometry. Green’s function between two receivers isretrieved from cross-correlation of the ambient noise field. Given the existence of complex geologicalphenomena in the Philippines, owing to its location in the ring of fire, it would be quintessential to study
the crustal structure in and around the Philippine islands. Previous techniques in seismology focused on
obtaining structural information based on earthquake activity or active source imaging. The method
introduced in this paper however focuses on the use of ambient noise and the technique of seismicinterferometry. Seismic interferometry was initially applied to a continental setting, but recent studies
have shown successful examples in island nations such as Japan and New Zealand. This study will be
the first application of seismic interferometry in Philippine islands. We will now assess the feasibilityof obtaining dispersion measurements for the Philippines.
2.DATA
Broadband data used in this study has been maintained and monitored by the Philippine Institute ofVolcanology and Seismology. Ten months of data from August 01, 2018 to May 31, 2019, recorded at27 stations with availability greater than 80 percent was used. Data from Nanometrics Trillium 120
PA/QA and Trillium 240 sensors were utilized for the 10-month period. It was however challenging due
to the satellite-telemetered seismic data being intermittent. This was resolved by inserting a zeroamplitude waveform after which the gaps became time jumps. And it was discovered that to keep
variations in data processing results to a minimum, it is best to only use data with time jumps less than
25 s. Considering the sensor-to-sensor distances ranging from 31 km to 1,721 km, dispersion
measurements are observed between 5 s and 50 s.
3.THEORY AND METHODOLOGY
The data processing goes through several phases following Bensen et al. (2007) and utilizes an in-houseprogram for seismic interferometry (partially used in Hayashida et al., 2014). The first phase involves
the removal of instrument response, de-meaning, de-trending, bandpass filtering, time-domain
normalization, and frequency domain normalization. These steps encompassing single station datapreparation are aimed at reducing the effect of earthquakes and instrument irregularities. Amongst thetime domain normalization techniques, the running-absolute-mean normalization (Bensen et al., 2007)
was selected since the technique is expected to reduce the effects from regional/far-field earthquakes
and some former studies showed the cross-correlation functions with higher signal-to-noise ratios(SNRs). The running-absolute-mean-normalization is described by the formulâ𝜔̂𝑛 = 2𝑁+1 ∑𝑛+𝑁
𝑗=𝑛−𝑁|𝑑𝑗 |1(1)Where 𝜔̂𝑛 is the temporal weight for a bandpass filtered seismogram, (2𝑁 + 1) the width of thenormalization window and 𝑑̂𝑗 the seismogram bandpass filtered in the earthquake band. Besides
normalizing in the time domain, the spectral amplitudes of ambient noise is not flat in the frequency
domain, so that we also need to perform frequency-domain normalization. The ambient noise spectrum
peaks were seen at two frequency bands, which are the result of storms and interaction of ocean waves(the primary and secondary microseisms). The frequency domain normalization acts to reduce the effectof these microseisms. The results of both time and frequency domain normalizations at seismic station
ABPP are shown in Figure 1(a) and (b).
Next we conducted cross-correlation analyses and stackings to extract approximate Green’s
functions between two stations. To speed up the process, the cross-correlation is performed in thefrequency domain before returning the signal to the time domain. Also, to improve efficiency ofcomputation (Lin et al., 2008), the cross-correlograms of the vertical (Z), north-south (N) and east-west(E) components are rotated to transverse (T) and radial (R) components. Since amplitudes of seismic
ambient noise in the vertical wavefield is dominant in comparison to the horizontal components, the2usage of vertical component gives better results when estimating Rayleigh waves. We conducted further
isolation of Rayleigh waves by using the method described by Takagi et al., (2014). In considering P
wave contamination in the derived cross-correlation functions we eliminated the body-wave
contribution by subtracting ZR component from RZ component (here we call it ZRRZ component).
Figure 2 shows the difference between ZZ and ZRRZ components. The waveforms in the ZRRZ
component show noiseless traces in the middle indicating successful removal of P-waves.

a)b)

Figure 1. Examples of (a) running absolute mean normalization and (b)spectral whitening or frequency domain normalization at station ABPP. The
effect from an earthquake (Ms 4.4, epicenter, on UTC March 17, 2019) was
successfully removed.
A substantial assumption at this stage is the equivalence between cross-correlation function andGreen’s function. This equivalence has been proven theoretically in normal-mode summation, plane
wave and representation theorem (Weaver and Lobkis, 2001). This assumption stands in the absence of
weak scattering, high attenuation and heterogeneous noise source distribution. Heuristically, the Green’sfunction may be retrieved from cross-correlations given that the azimuth from which the noise sources
originate are in the end-fire lobe directions at 0° and 180° with respect to a particular station pair. The
definition for Green’s function used in this study is based on Lin et al. (2008) which states that Green’s
function is equivalent to the negative time derivative of the symmetric cross-correlation function asshown in Equation (2).
𝐺𝐴𝐵 (𝑡) = −𝑑 𝐶𝐴𝐵 (𝑡)+𝐶𝐴𝐵 (−𝑡)[]𝑑𝑡
20 ≤𝑡 < ∞(2)Estimated Green’s function is now used to obtain dispersion measurements. Following
Bracewell (1978), we define an analytic signal which is then used to obtain Equation (3)3𝑠𝑎(𝑡) = 𝐴(𝑡)exp(𝑖Ø(𝑡))(3)Where 𝑠𝑎(𝑡) is the analytical signal, 𝐴(𝑡) is the envelope function and Ø(𝑡) is the phase function.
Dispersion measurements are conducted using 𝐴(𝑡), which gives the group velocity, and Ø(𝑡), which
gives the phase velocity. Here we used a program package provided by the University of Colorado
Boulder (aftan1.1; http://ciei.colorado.edu/Products/) for automatic frequency-time analysis (AFTAN)of the cross-correlation functions. The AFTAN conducts both the conventional frequency-time analysis(FTAN; Levshin et al., 1972) and match filtered FTAN (Bensen et al. 2007) analyses, so we adopt theresults from the match filtered FTAN analysis.

Figure 2. Removal of P wave contamination still visible in ZZ component
cross-correlogram (left) in comparison with ZRRZ component crosscorrelogram (right).
Station performance and SNR are important parameters for quality control. We found that SNR
decrease with interstation distance and increase with stack number. Stations with erroneous PSD plots
and showing no clear cross-correlation result were removed from consideration.
4.RESULTS AND DISCUSSION
To assess the feasibility of seismic interferometric applications, we looked into whether Rayleigh orLove waves would result in high signal-to-noise ratio. In comparing cross-correlation functions in theZRRZ (for Rayleigh waves) and TT (for Love waves) components, it is discovered that there are morestation pairs with high signal-to-noise ratio for Rayleigh than Love waves. Comparing the crosscorrelation functions and single-station power spectral density (PSD) plots revealed that asymmetriccross correlograms were obtained when the PSD levels of the two stations are similar to each other. Thiscan be an indication of the existence of very local noise sources (Lin et al., 2006) at certain stations,which cause the higher levels of PSD plot levels and yield asymmetric cross correlograms. A starkdifference can be seen in the SNR between station pairs from which dispersion curves could be obtained
as compared to those with no dispersion measurements.
After rigorous data processing and SNR-based quality evaluations, dispersion measurements
were finally obtained with a final consideration being that the wavelengths need to be less than one-thirdof the inter-station distance (Bensen et al., 2007). Figure 4 shows the derived dispersion curves in theZRRZ component for 353 station pairs. Comparing station pairs that have the similar propagation pathshow similar dispersion curves, demonstrating little variation in directional dependency with change inazimuth (Figure 5). We also found the regional pattern of the derived dispersion curves, possibly
indicating the difference of the crustal thickness (Figure 6).

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Figure 3. Comparison of SNR for station pairs with dispersion curves (left)
and without (right).

Figure 4. Dispersion curves for phase velocities (red) and group velocities(blue).

a)b)

Figure 5. Station pairsdemonstrate that directional
dependency of ambient noise issmall with change in azimuth.
The map (a) shows station
location and path while theplots (b) and (c) are dispersion
measurements for phase andgroup velocity.

c)

5d)

e)

Figure 6. Station pairs parallel to eachother show a possible region of thincrust. The map (d) show station
location and path while the plots (e)and (f) are dispersion measurementsfor phase and group velocity.

f)

5.CONCLUSION
Conventional ambient noise data processing techniques effectively overcame challenges for data gaps,
contamination by earthquake signals and microseism peaks in the spectral domain. After resolving theseproblems dispersion measurements could be obtained for the Philippine islands. Although it must be said
that ambient noise interferometry requires an increase in both the quality and quantity of data.
6.RECOMMENDATION
Increased period of observation increases signal-to-noise ratio and time jumps less than 25 s results in
the inclusion of more data. The dispersion measurements can therefore significantly improve with alonger period of observation data and less intermittent signal. With these improvements the emergenceof Love wave dispersion measurements may also increase in feasibility.
ACKNOWLEDGEMENTS
This research was conducted during the individual study period of the training course “Seismology,Earthquake Engineering and Tsunami Disaster Mitigation” by the Building Research Institute, JICA,and GRIPS. With deepest gratitude, I would like to thank my supervisor Dr. Takumi HAYASHIDAand adviser Dr. Tatsuhiko HARA for their invaluable insights, unwavering support and paramountguidance. In extending this gratitude I would like to thank JICA, IISEE and GRIPS for a course thathas helped me and many others in enhancing our skill and knowledge in disaster management.
REFERENCES
Bensen, GD & Ritzwoller, Michael & Barmin, MP & Levshin, Anatoli & Lin, Feifan & Moschetti,
Morgan & Shapiro, Nikolai & Yang, Yingji (2007). Geophysical Journal International. 169. 12391260. 10.1111/j.1365-246X.2007.03374.x.
Hayashida, T., Yoshimi, M., & Horikawa, H. (2014). 66, 127-145.
Levshin, Anatoli & Pisarenko, V.F. & Pogrebinsky, G.A. (1972). 28. 211-218.
Lin F., Morgan P. Moschetti, Michael H. Ritzwoller, Geophysical Journal International, Volume 173,Issue 1, April 2008, Pages 281–298, https://doi.org/10.1111/j.1365-246X.2008.03720.x
Takagi, R., H. Nakahara, T. Kono, and T. Okada (2014), J. Geophys. Res. Solid Earth, 119, 2005–2018,
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