**3. Seasonal variability of DF microseism and its association with sea-ice concentration**

We investigated spectral characteristics for KSJ1 through the estimation of PSDs and PDFs of seismicbackgroundnoise(Fig.2),andfoundthattheprimaryandsecondarymicroseismsappear distinctly on the plot. Comparing to the HNM and LNM, we may evaluate that KSJ1 has been well operatedin terms of system performance except slightly noisier(or might be higher energy level of DF microseisms) than the HNM around 10 s. In general, a plot of PDFs could provide helpful information on spectral signature of a station; however, temporal patterns of the microseisms are barely identifiable from it. In an attempt to examine temporal variation of the noise levelofKSJ1,weobtainthe statisticalmode forthe correspondingperiods fromdailyPSDs so that we could construct a power spectrum with respect to time (Fig. 3). Empty spaces in the figure show data missing due to most likely system malfunctioning. There is nothing noticea‐ bleintheperiodoflongerthan10s throughouttheoperationtime.Havinginterests inthefeature near 4-10 s inperiod,i.e.DFmicroseisms,ithappens thattheDFenergycomes tobeweakerfrom July through September (austral winter). The behavior is apparent in 2007, 2009, and 2011, whereas it becomes ephemeral in 2006, 2008, and 2010, but rather weaker in power compared to other seasons ina year.This observationcontrasts withthe seasonal variability of seismicnoises in the northern hemisphere; for instance, the amplitude of the Earth's hum reaches its seasonal maximum in winter season [17,18] revealed from an array analysis. The power of DF microse‐ isms in the northern hemisphere shows a similar pattern (e.g. [19]) as the Earth's hum. Most literatures suggest that these characteristics are attributed to seasonal variation of the intensi‐ ty of infragravity wave depending on swell amplitudes.

**Figure 3.** Spectral amplitude variation in seismic noise for BHZ (broadband vertical component) during the period of 2006-2011. Note that the seismic energy at the frequency range of DF microseisms (4-8 s) becomes weaker during July to September annually, which is a different behavior from that of the northern hemisphere except the Arctic region. Empty spaces in the plot indicate data missing or a period of malfunctioning.

several earthquakes occurred near the station during the operation period, they do not affect

**Figure 2.** A Probability Density Function (PDF) plot of BHZ for KSJ1 during 2006-2011. Two predominant peaks appear around 5 and 10 s in period, corresponding to secondary (or DF) and primary microseisms, respectively. HNM and LNM indicate (gray curves) the standard high and low noise model (Peterson, 1993), respectively. The most probable energy

**3. Seasonal variability of DF microseism and its association with sea-ice**

We investigated spectral characteristics for KSJ1 through the estimation of PSDs and PDFs of seismicbackgroundnoise(Fig.2),andfoundthattheprimaryandsecondarymicroseismsappear distinctly on the plot. Comparing to the HNM and LNM, we may evaluate that KSJ1 has been well operatedin terms of system performance except slightly noisier(or might be higher energy level of DF microseisms) than the HNM around 10 s. In general, a plot of PDFs could provide helpful information on spectral signature of a station; however, temporal patterns of the microseisms are barely identifiable from it. In an attempt to examine temporal variation of the noise levelofKSJ1,weobtainthe statisticalmode forthe correspondingperiods fromdailyPSDs so that we could construct a power spectrum with respect to time (Fig. 3). Empty spaces in the

the overall PDFs as we mentioned earlier.

with respect to frequency is presented by a dashed curve (mode).

150 Engineering Seismology, Geotechnical and Structural Earthquake Engineering

**concentration**

Ringdal and Bungum [20] reported a pure sinusoidal pattern in long period noise level, i.e. seasonal maximum in winter and minimum in summer, from a spectral analysis of NORSAR data for three years. It does not, however, necessarily occur in the Polar Regions, especially Antarctica, and might be due to a regional difference between the northern and southern hemispheres. More specifically, [15] similarly observed weaker energy of DF microseisms in austral winter atthe stationDRV,Antarctica, andexplainedthatthe acoustic energy fromocean swell tends to be severely attenuated by sea ice and reflecting waves along the coast suffers as well causing fewer DF microseisms generated by sources. We refer to it as 'sea-ice damping effect' in this study. Recently, numerical modeling approach to figure out this phenomena has been made by Tsai and McNamara [13], which shows that 75-90 % of the variability in microse‐ ism power in the Bering Sea can be predicted using a simple model of microseism damping by sea ice. Moreover, they argued that we could use the microseism as a good indicator to moni‐ tor the strength of sea ice that is not easily measured by through other means.

on ice, moving speed of glaciers, ice mass balance by means of AMSR-E, InSAR satellites, and GRACE, respectively. Even though all these methods give us great opportunities to monitor dramatic changes in the cryospheric environment, we should still conduct in-situ measure‐ ments to obtain physical and mechanical properties of ice such as stiffness. From the recent development on theoretical approach to predict the variability in DF microseisms [13] and the obvious evidence linking the variation of the DF energy to the SIC in this study, we anticipate that a long-term observation of the DF microseisms could be a good tool to monitor local climate change in Polar Regions. Most of literatures in seismology have dealt with several major issues such as determination of velocity and attenuation structures in the Earth, precise locating techniques, and investigation of earthquake sources. In this study, beyond that horizon, we find out that a seismological method could play a key role in understanding

Seismic Ambient Noise and Its Applicability to Monitor Cryospheric Environment

http://dx.doi.org/10.5772/55670

153

Substantial advances in seismograph technology allow us to consistently observe the Earth's continuous oscillation everywhere in the world. The DF microseism has been known that it is excited by ocean waves, thus it is likely to show seasonal variations [11]. Examining the ambient seismic noise level at KSJ1 during the period of from 2006-2011, we found a distinct seasonal pattern in the period of 4-10 s; the DF energy comes to be weaker from July through September (austral winter) in every year. Cross correlation results tell us that as the SIC becomes higher, the DF power decreases, and confirm that the DF energy responses immedi‐ ately to the sea-ice condition. Consequently, we propose that a long-term observation of the DF microseisms should be necessary to monitor local climate change in Polar Regions, which

This research has been supported by KOPRI research grants PN12040 (CATER 2012-8080) and

and Sinae Han1,2

physical interaction between climate change and the cryosphere.

contributes extra benefits to the satellite remote sensing.

**4. Conclusions**

**Acknowledgements**

PE13050.

**Author details**

, Joohan Lee1

\*Address all correspondence to: wonsang@kopri.re.kr

1 Korea Polar Research Institute, Republic of Korea

2 Kangwon National University, Republic of Korea

Won Sang Lee1

In order to carefully study the direct relation between the energy of DF microseisms and the sea ice condition, we extract and integrate the DF power ranging 4-10 s in period out of the power spectrum and collect Sea-Ice Concentration (SIC) information. To create time series of SIC, we used data based on brightness temperature observations at 89 GHz obtained from the AMSR-E (Advanced Microwave Scanning Radiometer for EOS Aqua) on board NASA's Aqua satellite. The brightness of each image pixel is converted to the SIC using the ASI (ARTIST Sea Ice) algorithm [21]. The data offer SICs on a grid with 6.25 km resolution with a complete daily coverage of the Polar Regions. A domain for calculating SIC covers a part of the Drake Passage allowing us to compare to the DF energy in this study. The calculated percentage of SIC is the percentage of grid cells containing more than 15 % sea ice. This is mainly attributed to the fact that the accuracy of the SIC is ±15 % in regions of first-year ice [22].

As shown in Figure 4, there is clear seasonal variation found in both the power of DF micro‐ seisms (red curve) and the SIC (black curve) from 2006-2011. To quantify how they are closely related, we apply cross correlation that is a standard method of estimating the degree to which two series are correlated. The bin size of each time series is chosen to be 1-day. The resultant cross-correlation coefficient is given by -0.70 that is a strong negative correlation. The result implies that as the SIC becomes higher, i.e. more sea-ice in the ocean, the DF power decreases, which is coincident with the hypothesis of 'sea-ice damping effect'. We also determined the lag time as almost zero from the cross correlation, which indicates that the DF energy responses immediately to the sea-ice condition nearby. When one may take a closer look at the period of May through September in a year, it becomes more prominent.

**Figure 4.** A comparative plot of Sea Ice Concentration (SIC, black curve) sampled near the KGI toward the Drake Pas‐ sage vs. seismic energy of DF microseisms (red curve) observed at KSJ1 during 2006-2011. When either the SIC increas‐ es or decreases, the DF power responses immediately (negatively correlated), suggesting the DF energy is a relevant seismic proxy to monitor cryospheric environment especially the sea ice condition nearby. The strong correlation (-0.70) between them supports the hypothesis.

Remote sensing using satellites allows us to extensively improve our knowledge over various scientific issues, especially in Polar Regions. For instance, we can measure surface melt extent on ice, moving speed of glaciers, ice mass balance by means of AMSR-E, InSAR satellites, and GRACE, respectively. Even though all these methods give us great opportunities to monitor dramatic changes in the cryospheric environment, we should still conduct in-situ measure‐ ments to obtain physical and mechanical properties of ice such as stiffness. From the recent development on theoretical approach to predict the variability in DF microseisms [13] and the obvious evidence linking the variation of the DF energy to the SIC in this study, we anticipate that a long-term observation of the DF microseisms could be a good tool to monitor local climate change in Polar Regions. Most of literatures in seismology have dealt with several major issues such as determination of velocity and attenuation structures in the Earth, precise locating techniques, and investigation of earthquake sources. In this study, beyond that horizon, we find out that a seismological method could play a key role in understanding physical interaction between climate change and the cryosphere.
