日本地震学会2023年度秋季大会

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2023年10月31日(火) 17:00 〜 18:30 P11会場 (F203) (アネックスホール)

[S08P-11] Ocean microseisms recorded by Cuban seismic stations: time variations and spectral features

*Viana POVEDA BROSSARD1, Kiwamu NISHIDA2, Bogdan ENESCU1 (1. Department of Geophysics, Graduate School of Science, Kyoto University, 2. Earthquake Research Institute, University of Tokyo)

The early efforts to study the seismic wavefield generated by the ocean date back to the middle of the 20th century (Longuest-Higgins, 1950; Hesselmann, 1963). The development of dense seismic observation has gradually allowed scientists to analyze ocean microseisms in detail, and the comparison with the ocean-wave action model reveals their source process (e.g., Tanimoto et al., 2006; Ardhuin et al., 2011; Nakata et al., 2019; Li et al., 2022). Although the ambient seismic noise generated by the ocean waves occurs in a relatively wide period range, from about 3 to 300 s (Nishida, 2017), strong double-peaked signals dominate the noise spectrum with periods from 3 to 20 s (Figure 1). The most prominent peak, at 3 to 10 s, is attributed to the so-called secondary microseisms (Figure 1) caused by the pressure field due to ocean-waves interactions. From 10 to 20 s, the less energetic peak, known as primary microseisms, originates from the linear coupling between ocean waves and seafloor topography in shallow water (Figure 1). Microseisms’ amplitude and frequency are affected by the proximity to the coast of the recording seismic station and by seasonal variations.

This work analyzed spectral characteristics of marine microseisms using broadband seismic records at 13 seismic stations in Cuba. First, we estimated the power spectra of the vertical component in 2020 by the Welch periodogram method (Welch, 1967) to data windows of 3600 s with a 50 % overlap. The method reduces the variance without smoothing or binning the spectral estimates for an appropriate spectral resolution (e.g., Anthony et al., 2020). Since the occurrence of teleseismic events may bias the spectral estimations, we eliminated the teleseismic signal of earthquakes with magnitudes larger than or equal to 5.5 by removing the two-hour data after such events. We also removed the mean and linear trends of waveforms and corrected them for the instrument responses. We next analyzed the mean power spectral density behavior in space, time, and frequency for the primary and secondary microseism peaks.

Our results indicate that the dominant period of the secondary microseisms at almost all stations is between 3 to 3.8 s, with slight variations from station to station. The primary microseisms peak is found at around 15.6 s for the majority of stations. The power spectral density ranges from -121 to -127 dB and -150 to -155 dB for the secondary and primary microseisms, respectively. We also analyzed the energy variation of microseisms in the two seasons of Cuba (i.e., dry and wet). The strongest signals at the main period of the secondary and primary microseism are found during the dry season. These seasonal variations are relatively weak, which is consistent with findings by Stutzmann et al. (2009) that report stable and relatively small temporal variations of the microseisms’ energy at stations located near the equator. We are currently investigating why relatively stronger signals are observed during the dry season. Meteorological events like tropical cyclones increase the intensity of microseisms on the days when these events’ path is close to Cuba.

We also studied how the maximum power spectral density changes with the geographical distribution of stations. In the dry season for stations in the northern part of Cuba, located closer to the Atlantic Ocean, these values are stronger than for stations close to the southern Caribbean coast, although variations from station to station are less than 6 dB. In the wet season, differences in power spectral density between stations seem to be smaller than during the dry season and independent of location.

The location of marine microseisms sources changed over time. To better understand microseisms’ characteristics, in particular their space-time migration, we are currently applying polarization analysis of the particle motion of Rayleigh and P waves (Takagi et al., 2018).