A year of microseisms in southern California

Gerstoft, Peter; Tanimoto, Toshiro

doi:10.1029/2007gl031091

Cited by 147 publications

(164 citation statements)

References 19 publications

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“…The large amplitude is generally explained by the non-linear interactions of opposing ocean waves, as proposed by Longuet-Higgins (1950), and expanded by Tanimoto (2007a) and Webb (2007). The new theory anticipates that secondary microseisms are more likely to be generated in shallow water near the coast, as observed by many authors (Haubrich and McCamy 1969;Bromirski and Duennebier 2002;Rhie and Romanowicz 2006;Tanimoto 2007b;Gerstoft and Tanimoto 2007).…”

Section: Introductionmentioning

confidence: 65%

Mutual Coupling Between Meteorological Parameters and Secondary Microseisms

Holub¹,

Kalenda²,

Rušajová³

2013

Terr. Atmos. Ocean. Sci.

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The basic scientific question of this study was: do other mechanisms exist for excitation of secondary microseisms aside from the widely accepted mechanism by non-linear interactions of respective ocean waves. Here we use continuous broadband data from secondary microseisms recorded at the Ostrava-Krásné Pole, Czech Republic (OKC) seismic station to create a massive seismological database. Except for seismological data, various meteorological features and their mutual relations were analysed: temperature, the so called "shifted" temperature, air density, changes of atmospheric pressure, and synoptic situations. These analyses prove that maximum amplitudes of microseisms were observed during winter, while minimum amplitudes occured in summer months. The annual variations of microseisms amplitudes could not be explained by annual variations of storm activity above the North Atlantic. In addition, current analyses also aim at quantitative and quantitative evaluation of synoptic situations for triggering individual microseismic anomalies. Some of the meteorological features, namely the distribution of low pressures above northern Europe and high-pressure areas in Central Europe make it easy to explain most of the microseismic extremes. Here we pay special attention to the influence of large earthquakes, which usually induce slow deformation waves. We conclude that at least three mechanisms of microseism generation are possible: (1) the function of atmospheric pressure at sea level in the North Atlantic, (2) the effects of spreading of thermoelastic waves in the rock mass and (3) deformation waves induced by large earthquakes.

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Section: Introductionmentioning

confidence: 65%

Mutual Coupling Between Meteorological Parameters and Secondary Microseisms

Holub¹,

Kalenda²,

Rušajová³

2013

Terr. Atmos. Ocean. Sci.

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“…Surface wave dispersion curves from active source seismic data have been used previously to investigate the firn structure by inverting for the shear (S)-wave velocity profile at, for example, Subglacial Lake Whillans, Antarctica (Picotti et al 2015) and the Italian Alps (Godio & Rege 2015). Here, we beamform the passive-source SP seismic array data to obtain the main propagation direction of the surface waves.…”

Section: I S P E R S I O N C U Rv E Smentioning

confidence: 99%

“…4). The source direction of the ambient noise signal was determined by beamforming (Gerstoft & Tanimoto 2007) of the vertical component of the SP array from 2-20 Hz. Beamforming was performed for 10 min data segments, for a slowness range of 0.1-1.2 s km −1 , and over azimuths from 0 • to 360 • .…”

Section: Beamforming To Estimate Source Azimuthmentioning

confidence: 99%

Ice shelf structure derived from dispersion curve analysis of ambient seismic noise, Ross Ice Shelf, Antarctica

et al. 2016

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S U M M A R YAn L-configured, three-component short period seismic array was deployed on the Ross Ice Shelf, Antarctica during November 2014. Polarization analysis of ambient noise data from these stations shows linearly polarized waves for frequency bands between 0.2 and 2 Hz. A spectral peak at about 1.6 Hz is interpreted as the resonance frequency of the water column and is used to estimate the water layer thickness below the ice shelf. The frequency band from 4 to 18 Hz is dominated by Rayleigh and Love waves propagating from the north that, based on daily temporal variations, we conclude were generated by field camp activity. Frequency-slowness plots were calculated using beamforming. Resulting Love and Rayleigh wave dispersion curves were inverted for the shear wave velocity profile within the firn and ice to ∼150 m depth. The derived density profile allows estimation of the pore close-off depth and the firn-air content thickness. Separate inversions of Rayleigh and Love wave dispersion curves give different shear wave velocity profiles within the firn. We attribute this difference to an effective anisotropy due to fine layering. The layered structure of firn, ice, water and the seafloor results in a characteristic dispersion curve below 7 Hz. Forward modelling the observed Rayleigh wave dispersion curves using representative firn, ice, water and sediment structures indicates that Rayleigh waves are observed when wavelengths are long enough to span the distance from the ice shelf surface to the seafloor. The forward modelling shows that analysis of seismic data from an ice shelf provides the possibility of resolving ice shelf thickness, water column thickness and the physical properties of the ice shelf and underlying seafloor using passive-source seismic data.

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“…Microseism metrics from global networks of long-running calibrated seismic stations are thus an integrative complement to other wave intensity monitors such as ocean buoys [Bromirski and Duennebier, 2002;Aster et al, 2008] and iceberg-or iceshelf-deployed seismometers [MacAyeal et al, 2006;Bromirski et al, 2010]. Because most microseism energy observed on land is coastally generated [Bromirski et al, 1999;Bromirski, 2001;Bromirski and Duennebier, 2002;Gerstoft and Tanimoto, 2007], extremes in microseism energy strongly correlate with extremes in near-shore wave energy [e.g., Aster et al, 2008].…”

Section: Introductionmentioning

confidence: 99%

Global trends in extremal microseism intensity

Aster

McNamara

Bromirski

2010

Geophysical Research Letters

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Globally ubiquitous seismic background noise peaks near 7 and 14 s period are generated via distinct mechanisms that transfer storm‐generated gravity wave energy to the seismic wave field. We utilize continuous digital ground motion data recorded by the Global Seismographic Network and precursor instrumentation to chronicle microseism power extreme events for 1972–2009. Because most land‐observed microseism surface‐wave energy is generated at or near coasts, microseism metrics are particularly relevant to assessing changes in coastal ocean wave energy. Extreme microseism winter storm season event counts reveal the widespread influence of the El Niño Southern Oscillation (ENSO). Individual station and ensemble slopes trend positive for this study period for Northern Hemisphere stations. The double‐frequency microseism is particularly volatile, suggesting that the weaker single‐frequency microseism directly generated by ocean swell at coasts is likely a more representative seismic proxy for broad‐scale ocean wave energy estimation.

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A year of microseisms in southern California

Cited by 147 publications

References 19 publications

Mutual Coupling Between Meteorological Parameters and Secondary Microseisms

Mutual Coupling Between Meteorological Parameters and Secondary Microseisms

Ice shelf structure derived from dispersion curve analysis of ambient seismic noise, Ross Ice Shelf, Antarctica

Global trends in extremal microseism intensity

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