Global scale analysis and modelling of primary microseisms
Primary microseism is the less studied seismic background vibration of the Earth. Evidence points to sources caused by ocean gravity waves coupling with the seafloor topography. As a result, these sources should be in water depth smaller than the wavelength of ocean waves. Using a state-of-the-art o...
Published in: | Geophysical Journal International |
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Main Authors: | , , , , |
Other Authors: | , , , , |
Format: | Article in Journal/Newspaper |
Language: | unknown |
Published: |
Oxford University Press (OUP)
2019
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Subjects: | |
Online Access: | http://hdl.handle.net/10754/678863 https://doi.org/10.1093/gji/ggz161 |
Summary: | Primary microseism is the less studied seismic background vibration of the Earth. Evidence points to sources caused by ocean gravity waves coupling with the seafloor topography. As a result, these sources should be in water depth smaller than the wavelength of ocean waves. Using a state-of-the-art ocean wave model, we carry out the first global-scale seismic modelling of the vertical-component power spectral density of primary microseisms. Our modelling allows us to infer that the observed weak seasonality of primary microseisms in the southern hemisphere corresponds to a weak local seasonality of the sources. Moreover, a systematic analysis of the source regions that mostly contribute to each station reveals that stations on both the east and west sides of the North Atlantic Ocean are sensitive to frequencydependent source regions. At low frequency (i.e. 0.05 Hz), the dominant source regions can be located thousands of kilometres away from the stations. This observation suggests that identifying the source regions of primary microseisms at the closest coasts can be misleading. We thank the GEOSCOPE Observatory and the Incorporated Research Institutions for Seismology (IRIS) for providing openly available seismic data. The output of the ocean wave model can be found at ftp://ftp.ifremer.fr/ifremer/ww3/HINDCAST. LG acknowledges support from Princeton University and King Abdullah University of Science and Technology. ES and FA acknowledge support through the ANR Project MIMOSA under Grant ANR-14-CE01-0012. CJ |
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