Japan Geoscience Union Meeting 2021

Presentation information

[J] Oral

S (Solid Earth Sciences ) » S-SS Seismology

[S-SS09] Seismic wave propagation: Theory and Application

Sat. Jun 5, 2021 1:45 PM - 3:15 PM Ch.18 (Zoom Room 18)

convener:Kaoru Sawazaki(National Research Institute for Earth Science and Disaster Resilience), Kiwamu Nishida(Earthquake Research Institute, University of Tokyo), Takao Nibe(JAPEX), Kyosuke Okamoto(National Institute of Advanced Industrial Science and Technology), Chairperson:Kiwamu Nishida(Earthquake Research Institute, University of Tokyo), Takashi Hirose(National Research Institute for Earth Science and Disaster Resilience)

2:15 PM - 2:30 PM

[SSS09-13] Incorporating wind information in the inversion of co-located pressure and seismic data for shallow elastic structure

*Toshiro Tanimoto1 (1.Department of Earth Science, University of California, Santa Barbara, CA93106, USA)

Keywords:Shallow elastic structure, Inversion of pressure and seismic data, Low-frequency seismic noise excited by winds

When surface pressure is large, seismic noise between about 0.01 Hz and 0.05 Hz is mostly generated by wind-related surface pressure changes. By analyzing this phenomenon using co-located pressure and seismic data, we can derive shallow elastic structure in the uppermost 50-100 m.

However, there still remains a difficult question on seismic excitation by wind-related surface pressure because of complexity in wind behaviors. Roughly speaking, it consists of the mean shear flow part and the turbulent part. The mean flow part should act like a moving pressure source on the surface while the turbulent parts act like a stochastic source as the surface pressure field becomes heterogeneous.


We point out that this complexity may be reduced by selecting time intervals of stable wind directions. During such time intervals, pressure changes occur in the direction of winds, which justifies us to use a relatively simple, moving pressure source model. We quantify the stability of wind direction by the standard deviation of wind direction data.

For stations with co-located wind, pressure, and seismic data, we propose to select time intervals with stable wind directions first and apply our inversion method based on the moving pressure model. In the absence of wind data at a station with co-located pressure and seismic data, we propose to analyze the high-pressure end of data, as this selection partially satisfies the stable wind condition. Some representative results will be shown by using data from the EarthScope Transportable Array.