Webinars - Detail

Modeling the Crust and Upper Mantle Under the Central and Western U.S. by Joint Inversion of Receiver Functions and Surface Waves
Dr. Weisen Shen, University of Colorado at Boulder

ABSTRACT

Rayleigh wave phase velocity maps from ambient noise and earthquake data are inverted jointly with receiver functions observed at ~1400 stations from the USArray Transportable Array west of 85°W longitude for data recorded in the years 2005 through 2012 to produce a 3-D model of shear wave speeds beneath the central and western US to a depth of 150 km. Eikonal tomography is applied to ambient noise data to produce over 300000 Rayleigh wave phase speed curves and Helmholtz tomography is applied to data following 2000 (Ms greater than 4.5) earthquakes so that Rayleigh wave dispersion maps are constructed from 8 sec to 80 sec period with associated uncertainties across the region. Harmonic stripping generates back-azimuth independent receiver functions with uncertainty estimates for each of the stations. A non-linear Bayesian Monte-Carlo method is used to estimate a distribution of Vs models beneath each station by jointly interpreting surface wave dispersion and receiver functions and their uncertainties. The assimilation of receiver functions improves the vertical resolution of the model by reducing the range of estimated Moho depths, improving the determination of the shear velocity jump across Moho, and improving the resolution of the depth of anomalies in the uppermost mantle. A great variety of geological and tectonic features are revealed in the 3-D model and call for more detailed local to regional scale analysis and interpretation. In particular, we will discuss the preliminary model of crustal and uppermost mantle structure beneath the Mid-Continental Rift.

METADATA

Last updated Key Points
2012-10-25
  • Rayleigh wave phase velocity maps from ambient noise and earthquake data are inverted jointly with receiver functions to produce a 3-D model of shear wave speeds
  • Various geological and tectonic features are revealed in the 3-D model and call for more detailed local to regional scale analysis and interpretation