Cascade.ANT.Gao-Shen.2014, Gao and Shen (2014), is based on a full-wave ambient noise tomographic method and the analysis of Rayleigh waves from ~1000 stations between 1995 to 2012, including the EarthScope USArray Transportable Array and many other permanent and flexible arrays.
|Title||3D shear-wave velocity model of the Cascades from full-wave ambient noise tomography|
|Type||3-D Tomography Earth Model|
|Sub Type||Shear-wave velocity (km/s)|
|Short Description||The model is based on a full-wave ambient noise tomographic method and the analysis of Rayleigh waves from ~1000 stations between 1995 to 2012, including the EarthScope USArray Transportable Array and many other permanent and flexible arrays.|
|Department of Geosciences|
|University of Massachusetts Amherst|
|Amherst, MA 01003, USA|
|Graduate School of Oceanography|
|University of Rhode Island|
|Narragansett, RI 02882, USA|
|CASCADE.ANT.GAO-SHEN.2014_kmps.nc (metadata ), is the netCDF binary for the above model expressed as shear velocity in km/s|
|Depth Coverage||0 to 200 km, (best resolution 20-120 km)|
|Areal Coverage||The Cascades (latitude: 36°/52°, longitude: 230°/254°)|
|Data Set Description||[Gao and Shen (2014)] The dataset includes multi-frequency Rayleigh-wave phase delay times between the observed and synthetic waveforms from ~1000 stations.|
|Supplemental Information||The supplemental information page for this model contains information on model resolution.|
- Gao, H., and Y. Shen (2014), Upper mantle structure of the Cascades from full-wave ambient noise tomography: Evidence for 3D mantle upwelling in the back-arc, Earth Planet. Sci. Lett., 309, 222-233, doi:10.1016/j.epsl.2014.01.012.
- Gao, H., and Y. Shen (2012), Validation of Shear-wave velocity models of the Pacific Northwest, Bull. Seism. Soc. Am., 102(6), 2611-2621, doi:10.1785/0120110336.
- Trabant, C., A. R. Hutko, M. Bahavar, R. Karstens, T. Ahern and R. Aster (2012), Data products at the IRIS DMC: stepping-stones for research and other application, Seismological Research Letters, 83(6), 846:854. doi: 10.1785/0220 120032
- H. Gao and Y. Shen