The method of choice for imaging the detailed 3D structure of the Earth is global seismic tomography. To date, the focus of global seismic tomography has been to image lateral variations in seismic velocities throughout the mantle and to infer their cause and implications for the evolution of the Earth. The main focus of this grant is to work towards an integrated model of anisotropy of the whole mantle. Anisotropy is the directional dependence of seismic wave speed caused by alignment of anisotropic crystals or the development of texture in a flow field. Thus, anisotropy is at least as important as heterogeneity from a dynamical point of view as it gives us a glimpse of flow and deformation in the deep Earth.

Resolving anisotropy throughout the mantle is a complex task as anisotropy takes fundamentally different forms in different parts of the mantle. Thus, inversions that assume simple forms of anisotropy throughout the mantle are likely doomed to failure. Here, we follow a strategy that allows us to integrate many different data types to generate a model of anisotropy that can explain the bulk of the observations. Since we are also interested in developing a global view of anisotropy, we are also increasing the quantity and quality of splitting observations by adapting and applying some recent observational techniques to large databases of (several million) seismograms which include globally distributed recordings as well as regional arrays. The main goal of this work is a first truly global look at anisotropy throughout the mantle.

Results of this research will be widely disseminated to the research community through a web site and anonymous ftp. This project will create detailed global images of mantle structure and anisotropy leading to an improved understanding of the history and tectonics of our planet. Finally, this grant supports graduate student education at SIO.

Agency
National Science Foundation (NSF)
Institute
Division of Earth Sciences (EAR)
Application #
0538238
Program Officer
Raffaella Montelli
Project Start
Project End
Budget Start
2006-03-15
Budget End
2011-02-28
Support Year
Fiscal Year
2005
Total Cost
$380,001
Indirect Cost
Name
University of California-San Diego Scripps Inst of Oceanography
Department
Type
DUNS #
City
La Jolla
State
CA
Country
United States
Zip Code
92093