Regional-scale structure and dynamics of the crust, upper mantle and transition zone from waveform tomography with massive datasets
APPROVED In the last decade, the number of seismic stations deployed globally increased dramatically, allowing to construct tomographic models with increasing resolution. Seismic data coverage however, is not homogeneous across the globe, and many regions are sampled by a distribution of ray paths t...
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| Format: | Doctoral or Postdoctoral Thesis |
| Language: | unknown |
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Trinity College Dublin. School of Natural Sciences. Discipline of Geology
2020
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| Online Access: | http://hdl.handle.net/2262/91290 https://tcdlocalportal.tcd.ie/pls/EnterApex/f?p=800:71:0::::P71_USERNAME:CELLIN |
| Summary: | APPROVED In the last decade, the number of seismic stations deployed globally increased dramatically, allowing to construct tomographic models with increasing resolution. Seismic data coverage however, is not homogeneous across the globe, and many regions are sampled by a distribution of ray paths that is both lower than average and uneven. In this work, we compute regional-scale tomographic models from massive, global waveform datasets, that we optimise for Africa, South America and the South- and North Atlantic Oceans, where coverage is highly heterogeneous. To maximise coverage in the study areas, we assemble a very large dataset of both regional and global teleseismic waveforms, retrieving all freely available data in the Atlantic Ocean and surrounding continents. We then invert the waveforms using the Automated Multimode Inversion (AMI) of S-, Multiple S- and Surface waves. AMI produces a set independent linear equations with uncorrelated uncertainties for each source-receiver path, describing the path-average P- and S-wave velocity structure and dispersion curves within approximate sensitivity kernels. We then combine all the equations in a linear system and solve it for the 3D distribution of P- and S-wave velocities and 2-psi azimuthal anisotropy in the crust, upper mantle and transition zone. Similarly, we combine all phase velocity dispersion curves and invert them to produce 2D phase velocity maps independently at different periods. Finally, we exploit the mutual consistency of our very large dataset to automatically identify and remove the least consistent measurements from both our 3D models and phase velocity maps. In order to obtain the best possible models, for each study area we compute a different model that is tuned to yield the best results in the region. In South America and the South Atlantic Ocean, we parametrise our 3D model SA2109 on a ~300 km triangular grid and fine tune its regularisation with the aid of spike tests to yield robust results across the area. In South America, we image ... |
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