Supervisors: Nicholas Rawlinson and Keith Priestley (Earth Sciences)
Importance of the area of research:
An improved understanding of the structure and composition of Earth's lithosphere is crucial to the advancement of modern Earth sciences in a variety of areas, including plate tectonics, continental formation and evolution, volcanism, mineral and hydrocarbon resources and earthquake hazard, to name but a few. Thanks to rapid increases in computing power, the emergence of large and freely available seismic datasets, and improvements in geophysical inversion methods over the last few decades, the Earth's lithosphere has been imaged in unprecedented detail. However, many challenges still remain, including irregular data sampling, non-linearity of the inverse problem and quantifying model uncertainty, all of which place limitations on the usefulness of the results produced so far. Advanced imaging techniques that are designed to tackle these issues and integrate multiple datasets are therefore required to produce the next generation of lithospheric model.
The aim of the project is to take the next step in global imaging of the crust and upper mantle by applying Bayesian transdimensional tomography to multiple seismic datasets, including surface wave, ambient noise and receiver functions. Transdimensional tomography is inherently multi-scale and data driven, as the number of unknowns is itself an unknown, and structure is represented by a parameterization which adapts to the resolving power of the dataset during the inversion, which is based on fully non-linear sampling. The new global lithosphere models are expected to greatly enhance our understanding of Earth structure, composition and dynamic processes.
What the student will do:
The student will work with the two supervisors to extend pre-existing transdimensional tomography methods to allow for global 3-D applications involving the joint inversion of teleseismic surface wave, ambient noise and receiver function datasets. One of the weaknesses of traditional inversion methods is that it is very difficult to quantify data uncertainty, yet it plays a critical role in determining whether models satisfy the data, and hence are acceptable or not. In an attempt to address this issue with multiple classes of seismic datasets, a hierarchical version of the transdimensional algorithm, which allows the level of data noise to be an unknown in the inversion, will be implemented. The student will also be involved in the collection of global seismic datasets from various sources and where necessary, the extraction of key observables such as surface wave dispersion.
Bodin, T., Sambridge, M., Tkalcic, H., Arroucau, P., Gallagher, K. & Rawlinson, N., Transdimensional inversion of receiver functions and surface wave dispersion. Journal of Geophysical Research 117 (B2), B02301 (2012).
Bodin, T., Sambridge, M., Rawlinson, N. & Arroucau, P., Transdimensional tomography with unknown data noise. Geophysical Journal International 189 (3), 1536-1556 (2012).
Priestley, K. and McKenzie, D., The relationship between shear wave velocity, temperature, attenuation and viscosity in the shallow part of the mantle. Earth and Planetary Science Letters, 381, 78-91 (2013)
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