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Large-scale mapping of the thickness of the crust from satellite gravity and gravity gradient data


Project Description

Geophysical methods offer a window into the deep Earth through indirect observations, such as disturbances in the Earth’s gravity field. Determining the inner structure of the Earth from geophysical observations (known as geophysical inversion) is challenging because there may not be a unique solution and results are sensitive to implicit assumptions made during data processing. This is particularly true of gravity data. Nonetheless, gravity disturbances still offer unparalleled spatial coverage for investigating the structure and thickness of the crust (e.g., Reguzzoni et al., 2013; Ebbing et al., 2018). To tackle the non-uniqueness issues, gravity inversions require prior information from other geophysical methods, such as crustal thickness estimates and regional crustal models from seismology (Uieda and Barbosa, 2017). Another major challenge is the quantification of uncertainty in crustal thickness estimates from gravity data. The dominant source of uncertainty comes not from noise in the data, but from effects that were not modelled during data processing or in the prior information. For example, gaps in our knowledge of the thermal structure of the lithosphere (Chappell and Kusznir, 2008) and the density of sedimentary basins (Uieda and Barbosa, 2017) can play a large role in biasing the crustal thickness estimates.

Project Summary:
The goal of this project is to develop improved inversion methods to determine crustal thickness from gravity and gravity gradient data, in particular Uieda and Barbosa (2017). Main objectives include: (1) accounting for density variation in the oceanic lithosphere due to temperature; (2) incorporate seismological estimates of crustal thickness in the inversion process; (3) estimate the density contrast across the crust-mantle interface in different domains; (4) joint inversion of gravity and gravity gradient data; (5) develop techniques to reduce the computational load of the inversion; (6) quantify uncertainty due to errors in regional crustal and sedimentary basin models. The inversion methods developed in this project can be applied to produce improved crustal thickness estimates for South America, Africa, Antarctica, the Moon, Mars, etc.

The appointed student will acquire the mathematical and programming skills required to undertake the project. They will be trained to develop software in a collaborative environment using GitHub and use the current best practices in software engineering. The project will be conducted following the current established norms of reproducible research, with all outputs published on the group’s GitHub page (github.com/compgeolab). The project will also involve code contributions to the different open-source Python software developed by the research group, mainly Fatiando a Terra (www.fatiando.org), leading to potential impact beyond standard scientific publications. This position would suit someone with mathematical and numerical methods skills (or who is willing to learn). Some experience with computer programming in any language is desirable. The appointed student will participate in online group discussions and peer-to-peer learning.

To apply for this opportunity, please visit: https://www.liverpool.ac.uk/study/postgraduate-research/how-to-apply/ and click the ’Apply online’ button.

Funding Notes

Full funding (fees, stipend, research support budget) is provided by the University of Liverpool for 3.5 years for UK or EU citizens. Formal training is offered through partnership between the Universities of Liverpool and Manchester. Our training programme will provide all PhD students with an opportunity to collaborate with an academic or non-academic partner and participate in placements.

References

Chappell, A. R., & Kusznir, N. J. (2008). Three-dimensional gravity inversion for Moho depth at rifted continental margins incorporating a lithosphere thermal gravity anomaly correction. Geophysical Journal International, 174(1), 1–13. doi:10.1111/j.1365-246X.2008.03803.x
Ebbing, J., Haas, P., Ferraccioli, F., Pappa, F., Szwillus, W., & Bouman, J. (2018). Earth tectonics as seen by GOCE - Enhanced satellite gravity gradient imaging. Scientific Reports, 8(1). doi:10.1038/s41598-018-34733-9
Reguzzoni, M., Sampietro, D., & Sanso, F. (2013). Global Moho from the combination of the CRUST2.0 model and GOCE data. Geophysical Journal International. doi:10.1093/gji/ggt247

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