The interior of the Earth holds the record of the evolution of the planet and the imprint of ongoing processes. Lithospheric material is transported into the deep interior through subduction and then distributed throughout the mantle by convection. Core and mantle exchange heat and material, leading to partial melting and compositional heterogeneity in the lowermost mantle and uppermost core. Continent-sized structures (Large Low Velocity Provinces) have been mapped in two near-antipodal locations along the equator and have been shown change location and shape in response to mantle convection mainly controlled by subduction linking the surface of the Earth to the deep mantle. In turn Large Low Velocity Provinces potentially control the location of intraplate volcanism and flood basalts, and influence the dynamics of the core creating the Earth’s magnetic field. Although we have learned about many of the structure in the deep Earth by the analysis of global seismic data, the origin of these strucures and the dynamics and evolutions of the interior of the planet remain unclear. E.g. recent studies relate LLVP to the impact of Theia during the moon forming impact early in Earth's history (Yuan et al., 2023). Better imaging and understanding of these structures, their origin, composition, and evolution are essential to understand our planet’s evolution to its current habitability.
Seismology has undergone a dramatic change in the last couple of years due to a rapid growth in the data from seismometers available for analysis. Due to advances in data availability and processing techniques, global seismology has entered the era of big data science. This project will use the huge amount of seismic data collected in the last decades together with machine learning approaches to better resolve some of the structures in the lowermost mantle focussing on small scale structures below the resolution of global tomography with an aim to produce global maps of mantle heterogeneity structure.
Seismic waves emitted by earthquakes and recorded by seismometers at Earth’s surface are one of the few probes available to image the Earth’s deep interior. Using the detailed information contained in these data allows mapping out the structures in the deep Earth. Mapping the Earth’s interior has been hampered by the uneven distribution of seismic stations and earthquake sources. The large increase of station deployments in the last decades and the related increase in available seismic data now opens an opportunity to move away from the analysis of seismic data in regional studies to using the available massive seismic datasets to gain global insight into the structure of the Earth’s deep mantle. To handle these large datasets modern machine learning approaches are necessary and will be a core component of this project.
This project aims to gain better insight into the distribution and structure of small-scale structures (with scalelengths of tens of km) in the deep Earth. We will use seismic timeseries datasets stored at international data centres and will target specific areas of the seismograms most sensitive to the structure of the deep Earth. Machine learning will be employed for data quality control, signal characterization (amplitude and waveform) and zonation. Using a combination of different probes for deep Earth structure we will be able to resolve a host of structure and infer their dynamical links, shared origins, and composition.
This project is suitable for a student interested in the application of modern processing techniques to studies of the Earth’s deep interior. A background in physics, geophysics, quantitative geology or environmental science, mathematics or computer science is suitable for this project.
The successful candidate will join the Institute of Geophysics and Tectonics in the School of Earth and Environment. The Deep Earth Research Group in Leeds is one of the largest grouping of researchers focussing on deep Earth structure and dynamics with expertise in seismology, mineralphysics, core dynamics and geomagnetism. The School of Earth and Environment hosts a vibrant and large group of PhD students with more than 200 PhD students working in a multi-disciplinary environment.
This project is part of the NERC funded Panorama DTP competition. For more information please see the Panorama resources.