Quantifying strain distribution in en-echelon fault systems: Northern Volcanic Zone, NE Iceland

In geological settings that are prone to intense faulting, being able to identify regions of potential future stress accommodation could contribute to forward planning for future hazard mitigation. In magmatically driven rifting environments such as the Mid-Atlantic Ridge, the build-up of stress during lithospheric separation across the plate boundary is released during episodic rifting episodes, with pressurised magma moving to the surface via dyke processes (e.g. Buck et al., 2006, Sigmundsson et al., 2014, Wright et al, 2012). The emplacement of dykes results in the accommodation of stress by both extensional horizontal opening of fractures and vertical slip along the fracture plane (e.g. Tryggvason, 1986, Hjartardottir et al, 2016).

The subaerial environment in Iceland provides an excellent opportunity to measure the surface fractures in a rifting zone to assess the spectrum in behaviour of stress accommodation across both individual fissure swarms and the larger scale volcanic zones. Total horizontal opening measured across selected cross-sections in the northern Krafla fissure swarm has shown that, over the last 10,000 years, Krafla appears to have accommodated the majority of expected horizontal opening in the region (Bramham, 2014). This project will use currently available high resolution LiDAR DEM over the Krafla fissure swarm to further study horizontal extension along the length of the Krafla fissure swarm. The student will also use detailed mapping of fractures across the Northern Volcanic Zone (NVZ) (Hjartardottir et al., 2012, 2015) to plan and acquire high resolution photogrammetry swaths across the fissure swarms to measure horizontal extension across the larger scale and identify regions where there is a stress accommodation deficit.

Additionally the project will study and perform fracture prediction modelling of the smaller scale behaviour of segmented fault systems within the fissure swarms, specifically using fault data extracted from a high resolution DEM over Krafla assessing stress accommodation and identifying regions of deficit.

http://www.nercdtp.leeds.ac.uk/projects/index.php?id=469