Conor Andrew Bacon

and 3 more

The Icelandic crust is a product of its unique tectonic setting, where the interaction of an ascending mantle plume and the mid-Atlantic Ridge has caused elevated mantle melting, which has accreted and cooled in the crust to form an oceanic plateau. Here, we investigate the strength, orientation and distribution of seismic anisotropy in the upper crust of the Northern Volcanic Zone using local earthquake shear wave splitting, with a view to understanding how the contemporary stress field may influence sub-wavelength structure and processes. This is achieved using a dataset comprising >50,000 earthquakes located in the top 10 km of the crust, recorded by up to 70 stations over a 9 year period. We find that anisotropy is largely confined to the top 3–4 km of the crust, with an average delay time of 0.10 ± 0.08 s and an average orientation of the fast axis of anisotropy of N15° ± 33°E, which closely matches the spreading direction of the Eurasian and North American plates (~N16°E). These results are consistent with the presence of rift-parallel cracks that gradually close with depth, the preferential opening of which is controlled by the regional stress field. Lateral variations in the strength of shear wave anisotropy reveal that regions with the highest concentrations of earthquakes have the highest SWA values (~10%), which reflects the presence of significant brittle deformation. Disruption of the orientation of the fast axis of anisotropy around Askja volcano can be related to local stress changes caused by underlying magmatic processes.

Tom Winder

and 5 more

Detecting and locating microearthquakes from continuous waveform records is the fundamental step in microseismic processing. Dense local networks and arrays have introduced the possibility to detect large numbers of far weaker events, but when viewed on seismic records from individual stations their waveforms are often obscured by noise. Furthermore, areas of interest for microseismic monitoring often feature extremely high event rates, highlighting the limitations of traditional techniques based on phase picking and association. In order to maximise the new insights gained, we require fully automated techniques which can exploit modern recordings to produce highly complete earthquake catalogues containing few artefacts. QuakeMigrate is a new modular, open-source python package providing a framework to efficiently, automatically and robustly detect and locate microseismicity. The user inputs continuous seismic data, a velocity model or pre-calculated look-up table and list of station locations. Instead of reducing the raw waveforms to discrete time picks, they are transformed (by amplitude, frequency and/or polarisation analysis) to continuous functions representing the probability of a particular phase arrival through time. These ‘onset functions’ from stations across the network are then migrated according to a travel-time look-up table and stacked to perform a grid-search for coherent sources of energy in the subsurface. This enables detection of earthquakes at close to or below the signal-to-noise ratio at individual stations, and implicitly associates phase arrivals even at very small inter-event times. We demonstrate the flexibility and power of this approach with examples of basal icequakes detected at the Rutford Ice Stream, Antarctica, dike- and caldera-collapse induced seismicity at Bárðarbunga central volcano, Iceland, and the aftershock sequence from a M5 earthquake at Mt. Kinabalu, North Borneo. The modular nature of the workflow and wide range of automatic plotting options makes parameter choice straightforward, and robust event location uncertainty statistics facilitate filtering to produce a robust catalogue. QuakeMigrate also outputs phase picks and local magnitude estimates, with an architecture designed to promote further community-driven extension in future.