Subduction zones are located where one of the Earth's tectonic plates slides beneath another - this motion is controlled by the plate boundary fault. These plate boundary faults are capable of generating the largest earthquakes and tsunami on Earth, such as the 2011 Tohuku-oki, Japan and the 2004 Sumatra-Andaman earthquakes, together responsible for ~250,000 fatalities. Although some plate boundary faults fail in catastrophic earthquakes, at some subduction margins the plates creep past each other effortlessly with no stress build-up along the fault, and therefore large earthquakes are not generated. Determining what controls whether a fault creeps or slips in large earthquakes is fundamental to assessing the seismic hazard communities living in the vicinity of plate boundary faults face and to our understanding of the earthquake process itself. In the last 15 years a completely new type of seismic phenomena has been discovered at subduction zones: silent earthquakes or slow slip events (SSEs). These are events that release as much energy as a large earthquake, but do so over several weeks or even months and there is no ground-shaking at all. SSEs may have the potential to trigger highly destructive earthquakes and tsunami, but whether this is possible and why SSEs occur at all are two of the most important questions in earthquake seismology today. We only know SSEs exist because they cause movements of the Earth that can be measured with GPS technology. Slow slip events have now been discovered at almost all subduction zones where there is a good, continuous GPS network, including Japan, Costa Rica, NW America and New Zealand. Importantly, there is recent evidence that SSEs preceded and may have triggered two of the largest earthquakes this decade, the 2011 Tohuki-oki and 2014 Iquique, Chile earthquakes. Therefore, there is an urgent societal need to better understand SSEs and their relationship to destructive earthquakes. We know little about SSEs because most of them occur at depths of 25-40 km: too deep to drill and to image clearly using seismic data, a remote method that uses high-energy sound waves to probe the Earth's crust. The Hikurangi margin of northern New Zealand is an important exception. Very shallow SSEs occur here at depths of c. 5 km below the sea bed, and they occur regularly every 1-2 years. This SSE zone is the only such zone worldwide within likely range of modern drilling capabilities and where we can image the fault clearly with seismic techniques - this location provides us with an opportunity to sample and image the fault zone that slowly slips. This will allow testing of a number of different hypotheses proposed to explain SSEs. We can also compare the properties of these rocks with drilling and seismic data from other locations such as Japan, where the faults behave differently and generate very large earthquakes. Through this comparison we can get closer to understanding why some subduction margin faults fail in large earthquakes and others do not and what fault properties control the different slip processes. Before the drilling can take place we need 3D seismic data to characterise the drill site to highlight any potential risks and to allow us to learn more about how rock properties vary in three dimensions away from the drill sites. Even before or without drilling the seismic images will provide important details of the slow slip process and fault properties. We will use a new technique, called full-waveform inversion (FWI) that can produce high resolution models of the speed of sound waves through the Earth's crust. Sound waves travel slower through rocks that contain a lot of fluids so we will look for low velocity anomalies signifying the presence of fluids, which models have suggested could allow generation of SSEs. The groundbreaking FWI imaging of the New Zealand subduction zone will be the first of its kind, providing information on fault zone properties at unprecedented resolution.

The Sichuan earthquake in mid-May killed more than 87,000 persons and caused damage on the order of fifty billion pounds. Much of the damage, and further loss of life, resulted from aftershocks - smaller earthquakes triggered by the occurrence of the M7.9 mainshock. Research over the past 15 years or so has increased our understanding of aftershocks and particularly of the controls on their locations. The most important realisation is that large earthquakes induce stress changes in the surrounding crust which can be calculated within a few hours of the mainshock; areas of stress increase correlate strongly with the occurrence of aftershocks. (These 'Coulomb' stresses are computed by resolving the tensorial stress perturbation into shear and normal components on planes of interest; increased shear stress and decrease normal stress encourage failure.) Maps of such stress changes can be used to inform emergency response so that, for example, evacuation shelters are sited in areas where stress has decreased and hence aftershocks are not expected. More useful information for emergency services would be an estimation of the probability of an earthquake above a particular magnitude affecting any given location. Calculating such probabilities is not straight-forward, however, as the only model that directly relates stress and probability changes is based on laboratory friction experiments and relies on a number of assumptions that may not be realistic as well as the determination of a number of poorly constrained parameters. To date, this model has only been subjected to one systematic test and the results were inconclusive. An alternative approach to estimating aftershock probabilities is purely statistical, based on two key observations: the Gutenberg-Richter relation which describes the number-size distribution of earthquakes and the Omori law for decrease in aftershock numbers with time. In a recent test on a single aftershock sequence, the abilities of 7 statistical and 4 stress-based models to forecast probabilities were rigorously tested. Surprisingly, the statistical models fared best, despite their lack of the essential physics that controls the spatial distribution of aftershocks. The reason for this result is open to interpretation, but the stress-based models may have suffered because of the failure of the unphysical assumptions in the friction law or because the required parameters were not estimated correctly. Alternatively, because in this model the expected rate of aftershocks depends on the magnitude of the stress change, there may have been problems with the calculated stress field due to un-modelled small scale heterogeneity in the earthquake slip distribution. The aim of this project is to develop a combined stress/statistical model for aftershock occurrence and rigorously test it against statistical and stress-based models as well as several simple reference models. This new model will retain the important spatial controls that result from the stress perturbation but will circumvent the difficulties associated with the rate-state approach. If successful, we will have a new method for calculating aftershock probabilities.

PROJECT SUMMARY As a result of continuous burning of fossil fuel, the global environment is facing a crisis stemming from rapidly rising concentrations of carbon dioxide and other greenhouse gases in our atmosphere. Assuming greenhouse gas emissions at or above current rates, carbon dioxide will reach nearly triple the pre-industrial concentrations by the end of this century. This is expected to raise global mean temperatures to a level not seen for more than 32 million years. According to the latest assessment of the Intergovernmental Panel on Climate Change, the high latitudes will experience the largest temperature increases, resulting in a rapid melting of polar ice-sheets and global sea level rise. For a further understanding of potential changes that our world may undergo in the future, it is vital to study environmental changes during past warm periods and across major climatic thresholds. The proposed research project will reconstruct past vegetation of Antarctica and southern Australasia during the Eocene (ca 55-34 million years ago). The Eocene is a geological time period of exceptional warmth, with atmospheric CO2 concentrations exceeding triple the pre-industrial levels. The project will reconstruct past vegetation by analysing pollen in sediments deposited during Eocene times. Vegetation provides detailed information on a number of important environmental parameters, such as annual temperature and precipitation, length of growing season, minimum and maximum temperatures, and soils. Of particular interest for this study are very short-lived time intervals during the early and late Eocene, during which carbon dioxide concentration and temperatures changed rapidly. For a full understanding of their climate forcings and mechanisms, the analysed sediments must have a high time resolution and unambiguous dating control, in order to relate them to respective past climate events. The marine cores 1171 and 1171, taken offshore Tasmania as part of the International Ocean Deep Drilling Programme Leg 189, as well as Eocene rock outcrops at Cape Foulwind in New Zealand, have been chosen for the proposed study, as they provide an unprecedented opportunity to produce high resolution pollen records for Antarctica and adjacent sub-polar regions. The data will be interpreted in a global context and related to Arctic palaeoenvironmental reconstructions by integrating them into the global GIS database TEVIS (Tertiary Environment and Vegetation Information System). The TEVIS dataset will be compared with a number of simulations using the cutting edge Hadley Centre climate model (used within the climate assessment reports of the Intergovernmental Panel of Climate Change IPCC). By combining regional high-resolution pollen analyses with global data-model comparison, this proposed study will foster a deeper understanding of how the terrestrial environments and polar ice sheets responded, and might respond in the future, to rapid changes in temperatures and atmospheric CO2 concentration. By indicating weakness and strength, the data-model comparison will also contribute to the improvement of climate models that we rely upon for simulating future climate change.