QICS work package 1: migration and trapping of CO2 from a reservoir to the seabed or land surface

Natural CO2 seeps can be used as analogues for studies into surface flux and impact resulting from leaking engineered geological CO2 reservoirs. However their long-lived nature often means that the local environment has either adapted or evolvedaround the seepage site. The ‘Quantifying Impact of carbon storage’ (QICS) experiment provides the solution to this issue by releasing CO2 into an environment previously untouched by CO2. Work Package 1 (WP1) of the QICS project is primarily concerned with the migration of CO2 in the subsurface and how to relate the results of the relatively shallow experiment to a full storage scale setting in the UK North Sea. The main objectives of WP1 are to investigate potential leakage pathways from the reservoir to the surface, determine possible leakage rates and assess the potential volumes of leaked CO2 that can reach the surface environment

Effects of Single-Dose Ultraviolet Radiation on Skin Superoxide Dismutase, Catalase, and Xanthine Oxidase in Hairless Mice

The effects of a single exposure to UVB radiation on skin antioxidant enzymes and superoxide-generating xanthine oxidase were examined in Skh:HR-1 hairless mice. Significant decreases in superoxide dismutase (SOD) and catalase (CAT) were observed by 12 h after UV irradiation and remained depressed for up to 72 h. No induction of xanthine dehydrogenase (XD) or xanthine oxidase (XO) occurred with UV treatment, although significant hyperplasia was evident. Ornithine decarboxylase was induced after UV irradiation as has been previously reported. These results demonstrate significant biochemical effects of a single dose of UVB on murine epidermis, especially in terms or antioxidant defenses

Approximation of bayesian Hawkes process models with Inlabru

Hawkes process are very popular mathematical tools for modelling phenomena exhibiting a \textit{self-exciting} or \textit{self-correcting} behaviour. Typical examples are earthquakes occurrence, wild-fires, drought, capture-recapture, crime violence, trade exchange, and social network activity. The widespread use of Hawkes process in different fields calls for fast, reproducible, reliable, easy-to-code techniques to implement such models. We offer a technique to perform approximate Bayesian inference of Hawkes process parameters based on the use of the R-package \inlabru. The \inlabru R-package, in turn, relies on the INLA methodology to approximate the posterior of the parameters. Our Hawkes process approximation is based on a decomposition of the log-likelihood in three parts, which are linearly approximated separately. The linear approximation is performed with respect to the mode of the parameters' posterior distribution, which is determined with an iterative gradient-based method. The approximation of the posterior parameters is therefore deterministic, ensuring full reproducibility of the results. The proposed technique only requires the user to provide the functions to calculate the different parts of the decomposed likelihood, which are internally linearly approximated by the R-package \inlabru. We provide a comparison with the \bayesianETAS R-package which is based on an MCMC method. The two techniques provide similar results but our approach requires two to ten times less computational time to converge, depending on the amount of data.Comment: 2o pages, 7 figures, 5 table

Data-driven optimization of seismicity models using diverse data sets: generation, evaluation and ranking using inlabru

Recent developments in earthquake forecasting models have demonstrated the need for a robust method for identifying which model components are most beneficial to understanding spatial patterns of seismicity. Borrowing from ecology, we use Log‐Gaussian Cox process models to describe the spatially varying intensity of earthquake locations. These models are constructed using elements which may influence earthquake locations, including the underlying fault map and past seismicity models, and a random field to account for any excess spatial variation that cannot be explained by deterministic model components. Comparing the alternative models allows the assessment of the performance of models of varying complexity composed of different components and therefore identifies which elements are most useful for describing the distribution of earthquake locations. We demonstrate the effectiveness of this approach using synthetic data and by making use of the earthquake and fault information available for California, including an application to the 2019 Ridgecrest sequence. We show the flexibility of this modeling approach and how it might be applied in areas where we do not have the same abundance of detailed information. We find results consistent with existing literature on the performance of past seismicity models that slip rates are beneficial for describing the spatial locations of larger magnitude events and that strain rate maps can constrain the spatial limits of seismicity in California. We also demonstrate that maps of distance to the nearest fault can benefit spatial models of seismicity, even those that also include the primary fault geometry used to construct them.K. B. was funded during this work by an EPSRC PhD studentship (Grant 1519006) and during the writing of this paper by NERC‐NSF grant NE/R000794/1 and by the Real‐time Earthquake Risk Reduction for a Resilient Europe "RISE" project, which has received funding from the European Union's Horizon 2020 research and innovation program under grant Agreement 821115