High-order kernels for Riemannian wavefield extrapolation

Riemannian wavefield extrapolation is a technique for one‐way extrapolation of acoustic waves. Riemannian wavefield extrapolation generalizes wavefield extrapolation by downward continuation by considering coordinate systems different from conventional Cartesian ones. Coordinate systems can conform with the extrapolated wavefield, with the velocity model or with the acquisition geometry. When coordinate systems conform with the propagated wavefield, extrapolation can be done accurately using low‐order kernels. However, in complex media or in cases where the coordinate systems do not conform with the propagating wavefields, low order kernels are not accurate enough and need to be replaced by more accurate, higher‐order kernels. Since Riemannian wavefield extrapolation is based on factorization of an acoustic wave‐equation, higher‐order kernels can be constructed using methods analogous to the one employed for factorization of the acoustic wave‐equation in Cartesian coordinates. Thus, we can construct space‐domain finite‐differences as well as mixed‐domain techniques for extrapolation. High‐order Riemannian wavefield extrapolation kernels improve the accuracy of extrapolation, particularly when the Riemannian coordinate systems does not closely match the general direction of wave propagation.     

Application of biomarkers for improving risk assessments of chemicals under the Water Framework Directive: a case study

To answer the requirement of the European Commission's Water Framework Directive (WFD) for biological-effects endpoints to classify the ecological health of aquatic ecosystems, we propose the biomarker response index (BRI). The BRI, based on a suite of biomarkers at different levels of biological response at the individual level, provides an integrated relative measure of the general health status of coastal invertebrates. Using the BRI, the health of mussels (Mytilus edulis) from 10 estuaries classified by the Environment Agency of England and Wales under the WFD was compared. Eight sites were healthier than predicted and two showed a similar health status to that of the predicted point-source pollution risk classification. Results indicate that the BRI offers a potential measure of organism health that can be used in monitoring under the WFD as an additional aid to reduce uncertainty in defining risk classification and to provide better evidence of existing impact.     

The use of monitoring data for identifying factors influencing phytoplankton bloom dynamics in the eutrophic Taw Estuary, SW England

Using the Taw Estuary as an example, data routinely collected by the Environment Agency for England and Wales over the period 1990-2004 were interrogated to identify the drivers of excessive algal growth. The estuary was highly productive with chlorophyll concentrations regularly exceeding 100 μg L⁻¹, mostly during periods of low freshwater input from the River Taw when estuarine water residence times were longest. However, algal growth in mid estuary was often inhibited by ammonia inputs from the adjacent sewage treatment works. The reported approach demonstrates the value of applying conventional statistical analyses in a structured way to existing monitoring data and is recommended as a useful tool for the rapid assessment of eutrophication. However, future estuarine monitoring should include the collection of dissolved organic nutrient data and targeted high temporal resolution data because the drivers of eutrophication are complex and often very specific to a particular estuary.     

Reconstruction of the layer anisotropic elastic parameters and high-resolution fracture characterization from P-wave data: a case study using seismic inversion and Bayesian rock physics parameter estimation

Wide-azimuth seismic data can be used to derive anisotropic parameters on the subsurface by observing variation in subsurface seismic response along different azimuths. Layer-based high-resolution estimates of components of the subsurface anisotropic elastic tensor can be reconstructed by using wide-azimuth P-wave data by combining the kinematic information derived from anisotropic velocity analysis with dynamic information obtained from amplitude versus angle and azimuth analysis of wide-azimuth seismic data. Interval P-impedance, S-impedance and anisotropic parameters associated with anisotropic fracture media are being reconstructed using linearized analysis assuming horizontal transverse anisotropy symmetry. In this paper it is shown how additional assumptions, such as the rock model, can be used to reduce the degrees of freedom in the estimation problem and recover all five anisotropic parameters. Because the use of a rock model is needed, the derived elastic parameters are consistent with the rock model and are used to infer fractured rock properties using stochastic rock physics inversion. The inversion is based on stochastic rock physics modelling and maximum a posteriori estimate of both porosity and crack density parameters associated with the observed elastic parameters derived from both velocity and amplitude versus angle and azimuth analysis. While the focus of this study is on the use of P-wave reflection data, we also show how additional information such as shear wave splitting and/or anisotropic well log data can reduce the assumptions needed to derive elastic parameter and rock properties.     

A land surface data assimilation framework using the land information system: Description and applications

The Land Information System (LIS) is an established land surface modeling framework that integrates various community land surface models, ground measurements, satellite-based observations, high performance computing and data management tools. The use of advanced software engineering principles in LIS allows interoperability of individual system components and thus enables assessment and prediction of hydrologic conditions at various spatial and temporal scales. In this work, we describe a sequential data assimilation extension of LIS that incorporates multiple observational sources, land surface models and assimilation algorithms. These capabilities are demonstrated here in a suite of experiments that use the ensemble Kalman filter (EnKF) and assimilation through direct insertion. In a soil moisture experiment, we discuss the impact of differences in modeling approaches on assimilation performance. Provided careful choice of model error parameters, we find that two entirely different hydrological modeling approaches offer comparable assimilation results. In a snow assimilation experiment, we investigate the relative merits of assimilating different types of observations (snow cover area and snow water equivalent). The experiments show that data assimilation enhancements in LIS are uniquely suited to compare the assimilation of various data types into different land surface models within a single framework. The high performance infrastructure provides adequate support for efficient data assimilation integrations of high computational granularity.