Seeing through the magnetite:Reassessing Eoarchean atmosphere composition from Isua(Greenland) ≥3.7 Ga banded iron formations

Estimates of early atmosphere compositions from metamorphosed banded iron formations(BIFs)including the well-studied ≥3.7 BIFs of the Isua supracrustal belt(Greenland)are dependent on knowledge of primary versus secondary Fe-mineralogical assemblages.Using new observations from locally well preserved domains,we interpret that a previously assumed primary redox indicator mineral,magnetite,is secondary after sedimentary Fe-clays(probably greenalite)±carbonates.Within ~3.7 Ga Isua BIF,pre-tectonic nodules of quartz+Fe-rich amphibole±calcite reside in a finegrained(≤100 μm)quartz+magnetite matrix.We interpret the Isua nodule amphibole as the metamorphosed equivalent of primary Fe-rich clays,armoured from diagenetic oxidative reactions by early silica concretion.Additionally,in another low strain lacunae,~3.76 Ga BIF layering is not solid magnetite but instead fine-grained magnetite+quartz aggregates.These magnetite+quartz aggregates are interpreted as the metamorphosed equivalent of Fe-clay-rich layers that were oxidised during diagenesis,because they were not armoured by early silicification.In almost all Isua BIF exposures,this evidence has been destroyed by strong ductile deformation.The Fe-clays likely formed by abiotic reactions between aqueous Fe~(2+)and silica.These clays along with silica±carbonate were deposited below an oceanic Fe-chemocline as the sedimentary precursors of BIF.Breakdown of the clays on the sea floor may have been by anaerobic oxidation of Fe~(2+),a mechanism compatible with iron isotopic data previously published on these rocks.The new determinations of the primary redoxsensitive Fe-mineralogy of BIF significantly revise estimates of early Earth atmospheric oxygen and CO_2 content,with formation of protolith Fe-rich clays and carbonates compatible with an anoxic Eoarchean atmosphere with much higher CO_2 levels than previously estimated for Isua and in the present-day atmosphere.     

Space use by Rhabdosargus holubi in a southern African estuary,with emphasis on fish movements and ecosystem connectivity

Twenty-one juvenile Cape stumpnose Rhabdosargus holubi (140–190 mm fork length) were tagged with internal acoustic transmitters in the lower, middle and upper reaches of the Kowie Estuary, South Africa. The movements of each fish were continually monitored from October 2014 to February 2015 using 22 stationary data-logging acoustic receivers situated at approximately equidistant intervals along the length of the estuary (21 km). Juvenile R. holubi spent the greatest proportion of time within the estuarine environment (83%), with the sea (16%) and riverine (1%) environments used to a much lesser extent. Within the estuarine environment, tagged individuals showed high levels of residency and fidelity to their capture and release sites; however, the degree of residency was dependent on the position of the release site, with batches in the upper and lower reaches exhibiting different space-use patterns. When larger juvenile R. holubi migrated back to the sea for the next phase of their life cycle, they generally did not return to the estuarine environment, thus indicating a permanent ontogenetic shift in habitat use with the onset of sexual maturity. This contribution to our understanding of the ecology of a ubiquitous estuarine fish further highlights the importance of estuarine habitats as nursery areas that require effective management.     

Generalized inversion of a global numerical weather prediction model, II: Analysis and implementation

Summary This is a sequel to Bennett, Chua and Leslie (1996), concerning weak-constraint, four-dimensional variational assimilation of reprocessed cloud-track wind observations (Velden, 1992) into a global, primitive-equation numerical weather prediction model. The assimilation is performed by solving the Euler-Lagrange equations associated with the variational principle. Bennett et al. (1996) assimilate 2436 scalar wind components into their model over a 24-hour interval, yielding a substantially improved estimate of the state of the atmosphere at the end of the interval. This improvement is still in evidence in forecasts for the next 48 hours.The model and variational equations are nonlinear, but are solved as sequence of linear equations. It is shown here that each linear solution is precisely equivalent to optimal or statistical interpolation using a background error covariance derived from the linearized dynamics, from the forcing error covariance, and from the initial error covariance. Bennett et al. (1996) control small-scale flow divergence using divergence dissipation (Talagrand, 1972). It is shown here that this approach is virtually equivalent to including a penalty, for the gradient of divergence, in the variational principle. The linearized variational equations are solved in terms of the representer functions for the wind observations. Diagonalizing the representer matrix yields rotation vectors. The rotated representers are the array modes of the entire system of the model, prior covariances and observations. The modes are the observable degrees of freedom of the atmosphere. Several leading array modes are presented here. Finally, appendices discuss a number of technical implementation issues: time convolutions, convergence in the presence of planetary shear instability, and preconditioning the essential inverse problem.With 9 Figures     

Acceleration of thermodynamic computations in fluid flow applications

Reservoir simulators model the highly nonlinear partial differential equations that represent flows in heterogeneous porous media. The system is made up of conservation equations for each thermodynamic species, flash equilibrium equations and some constraints. With advances in Field Development Planning (FDP) strategies, clients need to model highly complex Improved Oil Recovery processes such as gas re-injection and CO2 injection, which requires multi-component simulation models. The operating range of these simulation models is usually around the mixture critical point and this can be very difficult to simulate due to phase mislabeling and poor nonlinear convergence. We present a Machine Learning (ML) based approach that significantly accelerates such simulation models. One of the most important physical parameters required in order to simulate complex fluids in the subsurface is the critical temperature (T_crit). There are advanced iterative methods to compute the critical point such as the algorithm proposed by Heidemann and Khalil (AIChE J 26,769–799, 1980) but, because these methods are too expensive, they are usually replaced by cheaper and less accurate methods such as the Li-correlation (Reid and Sherwood 1966). In this work we use a ML workflow that is based on two interacting fully connected neural networks, one a classifier and the other a regressor, that are used to replace physical algorithms for single phase labelling and improve the convergence of the simulator. We generate real time compositional training data using a linear mixing rule between the injected and the in-situ fluid compositions that can exhibit temporal evolution. In many complicated scenarios, a physical critical temperature does not exist and the iterative sequence fails to converge. We train the classifier to identify, a-priori, if a sequence of iterations will diverge. The regressor is then trained to predict an accurate value of T_crit. A framework is developed inside the simulator based on TensorFlow that aids real time machine learning applications. The training data is generated within the simulator at the beginning of the simulation run and the ML models are trained on this data while the simulator is running. All the run-times presented in this paper include the time taken to generate the training data and train the models. Applying this ML workflow to real field gas re-injection cases suffering from severe convergence issues has resulted in a 10-fold reduction of the nonlinear iterations in the examples shown in this paper, with the overall run time reduced 2- to 10-fold, thus making complex FDP workflows several times faster. Such models are usually run many times in history matching and optimization workflows, which results in compounded computational savings. The workflow also results in more accurate prediction of the oil in place due to better single phase labelling.