Information content of snow hydrological signatures based on streamflow,precipitation and air temperature

Hydrological signatures that represent snow processes are valuable to gain insights into snow accumulation and snow melt dynamics. We investigated five snow signatures. Considering inter-annual average of each calendar day, two slopes derived from the relation between streamflow and air temperature for different periods and streamflow peak maxima are used as signatures. In addition, two different approaches are used to compute inter-annual average and yearly snow storage estimates. We evaluated the ability of these signatures to characterize average (a) snow melt dynamics and (b) snow storage. They were applied in 10 Critical Zone Observatory catchments of the Southern Sierra mountains (USA) characterized by a Mediterranean climate. The relevance and information content of the signatures are evaluated using snow depth and snow water equivalent measurements as well as inter-catchment differences in elevation. The slopes quantifying the relations between streamflow and air temperature and the date of streamflow peak were found to characterize snow melt dynamics in terms of snow melt rates and snow melt affected areas. Streamflow peak dates were linked to the period of highest snow melt rates. Snow storage could be estimated both on average, considering all years, and for each year. Snow accumulation dynamics could not be characterized due to the lack of streamflow response during the snow accumulation period. The signatures were found potentially valuable to gain insights into catchment scale snow processes. In particular, when comparing catchments or observed and simulated data, they could provide insights into differences in terms of (a) snow melt rate and/or snow melt affected area over the snow melt season and (b) average or yearly snow storage. Requiring only widely available data, these hydrological signatures can be valuable for snow processes characterization, catchment comparison/classification or model development, calibration or evaluation.     

Local and controlled prestack depth migration in complex areas

Prestack depth migrations based on wavefield extrapolation may be computationally expensive, especially in 3D. They are also very dependent on the acquisition geometry and are not flexible regarding the geometry of the imaging zone. Moreover, they do not deal with all types of wave, considering only primary reflection events through the model. Integral approaches using precalculated Green's functions, such as Kirchhoff migration and Born-based imaging, may overcome these problems. In the present paper, both finite-difference traveltimes and wavefront construction are used to obtain asymptotic Green's functions, and a generalized diffraction tomography is applied as an example of Born-based acoustic imaging. Target-orientated imaging is easy to perform, from any type of survey and subselection of shot/receiver pairs. Multifield imaging is possible using Green's functions that take into account, for instance, reflections at model boundaries. This may help to recover parts of complex structures which would be missing using a paraxial wave equation approach. Finally, a numerical evaluation of the resolution, or point-spread, function at any point of the depth-migrated section provides valuable information, either at the survey planning stage or for the interpretation.     

Integrating facies-based Bayesian inversion and supervised machine learning for petro-facies characterization in the Snadd Formation of the Goliat Field,south-western Barents Sea

Seismic petro-facies characterization in low net-to-gross reservoirs with poor reservoir properties such as the Snadd Formation in the Goliat field requires a multidisciplinary approach. This is especially important when the elastic properties of the desired petro-facies significantly overlap. Pore fluid corrected endmember sand and shale depth trends have been used to generate stochastic forward models for different lithology and fluid combinations in order to assess the degree of separation of different petro-facies. Subsequently, a spectral decomposition and blending of selected frequency volumes reveal some seismic fluvial geomorphological features. We then jointly inverted for impedance and facies within a Bayesian framework using facies-dependent rock physics depth trends as input. The results from the inversion are then integrated into a supervised machine learning neural network for effective porosity discrimination. Probability density functions derived from stochastic forward modelling of endmember depth trends show a decreasing seismic fluid discrimination with depth. Spectral decomposition and blending of selected frequencies reveal a dominant NNE trend compared to the regional SE–NW pro-gradational trend, and a local E–W trend potentially related to fault activity at branches of the Troms-Finnmark Fault Complex. The facies-based inversion captures the main reservoir facies within the limits of the seismic bandwidth. Meanwhile the effective porosity predictions from the multilayer feed forward neural network are consistent with the inverted facies model, and can be used to qualitatively highlight the cleanest regions within the inverted facies model. A combination of facies-based inversion and neural network improves the seismic reservoir delineation of the Snadd Formation in the Goliat Field.     

Spatial land use regression of nitrogen dioxide over a 5-year interval in Calgary,Canada

We analysed the spatial distribution of nitrogen dioxide over Calgary (Canada) in summer 2010 and winter 2011 and in summer 2015 and winter 2016, and estimated land use regressions for 2015–16 (2010–11 models were estimated previously). As nitrogen dioxide exhibited spatial clustering, we evaluated the following spatial specifications against a linear model: spatially autoregressive (lag), spatially autoregressive (error), and geographically weighted regression. The spatially autoregressive (lag) specification performed best, achieving goodness-of-fit aligned with or greater than values reported in the literature. We compared the 2015–16 spatially autoregressive models with the 2010–11 models and reparametrized them on the 2010–11 and the 2015–16 data. Finally, we identified a single set of predictors to best fit the data. Nitrogen dioxide concentration decreased over the 5 years, retaining consistent spatial and seasonal patterns, with higher concentrations over traffic corridors and industrial areas, and greater variation in summer than winter. The multi-temporal analysis suggested that spatial land use regressions were robust over the time interval, despite moderate land use change. Multi-temporal spatial land use regressions yielded consistent predictors for each season over time, which can aid estimation of air pollution at fine spatial resolution over an extended time period.     

Ontologies to interpret remote sensing images: why do we need them?

The development of new sensors and easier access to remote sensing data are significantly transforming both the theory and practice of remote sensing. Although data-driven approaches based on innovative algorithms and enhanced computing capacities are gaining importance to process big Earth Observation data, the development of knowledge-driven approaches is still considered by the remote sensing community to be one of the most important directions of their research. In this context, the future of remote sensing science should be supported by knowledge representation techniques such as ontologies. However, ontology-based remote sensing applications still have difficulty capturing the attention of remote sensing experts. This is mainly because of the gap between remote sensing experts’ expectations of ontologies and their real possible contribution to remote sensing. This paper provides insights to help reduce this gap. To this end, the conceptual limitations of the knowledge-driven approaches currently used in remote sensing science are clarified first. Then, the different modes of definition of geographic concepts, their duality, vagueness and ambiguity, and the sensory and semantic gaps are discussed in order to explain why ontologies can help address these limitations. In particular, this paper focuses on the capacity of ontologies to represent both symbolic and numeric knowledge, to reason based on cognitive semantics and to share knowledge on the interpretation of remote sensing images. Finally, a few recommendations are provided for remote sensing experts to comprehend the advantages of ontologies in interpreting satellite images.     

Mineralogical evidence for a local volcanic origin of the parent material of Bermuda Quaternary paleosols

The alternation of carbonate deposits and paleosols compose the emerged part of the Bermuda archipelago. The pedological units present a complex and diversified mineralogy. Former studies demonstrated that the paleosols are not primarily a product of the unique dissolution of the surrounding carbonates, but contain a massive input of allochthonous non-carbonate detrital material. Researchers during more than the past three decades have attributed this flux of insoluble residues (IR) to Saharan dusts. We carried out systematic field and mineralogical analyses on the Quaternary paleosols from the Bermuda archipelago. Their mineralogical assemblage predominantly includes carbonates, clay minerals (kaolinite, chlorite and chlorite/vermiculite), phosphates, and aluminium and iron oxides/hydroxides. This assemblage is strikingly close to the mineralogy of the weathered volcanic substrate of Bermuda, but noticeably different from the mineralogy of Saharan dust. Moreover, we found volcanic lithoclasts in numerous paleosol profiles all over the archipelago and in all the recorded time intervals. We thus consider the volcanic seamount underlying Bermuda as the main source of non-carbonate minerals detected in the paleosols. This hypothesis further resolves the anomalous maturity of Bermudan paleosols compared to their southern counterparts in the Bahamas and Barbados.     

Mineral chemical study of U-bearing minerals from the Dominion Reefs,South Africa

The Neo-Archean Dominion Reefs (~3.06 Ga) are thin meta-conglomerate layers with concentrations of U- and Th-bearing heavy minerals higher than in the overlying Witwatersrand Reefs. Ore samples from Uranium One Africa’s Rietkuil and Dominion exploration areas near Klerksdorp, South Africa, were investigated for their mineral paragenesis, texture and mineral chemical composition. The ore and heavy mineral assemblages consist of uraninite, other uraniferous minerals, Fe sulphides, Ni–Co sulfarsenides, garnet, pyrite, pyrrhotite, monazite, zircon, chromite, magnetite and minor gold. Sub-rounded uraninite grains occur associated with the primary detrital heavy mineral paragenesis. U–Ti, U–Th minerals, pitchblende (colloform uraninite) and coffinite are of secondary, re-mobilised origin as evidenced by crystal shape and texture. Most of the uranium mineralisation is represented by detrital uraninite with up to 70.2 wt.% UO₂ and up to 9.3 wt.% ThO₂. Re-crystallised phases such as secondary pitchblende (without Th), coffinite, U–Ti and U–Th phases are related to hydrothermal overprint during low-grade metamorphism and are of minor abundance.     

In situ rates of sulfate reduction in response to geochemical perturbations

Rates of in situ microbial sulfate reduction in response to geochemical perturbations were determined using Native Organism Geochemical Experimentation Enclosures (NOGEEs), a new in situ technique developed to facilitate evaluation of controls on microbial reaction rates. NOGEEs function by first trapping a native microbial community in situ and then subjecting it to geochemical perturbations through the introduction of various test solutions. On three occasions, NOGEEs were used at the Norman Landfill research site in Norman, Oklahoma, to evaluate sulfate-reduction rates in wetland sediments impacted by landfill leachate. The initial experiment, in May 2007, consisted of five introductions of a sulfate test solution over 11 d. Each test stimulated sulfate reduction with rates increasing until an apparent maximum was achieved. Two subsequent experiments, conducted in October 2007 and February 2008, evaluated the effects of concentration on sulfate-reduction rates. Results from these experiments showed that faster sulfate-reduction rates were associated with increased sulfate concentrations. Understanding variability in sulfate-reduction rates in response to perturbations may be an important factor in predicting rates of natural attenuation and bioremediation of contaminants in systems not at biogeochemical equilibrium.