Effect of the thermal gradient variation through geological time on basin modeling; a case study: The Paris basin

Many studies investigated the thermal modeling of the Paris basin for petroleum interests during the 1970s. Most of the softwares developed by oil companies or research centers were based on the assumption of a constant thermal gradient. In order to take into consideration the variation of the thermal gradient during basin evolution, we developed the TherMO's Visual Basic 1D program. We applied our model to twenty boreholes located along a cross-section roughly running EW over 150 km in the center of the Paris basin. The numerical results were calibrated with organic matter maturity data. TherMO's simulates the amount of heat provided to the sedimentary organic matter. The heat parameter simulated shows lateral variation along the cross-section. It decreases from Rambouillet to Trou Aux Loups boreholes (87–66 mW/m²) at about 100 km more to the east whereas the heat flux value simulated is 73 mW/m² in St. Loup borehole. The mean thermal gradient calculated for liassic horizons at 87 My for the Rambouillet well is 50.4 °C/km. This value is similar to previously published results. By integrating the calculation of the thermal gradients and conductivities related to the burial of each stratigraphic sequence, our approach points out variations in the thermal regimes the sedimentary organic matter (SOM) has been subjected to through geological time.     

Effects of mantle upwelling in a compressional setting: the Atlas Mountains of Morocco

We discuss the implications of a lithospheric model of the Moroccan Atlas Mountains based on topography, heat flow, gravity and geoid anomalies, taking into account the regional geology. The NW African cratonic lithosphere, some 160–180 km thick, thins to c. 80 km beneath the Atlas fold-thrust belts, in contrast with the shortening regime prevailing there since the early Cenozoic. This fact explains several geological and geophysical features as high topography with modest tectonic shortening, the occurrence of alkaline magmatism contemporaneous to compression, the absence of large crustal roots to support elevation, the scarce development of foreland basins, and a marked geoid high. The modelled lithosphere thinning is related to a thermal upwelling constrained between the Iberia–Africa convergent plate boundary and the Saharan craton.     

Biomarkers in croakers Micropogonias furnieri (Teleostei: Sciaenidae) from polluted and non-polluted areas from the Patos Lagoon estuary (Southern Brazil): evidences of genotoxic and immunological effects

Biomarkers of exposure and effect of pollutants were analyzed in croakers Micropogonias furnieri (Teleostei: Sciaenidae) captured in winter and summer in a polluted and in a non-polluted site at the Patos Lagoon estuary (Southern Brazil). Catalase and glutathione S-transferase activities (exposure biomarkers) and lipid peroxidation (effect biomarker) were analyzed in liver samples. Other two effect biomarkers were also studied: blood cells DNA damage (through comet assay and micronucleus test) and respiratory burst measurements. In a broad view, results point to an important seasonal variation of the biochemical biomarkers analyzed. However, data obtained clearly indicate that croakers collected in winter at the polluted site were subjected to a level of clastogenic agents sufficient to generate irreversible genetic damages (mutations) and impair the fish immune system.     

Review of solutions for 3D hydrodynamic modeling applied to aquaculture in South Pacific atoll lagoons

A workshop organized in French Polynesia in November 2004 allowed reviewing the current methods to model the three-dimensional hydrodynamic circulation in semi-enclosed atoll lagoons for aquaculture applications. Mollusk (e.g. pearl oyster, clam) aquaculture is a major source of income for South Pacific countries such as French Polynesia or Cook Islands. This aquaculture now requires a better understanding of circulation patterns to improve the spatial use of the lagoons, especially to define the best area to set larvae collectors. The pelagic larval duration of the relevant species (<20 days) and the size of the semi-closed lagoons (few hundreds of km2) drive the specifications of the model in terms of the spatial and temporal scale. It is considered that, in contrast with fish, mollusk larvae movements are limited and that their cycle occurs completely in the lagoon, without an oceanic stage. Atolls where aquaculture is productive are generally well-bounded, or semi-closed, without significant large and deep openings to the ocean. Nevertheless part of the lagoon circulation is driven by oceanic water inputs through the rim, ocean swells, tides and winds. Therefore, boundary conditions of the lagoon system are defined by the spatial structure of a very shallow rim (exposition and number of hoas), the deep ocean swell climate, tides and wind regimes. To obtain a realistic 3D numerical model of lagoon circulation with adequate forcing, it is thus necessary to connect in an interdisciplinary way a variety of methods (models, remote sensing and in situ data collection) to accurately represent the different components of the lagoon system and its specific boundary conditions. We review here the current methods and tools used to address these different components for a hypothetical atoll of the Tuamotu Archipelago (French Polynesia), representative of the semi-closed lagoons of the South Pacific Ocean. We hope this paper will serve as a guide for similar studies elsewhere and we provide guidelines in terms of costs for all the different stages involved.     

Hyperspectral image betweenness centrality clustering based adaptive spatial and spectral neighborhood approach for anomaly detection

Segmentation-based anomaly detectors proceeds to the clustering of the hyperspectral image as a first step. However, most of the well-known clustering methods cluster anomalous pixels as a part of the background. This paper presents a new hyperspectral image clustering approach based on the betweenness centrality measure. The proposed approach starts by the construction of an adaptive spatial and spectral neighborhood for each pixel. This neighborhood is based on the selection of the nearest spectral and spatial neighbors in multiple windows around each pixel to allow well-suited representation of the image features. In the next step, this neighborhood is clustered based on the edge betweenness measure algorithm that splits the image into regions sharing similar features. This approach (1) allows the reduction of intercluster relationship, (2) favors intracluster relations, and (3) preserves small clusters that can hold anomalous pixels. Experimental results show that the proposed approach is efficient for clustering and overcomes the state of the art approaches.     

Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part 1: Temperature mean state and seasonal cycle in South America

Projections for South America of future climate change conditions in mean state and seasonal cycle for temperature during the twenty-first century are discussed. Our analysis includes one simulation of seven Atmospheric-Ocean Global Circulation Models, which participated in the Intergovernmental Panel on Climate Change Project and provided at least one simulation for the twentieth century (20c3m) and one simulation for each of three Special Report on Emissions Scenarios (SRES) A2, A1B, and B1. We developed a statistical method based on neural networks and Bayesian statistics to evaluate the models’ skills in simulating late twentieth century temperature over continental areas. Some criteria [model weight indices (MWIs)] are computed allowing comparing over such large regions how each model captures the temperature large scale structures and contributes to the multi-model combination. As the study demonstrates, the use of neural networks, optimized by Bayesian statistics, leads to two major results. First, the MWIs can be interpreted as optimal weights for a linear combination of the climate models. Second, the comparison between the neural network projection of twenty-first century conditions and a linear combination of such conditions allows the identification of the regions, which will most probably change, according to model biases and model ensemble variance. Model simulations in the southern tip of South America and along the Chilean and Peruvian coasts or in the northern coasts of South America (Venezuela, Guiana) are particularly poor. Overall, our results present an upper bound of potential temperature warming for each scenario. Spatially, in SRES A2, our major findings are that Tropical South America could warm up by about 4°C, while southern South America (SSA) would also undergo a near 2–3°C average warming. Interestingly, this annual mean temperature trend is modulated by the seasonal cycle in a contrasted way according to the regions. In SSA, the amplitude of the seasonal cycle tends to increase, while in northern South America, the amplitude of the seasonal cycle would be reduced leading to much milder winters. We show that all the scenarios have similar patterns and only differ in amplitude. SRES A1B differ from SRES A2 mainly for the late twenty-first century, reaching more or less an 80–90% amplitude compared to SRES A2. SRES B1, however, diverges from the other scenarios as soon as 2025. For the late twenty-first century, SRES B1 displays amplitudes, which are about half those of SRES A2.     

Relationship between electrical conductivity, <Superscript>18</Superscript>O of water and NO<Subscript>3</Subscript> content in different streamflow stages

The Ballenera Creek has 160 km² being a small catchment in the Pampa Plain in Argentina. This area has been deeply modified by human action through agricultural activities. From 2013 to 2017, electrical conductivity, stable isotopes of water and nitrate concentration monitoring program were conducted. The sampling included weekly and bimonthly samples in two sites along the stream, several groundwater wells and monthly precipitation. Chemical and isotopic tracers are used to discriminate the streamflow components and to evaluate their incidence in the nitrate concentration. The easiest conceptual model for gaining streams contemplates two main elements: direct runoff and groundwater (baseflow and pre-event water). The direct runoff has the lowest electrical conductivity and ¹⁸O_w variable content. The baseflow component is characterized by the highest electrical conductivity and isotope composition quite constant. Finally, pre-event water has an intermediate electrical conductivity and isotopic content close to the rainfall-weighted average composition. The nitrate concentration obtained was in general related to the different stream stages and was a useful indicator to evaluate the fertilization in agricultural zones.