Role of Fracturing in the Organization of the Karst Features of Azrou Plateau (Middle Atlas,Morroco) Studied by Remote Sensing Imagery

Karst formation geometry can be controlled by fractures and faults, and by other lithologies. Here we show the organization of kastic collapse features related to structures and to extensive basaltic lava flows in the Middle Atlas of Morocco. A lineament map of major faults and fractures has been created for the Middle Atlas using Landsat 7 ETM⁺ satellite images. This shows a dominant NE–SW regional direction and less prominent NNW–SSE and ENE–WSE directions. All these directions coincide with the alignments of karstic depressions that have formed in the Liassic limestones. The basaltic flows covering these formations on the Middle Atlas limestone plateau, have allowed the generation of cryptokarst, geometrically organized a long these major lineament directions. Karst landforms probabaly existed before the eruption of the lavas, but there were partly invaded by intrusions and volcanism. The extensive basaltic flows allowed for increased infiltration, and subsurface water flow, increasing the rate of kast formation after eruptions. Some basins show evidence of increased subsidence after lava emplacement (Aguelmam Sidi Ali Lake) and some maar-like craters also have subsided after eruption, by karts formation. We lay out the structural and lithological controls on Karstic formation in an intraplate volcanic field based on limestones and evaporites.     

Modeling the environmental susceptibility of landfill sites in California

??The proper management of solid waste (SW) is a global environmental challenge. A major issue is the proper disposal of SW while balancing a wide range of criteria and working with different spatial data. In this study, we used geographic information system as a tool to perform multi-criteria decision analysis with an analytical hierarchy process to develop an environmental impact susceptibility model (EISM) for landfills. The model was applied to the state of California, USA and results are presented herein. In particular, the EISM considers factors such as geology, pedology, geomorphology, water resources, and climate as represented by 13 associated environmental indicators. The results of the EISM indicate that more than 75% of California’s territory is situated in areas with very low, low, and medium environmental impact susceptibility categories. However, in the remaining 25% of the state’s land, 61 landfills are located in the high and very high categories. These results are alarming because during the period from 2000 to 2015, these 61 landfills received approximately 308 million tons of SW, which corresponds to more than 57% of all SW disposed in California. The model results can be used toward mitigating the environmental impacts of these facilities.     

Identification of paralytic shellfish toxin-producing microalgae using machine learning and deep learning methods

Journal of Oceanology and Limnology - Paralytic shellfish poisoning (PSP) microalgae, as one of the harmful algal blooms, causes great damage to the offshore fishery, marine culture, and marine...     

On the age calibration of the geomagnetic polarity timescale

Knowledge of the age of undated events is not null if a time-order relationship can be found among these events. The knowledge of such a time-ordered sequence can be formalized by using non-informative (uniform) prior probability densities for the ages of undated events and Bayes' theorem to introduce the time-order relationship condition. We show that the conditional probability densities of the ages of events of unknown age are given by various forms of Euler's beta distribution. These distributions yield an estimate of the probability for an undated event to occur in a given age interval.
  We use this method to propose appropriate probabilistic representations of our actual knowledge of the dating of the magnetic polarity reversals during the Cenozoic. These representations take into account the uncertainties arising from irregularities in accretion process and from the quality of a few calibration points. Both types of uncertainties generate large ambiguities in the age of magnetic reversals, which should be taken into consideration when the geomagnetic polarity timescale is used for dating purposes. We propose to use the entropy function to quantify these ambiguities.     

Modelling of local velocity anomalies: a cookbook

The determination of small-scale velocity anomalies (from tens to a few hundreds of metres) is a major problem in seismic exploration. The impact of such anomalies on a structural interpretation can be dramatic and conventional techniques such as tomographic inversion or migration velocity analysis are powerless to resolve the ambiguity between structural and velocity origins of anomalies. We propose an alternative approach based on stochastic modelling of numerous anomalies until a set of models is found which can explain the real data. This technique attempts to include as much a priori geological information as possible. It aims at providing the interpreter with a set of velocity anomalies which could possibly be responsible for the structural response. The interpreter can then choose one or several preferred models and pursue a more sophisticated analysis. The class of retained models are all equivalent in terms of data and therefore represent the uncertainty in the model space. The procedure emulates the real processing sequence using a simplified scheme. Essentially, the technique consists of five steps: 1 Interpretation of a structural anomaly in terms of a velocity anomaly with its possible variations in terms of position, size and amplitude. 2 Drawing a model by choosing the parameters of the anomaly within the acceptable range. 3 Modelling the traveltimes in this model and producing the imaging of the reflected interface. 4 Comparing the synthetic data with the real data and keeping the model if it lies within the data uncertainty range. 5 Iterate from step 2. In order to avoid the high computational cost inherent in using statistical determinations, simplistic assumptions have been made: ? The anomaly is embedded in a homogeneous medium: we assume that the refraction and the time shift due to the anomaly have a first-order effect compared with ray bending in the intermediate layers. ? We model only the zero-offset rays and therefore we restrict ourselves to structural problems. ? We simulate time migration and so address only models of limited structural complexity. These approximations are justified in a synthetic model which includes strong lateral velocity variations, by comparing the result of a full processing sequence (prestack modelling, stack and depth migration) with the simplified processing. This model is then used in a blind test on the inversion scheme.     

Spatial models for probabilistic prediction of wind power with application to annual-average and high temporal resolution data

Producing accurate spatial predictions for wind power generation together with a quantification of uncertainties is required to plan and design optimal networks of wind farms. Toward this aim, we propose spatial models for predicting wind power generation at two different time scales: for annual average wind power generation, and for a high temporal resolution (typically wind power averages over 15-min time steps). In both cases, we use a spatial hierarchical statistical model in which spatial correlation is captured by a latent Gaussian field. We explore how such models can be handled with stochastic partial differential approximations of Matérn Gaussian fields together with Integrated Nested Laplace Approximations. We demonstrate the proposed methods on wind farm data from Western Denmark, and compare the results to those obtained with standard geostatistical methods. The results show that our method makes it possible to obtain fast and accurate predictions from posterior marginals for wind power generation. The proposed method is applicable in scientific areas as diverse as climatology, environmental sciences, earth sciences and epidemiology.