Discovery of ferromanganese hydrocarbon-related nodules associated with the Meknes mud volcano (Western Moroccan margin)

A suite of ferromanganese nodules were sampled during the MVSEIS-2008 cruise aboard of the R/V Hespérides in the flanks of Meknes mud volcano (Moroccan margin, NE Central Atlantic). The nodules were collected at water depths between 750-850 m within a seabed area characterized by high acoustic backscatter values. Debris of cold water corals and hydrocarbon-derived authigenic carbonate crusts were sampled at same time. The nodules show tabular morphology, up to 20 cm in maximum diameter and 2 kg of weight, brown-reddish external color and they are internally composed by a concentric to complex arrangement of laminae. The results of X-ray diffraction analysis show that these ferromanganese nodules are essentially composed of goethite and lepidocrocite, being Mn-oxides, silicates (quartz and clay minerals) and carbonates (calcite, dolomite and siderite) accessory to occasional minerals. All the samples display micritic to micro-sparitic mosaic under the petrographic microscope which forms massive, laminated or dendritic-mottled textures. The nodules show a high abundance of Fe, minor Mn and low contents of trace metals and REEs. Mature hydrocarbons, as n-alkanes derived from marine bacterial activity, and phenanthrene have been detected in all the ferromanganese nodules analyzed. These nodules display analogous characteristics (textural, mineralogical and geochemical) to the nodules studied by González et al (2009) in the carbonate mud-mounds in the Gulf of Cadiz, offshore Iberian margin. In this way, the same preliminary genetic model proposed for these nodules might be applicable to those find in the Meknes mud volcano. Therefore, the anaerobic oxidation of hydrocarbon-rich fluids within the mud-breccia sediments in the flanks of Meknes mud volcano would induce the formation of early diagenetic Fe-(Mn) carbonate nodules. Thus, the nodules were later exhumed by the erosive action of sea bottom currents generating the replacement of ferromanganese carbonates by Fe-Mn oxy-hydroxides. Thus, the hydrocarbon-rich fluid venting from deep seated reservoirs and erosive action of bottom currents must have been essential actors, as mineralization controls, for ferromanganese nodules generation and evolution. These findings imply that this type of nodules must be considered as new product as derived from the anaerobic/aerobic oxidation of hydrocarbons in areas of active seepages.

Flutter margin with non-linearities: real-time prediction of flutter onset speed

The present article shows a procedure to predict the flutter speed based on real-time tuning of a quasi non-linear aeroelastic model. A two-dimensional non-linear (freeplay) aeroeslastic model is implemented inMatLab/Simulink with incompressible aerodynamic conditions. A comparison with real compressible conditions is provided. Once the numerical validation is accomplished, a parametric aeroelastic model is built in order to describe the proposed procedure and contribute to reduce the number of flight hours needed to expand the flutter envelope.

Polymetallic ferromanganese deposits research on the Atlantic Spanish continental margin

Seamounts, submarine banks, volcanoes and undercurrent channels are prominent geomorphic features that have become an important target for minerals research and exploration with the goal of future exploitation. Polymetallic ferromanganese deposits are common types of mineralization on these settings. Co-rich ferromanganese crusts are important as potential resources of Mn and Co, but also Ti, Ni, Tl, REEs, PGEs, and other metals. Many seamounts and channels along the Atlantic Spanish continental margin are known to hold mineral deposits but are poorly studied. This work presents and briefly describes the most recent activities of the Spanish Geological Survey (IGME) on exploration and investigation of ferromanganese deposits along the Atlantic Spanish continental margin. Different submarine areas from the northwestern margin of the Iberian Peninsula to the west off Canary Islands have been surveyed by geophysical, sampling and underwater observations from 89 to 4000 m water depth. The mineral deposits cover a large diversity of submarine geological and geomorphical features: mud volcanoes and diapirs related to hydrocarbon seeps, seamounts associated with hot spot volcanism, hydrothermal vents in active magmatic volcanoes, structural basement highs and banks or contourite channels. Considering the collected dataset, we present the preliminary results of the study of these mineral deposits, including ferromanganese nodules and crusts and phosphate pavements and nodules, which can be considered as potential sources of raw materials.

New definitions of margin for multi-class Boosting algorithms

La familia de algoritmos de Boosting son un tipo de técnicas de clasificación y regresión que han demostrado ser muy eficaces en problemas de Visión Computacional. Tal es el caso de los problemas de detección, de seguimiento o bien de reconocimiento de caras, personas, objetos deformables y acciones. El primer y más popular algoritmo de Boosting, AdaBoost, fue concebido para problemas binarios. Desde entonces, muchas han sido las propuestas que han aparecido con objeto de trasladarlo a otros dominios más generales: multiclase, multilabel, con costes, etc. Nuestro interés se centra en extender AdaBoost al terreno de la clasificación multiclase, considerándolo como un primer paso para posteriores ampliaciones. En la presente tesis proponemos dos algoritmos de Boosting para problemas multiclase basados en nuevas derivaciones del concepto margen. El primero de ellos, PIBoost, está concebido para abordar el problema descomponiéndolo en subproblemas binarios. Por un lado, usamos una codificación vectorial para representar etiquetas y, por otro, utilizamos la función de pérdida exponencial multiclase para evaluar las respuestas. Esta codificación produce un conjunto de valores margen que conllevan un rango de penalizaciones en caso de fallo y recompensas en caso de acierto. La optimización iterativa del modelo genera un proceso de Boosting asimétrico cuyos costes dependen del número de etiquetas separadas por cada clasificador débil. De este modo nuestro algoritmo de Boosting tiene en cuenta el desbalanceo debido a las clases a la hora de construir el clasificador. El resultado es un método bien fundamentado que extiende de manera canónica al AdaBoost original. El segundo algoritmo propuesto, BAdaCost, está concebido para problemas multiclase dotados de una matriz de costes. Motivados por los escasos trabajos dedicados a generalizar AdaBoost al terreno multiclase con costes, hemos propuesto un nuevo concepto de margen que, a su vez, permite derivar una función de pérdida adecuada para evaluar costes. Consideramos nuestro algoritmo como la extensión más canónica de AdaBoost para este tipo de problemas, ya que generaliza a los algoritmos SAMME, Cost-Sensitive AdaBoost y PIBoost. Por otro lado, sugerimos un simple procedimiento para calcular matrices de coste adecuadas para mejorar el rendimiento de Boosting a la hora de abordar problemas estándar y problemas con datos desbalanceados. Una serie de experimentos nos sirven para demostrar la efectividad de ambos métodos frente a otros conocidos algoritmos de Boosting multiclase en sus respectivas áreas. En dichos experimentos se usan bases de datos de referencia en el área de Machine Learning, en primer lugar para minimizar errores y en segundo lugar para minimizar costes. Además, hemos podido aplicar BAdaCost con éxito a un proceso de segmentación, un caso particular de problema con datos desbalanceados. Concluimos justificando el horizonte de futuro que encierra el marco de trabajo que presentamos, tanto por su aplicabilidad como por su flexibilidad teórica. Abstract The family of Boosting algorithms represents a type of classification and regression approach that has shown to be very effective in Computer Vision problems. Such is the case of detection, tracking and recognition of faces, people, deformable objects and actions. The first and most popular algorithm, AdaBoost, was introduced in the context of binary classification. Since then, many works have been proposed to extend it to the more general multi-class, multi-label, costsensitive, etc... domains. Our interest is centered in extending AdaBoost to two problems in the multi-class field, considering it a first step for upcoming generalizations. In this dissertation we propose two Boosting algorithms for multi-class classification based on new generalizations of the concept of margin. The first of them, PIBoost, is conceived to tackle the multi-class problem by solving many binary sub-problems. We use a vectorial codification to represent class labels and a multi-class exponential loss function to evaluate classifier responses. This representation produces a set of margin values that provide a range of penalties for failures and rewards for successes. The stagewise optimization of this model introduces an asymmetric Boosting procedure whose costs depend on the number of classes separated by each weak-learner. In this way the Boosting procedure takes into account class imbalances when building the ensemble. The resulting algorithm is a well grounded method that canonically extends the original AdaBoost. The second algorithm proposed, BAdaCost, is conceived for multi-class problems endowed with a cost matrix. Motivated by the few cost-sensitive extensions of AdaBoost to the multi-class field, we propose a new margin that, in turn, yields a new loss function appropriate for evaluating costs. Since BAdaCost generalizes SAMME, Cost-Sensitive AdaBoost and PIBoost algorithms, we consider our algorithm as a canonical extension of AdaBoost to this kind of problems. We additionally suggest a simple procedure to compute cost matrices that improve the performance of Boosting in standard and unbalanced problems. A set of experiments is carried out to demonstrate the effectiveness of both methods against other relevant Boosting algorithms in their respective areas. In the experiments we resort to benchmark data sets used in the Machine Learning community, firstly for minimizing classification errors and secondly for minimizing costs. In addition, we successfully applied BAdaCost to a segmentation task, a particular problem in presence of imbalanced data. We conclude the thesis justifying the horizon of future improvements encompassed in our framework, due to its applicability and theoretical flexibility.