899 resultados para Deep-focus earthquake
Resumo:
Observations from three deep focus earthquakes, of focal depth 600 km, originating in the Fiji Islands region, have permitted to trace the travel-time curves for the longitudinal waves travelling through the earth's core. Of principal importance is the travel-time table for the phase PKP1 which has been given upto 158° and for which the data so far were incomplete. Other important phases observed between 103 and 143° have been illustrated and their travel-time curves drawn.
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The existence of fluids and partial melt in the lower crust of the seismically active Kutch rift basin (on the western continental margin of India) owing to underplating has been proposed in previous geological and geophysical studies. This hypothesis is examined using magnetotelluric (MT) data acquired at 23 stations along two profiles across Kutch Mainland Uplift and Wagad Uplift. A detailed upper crustal structure is also presented using twodimensional inversion of MT data in the Bhuj earthquake (2001) area. The prominent boundaries of reflection in the upper crust at 5, 10 and 20 km obtained in previous seismic reflection profiles correlate with conductive structures in our models. The MT study reveals 1-2 km thick Mesozoic sediments under the Deccan trap cover. The Deccan trap thickness in this region varies from a few meters to 1.5 km. The basement is shallow on the northern side compared to the south and is in good agreement with geological models as well as drilling information. The models for these profiles indicate that the thickness of sediments would further increase southwards into the Gulf of Kutch. Significant findings of the present study indicate 1) the hypocentre region of the earthquake is devoid of fluids, 2) absence of melt (that is emplaced during rifting as suggested from the passive seismological studies) in the lower crust and 3) a low resistive zone in the depth range of 5-20 km. The present MT study rules out fluidsand melt (magma) as the causative factors that triggered the Bhuj earthquake. The estimated porosity value of 0.02% will explain 100-500 ohm·m resistivity values observed in the lower crust. Based on the seismic velocities and geochemical studies, presence of garnet is inferred. The lower crust consists of basalts - probably generated by partial melting of metasomatised garnet peridotite at deeper depths in the lithosphere - and their composition might be modified by reaction with the spinel peridotites.
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Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies
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Recoveries after recent earthquakes in the U.S. and Japan have shown that large welfare gains can be achieved by reshaping current emergency plans as incentive-compatible contracts. We apply tools from the mechanisms design literature to show ways to integrate economic incentives into the management of natural disasters and discuss issues related to the application to seismic event recovery. The focus is on restoring lifeline services such as the water, gas, transportation, and electric power networks. We put forward decisional procedures that an uninformed planner could employ to set repair priorities and help to coordinate lifeline firms in the post-earthquake reconstruction.
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AMADEUS is a dexterous subsea robot hand incorporating force and slip contact sensing, using fluid filled tentacles for fingers. Hydraulic pressure variations in each of three flexible tubes (bellows) in each finger create a bending moment, and consequent motion or increase in contact force during grasping. Such fingers have inherent passive compliance, no moving parts, and are naturally depth pressure-compensated, making them ideal for reliable use in the deep ocean. In addition to the mechanical design, development of the hand has also considered closed loop finger position and force control, coordinated finger motion for grasping, force and slip sensor development/signal processing, and reactive world modeling/planning for supervisory `blind grasping¿. Initially, the application focus is for marine science tasks, but broader roles in offshore oil and gas, salvage, and military use are foreseen. Phase I of the project is complete, with the construction of a first prototype. Phase I1 is now underway, to deploy the hand from an underwater robot arm, and carry out wet trials with users.
Resumo:
AMADEUS is a dexterous subsea robot hand incorporating force and slip contact sensing, using fluid filled tentacles for fingers. Hydraulic pressure variations in each of three flexible tubes (bellows) in each finger create a bending moment, and consequent motion or increase in contact force during grasping. Such fingers have inherent passive compliance, no moving parts, and are naturally depth pressure-compensated, making them ideal for reliable use in the deep ocean. In addition to the mechanical design, development of the hand has also considered closed loop finger position and force control, coordinated finger motion for grasping, force and slip sensor development/signal processing, and reactive world modeling/planning for supervisory `blind grasping¿. Initially, the application focus is for marine science tasks, but broader roles in offshore oil and gas, salvage, and military use are foreseen. Phase I of the project is complete, with the construction of a first prototype. Phase I1 is now underway, to deploy the hand from an underwater robot arm, and carry out wet trials with users.
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Basal ganglia and brain stem nuclei are involved in the pathophysiology of various neurological and neuropsychiatric disorders. Currently available structural T1-weighted (T1w) magnetic resonance images do not provide sufficient contrast for reliable automated segmentation of various subcortical grey matter structures. We use a novel, semi-quantitative magnetization transfer (MT) imaging protocol that overcomes limitations in T1w images, which are mainly due to their sensitivity to the high iron content in subcortical grey matter. We demonstrate improved automated segmentation of putamen, pallidum, pulvinar and substantia nigra using MT images. A comparison with segmentation of high-quality T1w images was performed in 49 healthy subjects. Our results show that MT maps are highly suitable for automated segmentation, and so for multi-subject morphometric studies with a focus on subcortical structures.
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In the drilling processes and especially deep-hole drilling process, the monitoring system and having control on mechanical parameters (e.g. Force, Torque,Vibration and Acoustic emission) are essential. The main focus of this thesis work is to study the characteristics of deep-hole drilling process, and optimize the monitoring system for controlling the process. The vibration is considered as a major defect area of the deep-hole drilling process which often leads to breakage of the drill, therefore by vibration analysis and optimizing the workpiecefixture, this area is studied by finite element method and the suggestions are explained. By study on a present monitoring system, and searching on the new sensor products, the modifications and recommendations are suggested for optimize the present monitoring system for excellent performance in deep-hole drilling process research and measurements.
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We present the results of stereoscopic observations of the satellite galaxy Segue 1 with the MAGIC Telescopes, carried out between 2011 and 2013. With almost 160 hours of good-quality data, this is the deepest observational campaign on any dwarf galaxy performed so far in the very high energy range of the electromagnetic spectrum. We search this large data sample for signals of dark matter particles in the mass range between 100 GeV and 20 TeV. For this we use the full likelihood analysis method, which provides optimal sensitivity to characteristic gamma-ray spectral features, like those expected from dark matter annihilation or decay. In particular, we focus our search on gamma-rays produced from different final state Standard Model particles, annihilation with internal bremsstrahlung, monochromatic lines and box-shaped signals. Our results represent the most stringent constraints to the annihilation cross-section or decay lifetime obtained from observations of satellite galaxies, for masses above few hundred GeV. In particular, our strongest limit (95% confidence level) corresponds to a ~ 500 GeV dark matter particle annihilating into τ+τ−, and is of order langleσannvrangle simeq 1.2 × 10−24 cm3 s−1 a factor ~ 40 above the langleσannvrangle simeq thermal value.
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The ventral striatum / nucleus accumbens has been implicated in the craving for drugs and alcohol which is a major reason for relapse of addicted people. Craving might be induced by drug-related cues. This suggests that disruption of craving-related neural activity in the nucleus accumbens may significantly reduce craving in alcohol-dependent patients. Here we report on preliminary clinical and neurophysiological evidence in three male patients who were treated with high frequency deep brain stimulation of the nucleus accumbens bilaterally. All three had been alcohol dependent for many years, unable to abstain from drinking, and had experienced repeated relapses prior to the stimulation. After the operation, craving was greatly reduced and all three patients were able to abstain from drinking for extended periods of time. Immediately after the operation but prior to connection of the stimulation electrodes to the stimulator, local field potentials were obtained from the externalized cables in two patients while they performed cognitive tasks addressing action monitoring and incentive salience of drug related cues. LFPs in the action monitoring task provided further evidence for a role of the nucleus accumbens in goal-directed behaviors. Importantly, alcohol related cue stimuli in the incentive salience task modulated LFPs even though these cues were presented outside of the attentional focus. This implies that cue-related craving involves the nucleus accumbens and is highly automatic.
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L'apprentissage profond est un domaine de recherche en forte croissance en apprentissage automatique qui est parvenu à des résultats impressionnants dans différentes tâches allant de la classification d'images à la parole, en passant par la modélisation du langage. Les réseaux de neurones récurrents, une sous-classe d'architecture profonde, s'avèrent particulièrement prometteurs. Les réseaux récurrents peuvent capter la structure temporelle dans les données. Ils ont potentiellement la capacité d'apprendre des corrélations entre des événements éloignés dans le temps et d'emmagasiner indéfiniment des informations dans leur mémoire interne. Dans ce travail, nous tentons d'abord de comprendre pourquoi la profondeur est utile. Similairement à d'autres travaux de la littérature, nos résultats démontrent que les modèles profonds peuvent être plus efficaces pour représenter certaines familles de fonctions comparativement aux modèles peu profonds. Contrairement à ces travaux, nous effectuons notre analyse théorique sur des réseaux profonds acycliques munis de fonctions d'activation linéaires par parties, puisque ce type de modèle est actuellement l'état de l'art dans différentes tâches de classification. La deuxième partie de cette thèse porte sur le processus d'apprentissage. Nous analysons quelques techniques d'optimisation proposées récemment, telles l'optimisation Hessian free, la descente de gradient naturel et la descente des sous-espaces de Krylov. Nous proposons le cadre théorique des méthodes à région de confiance généralisées et nous montrons que plusieurs de ces algorithmes développés récemment peuvent être vus dans cette perspective. Nous argumentons que certains membres de cette famille d'approches peuvent être mieux adaptés que d'autres à l'optimisation non convexe. La dernière partie de ce document se concentre sur les réseaux de neurones récurrents. Nous étudions d'abord le concept de mémoire et tentons de répondre aux questions suivantes: Les réseaux récurrents peuvent-ils démontrer une mémoire sans limite? Ce comportement peut-il être appris? Nous montrons que cela est possible si des indices sont fournis durant l'apprentissage. Ensuite, nous explorons deux problèmes spécifiques à l'entraînement des réseaux récurrents, à savoir la dissipation et l'explosion du gradient. Notre analyse se termine par une solution au problème d'explosion du gradient qui implique de borner la norme du gradient. Nous proposons également un terme de régularisation conçu spécifiquement pour réduire le problème de dissipation du gradient. Sur un ensemble de données synthétique, nous montrons empiriquement que ces mécanismes peuvent permettre aux réseaux récurrents d'apprendre de façon autonome à mémoriser des informations pour une période de temps indéfinie. Finalement, nous explorons la notion de profondeur dans les réseaux de neurones récurrents. Comparativement aux réseaux acycliques, la définition de profondeur dans les réseaux récurrents est souvent ambiguë. Nous proposons différentes façons d'ajouter de la profondeur dans les réseaux récurrents et nous évaluons empiriquement ces propositions.
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During the management of the crisis after the earthquake occurred in Haiti in 12 January 2010, Brazil played an important role on the efforts of humanitarian assistance. Based on bibliography and documents the paper presents the role played by Brazil with the focus on the humanitarian assistance as part of country’ foreign policy as an emerging power to increase the presence into the international system. To achieve this goal the article presents some considerations about emerging powers, foreign policy and theoretical concepts about humanitarian assistance and international relations, the extension of the earthquake in Haiti and the actions performed by Brazil during the response phase of the crisis management.