Integration of local-scale hydrological and regional-scale geophysical based on a nonlinear Bayesian sequential simulation approach

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending the corresponding approaches to the regional scale represents a major, and as-of-yet largely unresolved, challenge. To address this problem, we have developed a downscaling procedure based on a non-linear Bayesian sequential simulation approach. The basic objective of this algorithm is to estimate the value of the sparsely sampled hydraulic conductivity at non-sampled locations based on its relation to the electrical conductivity, which is available throughout the model space. The in situ relationship between the hydraulic and electrical conductivities is described through a non-parametric multivariate kernel density function. This method is then applied to the stochastic integration of low-resolution, re- gional-scale electrical resistivity tomography (ERT) data in combination with high-resolution, local-scale downhole measurements of the hydraulic and electrical conductivities. Finally, the overall viability of this downscaling approach is tested and verified by performing and comparing flow and transport simulation through the original and the downscaled hydraulic conductivity fields. Our results indicate that the proposed procedure does indeed allow for obtaining remarkably faithful estimates of the regional-scale hydraulic conductivity structure and correspondingly reliable predictions of the transport characteristics over relatively long distances.

Improving sampling, optimization and feature extraction in Boltzmann machines

L’apprentissage supervisé de réseaux hiérarchiques à grande échelle connaît présentement un succès fulgurant. Malgré cette effervescence, l’apprentissage non-supervisé représente toujours, selon plusieurs chercheurs, un élément clé de l’Intelligence Artificielle, où les agents doivent apprendre à partir d’un nombre potentiellement limité de données. Cette thèse s’inscrit dans cette pensée et aborde divers sujets de recherche liés au problème d’estimation de densité par l’entremise des machines de Boltzmann (BM), modèles graphiques probabilistes au coeur de l’apprentissage profond. Nos contributions touchent les domaines de l’échantillonnage, l’estimation de fonctions de partition, l’optimisation ainsi que l’apprentissage de représentations invariantes. Cette thèse débute par l’exposition d’un nouvel algorithme d'échantillonnage adaptatif, qui ajuste (de fa ̧con automatique) la température des chaînes de Markov sous simulation, afin de maintenir une vitesse de convergence élevée tout au long de l’apprentissage. Lorsqu’utilisé dans le contexte de l’apprentissage par maximum de vraisemblance stochastique (SML), notre algorithme engendre une robustesse accrue face à la sélection du taux d’apprentissage, ainsi qu’une meilleure vitesse de convergence. Nos résultats sont présent ́es dans le domaine des BMs, mais la méthode est générale et applicable à l’apprentissage de tout modèle probabiliste exploitant l’échantillonnage par chaînes de Markov. Tandis que le gradient du maximum de vraisemblance peut-être approximé par échantillonnage, l’évaluation de la log-vraisemblance nécessite un estimé de la fonction de partition. Contrairement aux approches traditionnelles qui considèrent un modèle donné comme une boîte noire, nous proposons plutôt d’exploiter la dynamique de l’apprentissage en estimant les changements successifs de log-partition encourus à chaque mise à jour des paramètres. Le problème d’estimation est reformulé comme un problème d’inférence similaire au filtre de Kalman, mais sur un graphe bi-dimensionnel, où les dimensions correspondent aux axes du temps et au paramètre de température. Sur le thème de l’optimisation, nous présentons également un algorithme permettant d’appliquer, de manière efficace, le gradient naturel à des machines de Boltzmann comportant des milliers d’unités. Jusqu’à présent, son adoption était limitée par son haut coût computationel ainsi que sa demande en mémoire. Notre algorithme, Metric-Free Natural Gradient (MFNG), permet d’éviter le calcul explicite de la matrice d’information de Fisher (et son inverse) en exploitant un solveur linéaire combiné à un produit matrice-vecteur efficace. L’algorithme est prometteur: en terme du nombre d’évaluations de fonctions, MFNG converge plus rapidement que SML. Son implémentation demeure malheureusement inefficace en temps de calcul. Ces travaux explorent également les mécanismes sous-jacents à l’apprentissage de représentations invariantes. À cette fin, nous utilisons la famille de machines de Boltzmann restreintes “spike & slab” (ssRBM), que nous modifions afin de pouvoir modéliser des distributions binaires et parcimonieuses. Les variables latentes binaires de la ssRBM peuvent être rendues invariantes à un sous-espace vectoriel, en associant à chacune d’elles, un vecteur de variables latentes continues (dénommées “slabs”). Ceci se traduit par une invariance accrue au niveau de la représentation et un meilleur taux de classification lorsque peu de données étiquetées sont disponibles. Nous terminons cette thèse sur un sujet ambitieux: l’apprentissage de représentations pouvant séparer les facteurs de variations présents dans le signal d’entrée. Nous proposons une solution à base de ssRBM bilinéaire (avec deux groupes de facteurs latents) et formulons le problème comme l’un de “pooling” dans des sous-espaces vectoriels complémentaires.

Application of Gaussian multi-scale representation to feature tracking in meteorological satellite imagery

Gaussian multi-scale representation is a mathematical framework that allows to analyse images at different scales in a consistent manner, and to handle derivatives in a way deeply connected to scale. This paper uses Gaussian multi-scale representation to investigate several aspects of the derivation of atmospheric motion vectors (AMVs) from water vapour imagery. The contribution of different spatial frequencies to the tracking is studied, for a range of tracer sizes, and a number of tracer selection methods are presented and compared, using WV 6.2 images from the geostationary satellite MSG-2.

A Parallel Hardware Architecture for Scale and Rotation Invariant Feature Detection

This paper proposes a parallel hardware architecture for image feature detection based on the Scale Invariant Feature Transform algorithm and applied to the Simultaneous Localization And Mapping problem. The work also proposes specific hardware optimizations considered fundamental to embed such a robotic control system on-a-chip. The proposed architecture is completely stand-alone; it reads the input data directly from a CMOS image sensor and provides the results via a field-programmable gate array coupled to an embedded processor. The results may either be used directly in an on-chip application or accessed through an Ethernet connection. The system is able to detect features up to 30 frames per second (320 x 240 pixels) and has accuracy similar to a PC-based implementation. The achieved system performance is at least one order of magnitude better than a PC-based solution, a result achieved by investigating the impact of several hardware-orientated optimizations oil performance, area and accuracy.

Insights of meso and micro-scale processes for the Caxiuanã forest region from high resolution simulation

Dados meteorológicos e simulações numéricas de alta resolução foram usados para estimar campos espaciais na região leste da Amazônia onde se situam a Floresta e a Baía de Caxiuanã, no Estado do Pará. O estudo foi feito para o período de Novembro de 2006, quando foi realizado o experimento de campo COBRA-PARÁ. Análises de imagens do sensor MODIS mostram a ocorrência de vários fenômenos locais como avenidas de nuvens, sistemas convectivos precipitantes, e importante influência das interfaces entre a floresta e as superfícies aquáticas. Simulações numéricas para o dia 7 de novembro de 2006 mostraram que o modelo representou bem as principais variáveis meteorológicas. Os resultados mostram que a Baía de Caxiuanã provoca importante impacto nos campos meteorológicos adjacentes, principalmente, através da advecção pelos ventos de nordeste que induzem a temperaturas do dossel mais frias a oeste da baía. Simulações de alta resolução (LES) produziram padrões espaciais de temperatura e umidade alinhados com os ventos durante o período diurno e mudanças noturnas causadas principalmente pela presença da baía e chuvas convectivas. Correlações espaciais entre os ventos de níveis médios e os fluxos verticais de calor latente mostraram que existe uma mudança de correlações negativas para as primeiras horas do dia passando para correlações positivas para o período da tarde e início da noite.

Using landscape metrics to analyze micro-scale soil erosion processes

Methods of recording soil erosion using photographs exist but they are not commonly considered in scientific studies. Digital images may hold an expressive amount of information that can be extracted quickly in different manners. The investigation of several metrics that were initially developed for landscape ecology analysis constitutes one method. In this study we applied a method of landscape metrics to quantify the spatial configuration of surface micro-topography and erosion-related features, in order to generate a possible complementary tool for environmental management. In a 3.7 m wide and 9.7 m long soil box used during a rainfall simulation study, digital images were systematically acquired in four instances: (a) when the soil was dry; (b) after a short duration rain for initial wetting; (c) after the first erosive rain; and (d) after the 2nd erosive rain. Thirteen locations were established in the box and digital photos were taken at these locations with the camera positioned at the same orthogonal distance from the soil surface under the same ambient light intensity. Digital photos were converted into bimodal images and seven landscape metrics were analyzed: percentage of land, number of patches, density of patches, largest patch index, edge density, shape index, and fractal dimension. Digital images were an appropriate tool because they can generate data very quickly. The landscape metrics were sensitive to changes in soil surface micro-morphology especially after the 1st erosive rain event, indicating significant erosional feature development between the initial wetting and first erosive rainfall. The method is considered suitable for spatial patterns of soil micro-topography evolution from rainfall events that bear similarity to landscape scale pattern evolution from eco-hydrological processes. Although much more study is needed for calibrating the landscape metrics at the micro-scale, this study is a step forward in demonstrating the advantages of the method.

Feature-oriented regional modeling and simulations (FORMS) for the western South Atlantic: Southeastern Brazil region

The multi-scale synoptic circulation system in the southeastern Brazil (SEBRA) region is presented using a feature-oriented approach. Prevalent synoptic circulation structures, or ""features,"" are identified from previous observational studies. These features include the southward-flowing Brazil Current (BC), the eddies off Cabo Sao Tome (CST - 22 degrees S) and off Cabo Frio (CF - 23 degrees S), and the upwelling region off CF and CST. Their synoptic water-mass (T-S) structures are characterized and parameterized to develop temperature-salinity (T-S) feature models. Following [Gangopadhyay, A., Robinson, A.R., Haley, PJ., Leslie, W.J., Lozano, C.j., Bisagni, J., Yu, Z., 2003. Feature-oriented regional modeling and simulation (forms) in the gulf of maine and georges bank. Cont. Shelf Res. 23 (3-4), 317-353] methodology, a synoptic initialization scheme for feature-oriented regional modeling and simulation (FORMS) of the circulation in this region is then developed. First, the temperature and salinity feature-model profiles are placed on a regional circulation template and objectively analyzed with available background climatology in the deep region. These initialization fields are then used for dynamical simulations via the Princeton Ocean Model (POM). A few first applications of this methodology are presented in this paper. These include the BC meandering, the BC-eddy interaction and the meander-eddy-upwelling system (MEUS) simulations. Preliminary validation results include realistic wave-growth and eddy formation and sustained upwelling. Our future plan includes the application of these feature models with satellite, in-situ data and advanced data-assimilation schemes for nowcasting and forecasting the SEBRA region. (c) 2008 Elsevier Ltd. All rights reserved.

Millennial-scale climate cycles in Permian-Carboniferous rhythmites: Permanent feature throughout geologic time?

Two late Paleozoic glacial rhythmite successions from the Itarare Group (Parana Basin, Brazil) were examined for paleoclimate variations. Paleomagnetic (characteristic remanent magnetization, ChRM) and magnetic susceptibility (K(z)) measurements taken from the rhythmites are interpreted as paleoclimatic proxies. Ratios of low-frequency components in the K(z) variations suggest Milankovitch periodicities; this leads to recognition of other, millennial-scale variations reminiscent of abrupt climate changes during late Quaternary time, and are suggestive of Bond cycles and the 2.4 k.y. solar cycle. We infer from these patterns that millennial-scale climate change is not restricted to the Quaternary Period, and that millennial forcing mechanisms may have been prevalent throughout geologic time.

Finite element simulation of a local scale air quality model over complex terrain

[EN]In this paper we propose a finite element method approach for modelling the air quality in a local scale over complex terrain. The area of interest is up to tens of kilometres and it includes pollutant sources. The proposed methodology involves the generation of an adaptive tetrahedral mesh, the computation of an ambient wind field, the inclusion of the plume rise effect in the wind field, and the simulation of transport and reaction of pollutants. We apply our methodology to simulate a fictitious pollution episode in La Palma island (Canary Island, Spain)...

Micro-simulation models at neighbourhood scale: the interplay of agents, buildings, activities and transport.

Urban systems consist of several interlinked sub-systems - social, economic, institutional and environmental – each representing a complex system of its own and affecting all the others at various structural and functional levels. An urban system is represented by a number of “human” agents, such as individuals and households, and “non-human” agents, such as buildings, establishments, transports, vehicles and infrastructures. These two categories of agents interact among them and simultaneously produce impact on the system they interact with. Try to understand the type of interactions, their spatial and temporal localisation to allow a very detailed simulation trough models, turn out to be a great effort and is the topic this research deals with. An analysis of urban system complexity is here presented and a state of the art review about the field of urban models is provided. Finally, six international models - MATSim, MobiSim, ANTONIN, TRANSIMS, UrbanSim, ILUTE - are illustrated and then compared.

Scale bridging concepts in molecular simulation : coarse-graining and thermodynamic coupling

In dieser Arbeit wurden Simulation von Flüssigkeiten auf molekularer Ebene durchgeführt, wobei unterschiedliche Multi-Skalen Techniken verwendet wurden. Diese erlauben eine effektive Beschreibung der Flüssigkeit, die weniger Rechenzeit im Computer benötigt und somit Phänomene auf längeren Zeit- und Längenskalen beschreiben kann.rnrnEin wesentlicher Aspekt ist dabei ein vereinfachtes (“coarse-grained”) Modell, welches in einem systematischen Verfahren aus Simulationen des detaillierten Modells gewonnen wird. Dabei werden ausgewählte Eigenschaften des detaillierten Modells (z.B. Paar-Korrelationsfunktion, Druck, etc) reproduziert.rnrnEs wurden Algorithmen untersucht, die eine gleichzeitige Kopplung von detaillierten und vereinfachten Modell erlauben (“Adaptive Resolution Scheme”, AdResS). Dabei wird das detaillierte Modell in einem vordefinierten Teilvolumen der Flüssigkeit (z.B. nahe einer Oberfläche) verwendet, während der Rest mithilfe des vereinfachten Modells beschrieben wird.rnrnHierzu wurde eine Methode (“Thermodynamische Kraft”) entwickelt um die Kopplung auch dann zu ermöglichen, wenn die Modelle in verschiedenen thermodynamischen Zuständen befinden. Zudem wurde ein neuartiger Algorithmus der Kopplung beschrieben (H-AdResS) der die Kopplung mittels einer Hamilton-Funktion beschreibt. In diesem Algorithmus ist eine zur Thermodynamischen Kraft analoge Korrektur mit weniger Rechenaufwand möglich.rnrnAls Anwendung dieser grundlegenden Techniken wurden Pfadintegral Molekulardynamik (MD) Simulationen von Wasser untersucht. Mithilfe dieser Methode ist es möglich, quantenmechanische Effekte der Kerne (Delokalisation, Nullpunktsenergie) in die Simulation einzubeziehen. Hierbei wurde zuerst eine Multi-Skalen Technik (“Force-matching”) verwendet um eine effektive Wechselwirkung aus einer detaillierten Simulation auf Basis der Dichtefunktionaltheorie zu extrahieren. Die Pfadintegral MD Simulation verbessert die Beschreibung der intra-molekularen Struktur im Vergleich mit experimentellen Daten. Das Modell eignet sich auch zur gleichzeitigen Kopplung in einer Simulation, wobei ein Wassermolekül (beschrieben durch 48 Punktteilchen im Pfadintegral-MD Modell) mit einem vereinfachten Modell (ein Punktteilchen) gekoppelt wird. Auf diese Weise konnte eine Wasser-Vakuum Grenzfläche simuliert werden, wobei nur die Oberfläche im Pfadintegral Modell und der Rest im vereinfachten Modell beschrieben wird.

Testing of subgrid scale (SGS) models for large-eddy simulation (LES) of turbulent channel flow

Sub-grid scale (SGS) models are required in order to model the influence of the unresolved small scales on the resolved scales in large-eddy simulations (LES), the flow at the smallest scales of turbulence. In the following work two SGS models are presented and deeply analyzed in terms of accuracy through several LESs with different spatial resolutions, i.e. grid spacings. The first part of this thesis focuses on the basic theory of turbulence, the governing equations of fluid dynamics and their adaptation to LES. Furthermore, two important SGS models are presented: one is the Dynamic eddy-viscosity model (DEVM), developed by \cite{germano1991dynamic}, while the other is the Explicit Algebraic SGS model (EASSM), by \cite{marstorp2009explicit}. In addition, some details about the implementation of the EASSM in a Pseudo-Spectral Navier-Stokes code \cite{chevalier2007simson} are presented. The performance of the two aforementioned models will be investigated in the following chapters, by means of LES of a channel flow, with friction Reynolds numbers $Re_\tau=590$ up to $Re_\tau=5200$, with relatively coarse resolutions. Data from each simulation will be compared to baseline DNS data. Results have shown that, in contrast to the DEVM, the EASSM has promising potentials for flow predictions at high friction Reynolds numbers: the higher the friction Reynolds number is the better the EASSM will behave and the worse the performances of the DEVM will be. The better performance of the EASSM is contributed to the ability to capture flow anisotropy at the small scales through a correct formulation for the SGS stresses. Moreover, a considerable reduction in the required computational resources can be achieved using the EASSM compared to DEVM. Therefore, the EASSM combines accuracy and computational efficiency, implying that it has a clear potential for industrial CFD usage.

Coupling biomechanics to a cellular level model: An approach to patient-specific image driven multi-scale and multi-physics tumor simulation

Modeling of tumor growth has been performed according to various approaches addressing different biocomplexity levels and spatiotemporal scales. Mathematical treatments range from partial differential equation based diffusion models to rule-based cellular level simulators, aiming at both improving our quantitative understanding of the underlying biological processes and, in the mid- and long term, constructing reliable multi-scale predictive platforms to support patient-individualized treatment planning and optimization. The aim of this paper is to establish a multi-scale and multi-physics approach to tumor modeling taking into account both the cellular and the macroscopic mechanical level. Therefore, an already developed biomodel of clinical tumor growth and response to treatment is self-consistently coupled with a biomechanical model. Results are presented for the free growth case of the imageable component of an initially point-like glioblastoma multiforme tumor. The composite model leads to significant tumor shape corrections that are achieved through the utilization of environmental pressure information and the application of biomechanical principles. Using the ratio of smallest to largest moment of inertia of the tumor material to quantify the effect of our coupled approach, we have found a tumor shape correction of 20\% by coupling biomechanics to the cellular simulator as compared to a cellular simulation without preferred growth directions. We conclude that the integration of the two models provides additional morphological insight into realistic tumor growth behavior. Therefore, it might be used for the development of an advanced oncosimulator focusing on tumor types for which morphology plays an important role in surgical and/or radio-therapeutic treatment planning.

Tropopause level Rossby wave breaking in the Northern Hemisphere: a feature-based validation of the ECHAM5-HAM climate model

Breaking synoptic-scale Rossby waves (RWB) at the tropopause level are central to the daily weather evolution in the extratropics and the subtropics. RWB leads to pronounced meridional transport of heat, moisture, momentum, and chemical constituents. RWB events are manifest as elongated and narrow structures in the tropopause-level potential vorticity (PV) field. A feature-based validation approach is used to assess the representation of Northern Hemisphere RWB in present-day climate simulations carried out with the ECHAM5-HAM climate model at three different resolutions (T42L19, T63L31, and T106L31) against the ERA-40 reanalysis data set. An objective identification algorithm extracts RWB events from the isentropic PV field and allows quantifying the frequency of occurrence of RWB. The biases in the frequency of RWB are then compared to biases in the time mean tropopause-level jet wind speeds. The ECHAM5-HAM model captures the location of the RWB frequency maxima in the Northern Hemisphere at all three resolutions. However, at coarse resolution (T42L19) the overall frequency of RWB, i.e. the frequency averaged over all seasons and the entire hemisphere, is underestimated by 28%.The higher-resolution simulations capture the overall frequency of RWB much better, with a minor difference between T63L31 and T106L31 (frequency errors of −3.5 and 6%, respectively). The number of large-size RWB events is significantly underestimated by the T42L19 experiment and well represented in the T106L31 simulation. On the local scale, however, significant differences to ERA-40 are found in the higher-resolution simulations. These differences are regionally confined and vary with the season. The most striking difference between T106L31 and ERA-40 is that ECHAM5-HAM overestimates the frequency of RWB in the subtropical Atlantic in all seasons except for spring. This bias maximum is accompanied by an equatorward extension of the subtropical westerlies.