L´étude des aérosols de la stratosphère est primordiale pour modéliser précisément le bilan radiatif terrestre, et pour évaluer l´influence des particules sur le cycle de destruction de l´ozone. Depuis la découverte de la couche de Junge, ce domaine de recherche connaît différents décors, du plus important contenu en aérosols du dernier siècle après l´éruption du Mont Pinatubo en 1991, à un rétablissement vers les faibles niveaux atteints dans les années 2000, qui permet l´étude des particules autres que celles d´origine volcanique. Cependant, à ce jour, le degré de connaissance est faible quant à la distribution spatiale et verticale de ces aérosols dans la moyenne et haute stratosphère. Leur détection présente plusieurs difficultés: les particules ont une grande variété d´origines, compositions, tailles et formes, et leurs faibles épaisseurs optiques rendent indispensables des résultats précis. Un algorithme d´inversion développé au LPC2E a été adapté à l´analyse des données de niveau 1b de l´instrument GOMOS à bord d´ENVISAT, qui emploie la technique d´occultation stellaire, et fournit une bonne (mais irrégulière) couverture géographique et temporelle des mesures; un critère de sélection est d´ailleurs nécessaire du fait de l´utilisation de sources lumineuses de propriétés différentes. La méthode mise au point est validée pour l´étude de l´extinction induite par les aérosols; une climatologie globale est alors établie pour la période allant d´août 2002 à juillet 2006, et indique la présence permanente de particules dans l´ensemble du globe, jusqu´à environ 45 km d´altitude. La variabilité temporelle de l´extinction montre une augmentation progressive du contenu moyen depuis 2002 aux latitudes tropicales dans la basse stratosphère, et a permis d´évaluer l´effet de l´oscillation quasi-biennale et d´étudier d´autres variations saisonnières. La dépendance spectrale permet de déduire certaines spécificités concernant la taille et la nature des aérosols, majoritairement des particules sulfatées, mais également des suies en provenance de la troposphère et des particules d´origine interplanétaire. / The study of stratospheric aerosols is crucial for modeling precisely the earth´s radiative budget and because of their influence on ozone depletion. Since the discovery of Junge layer, this area of research has been through various situations: from the greatest volcanic upload of last century after Mount Pinatubo eruption in 1991, and slowly recovering to background levels reached in the 2000s, which allow the study of other than volcanic particles. However, the vertical and spatial distribution of these aerosols in the middle and high stratosphere is still poorly documented and not yet totally understood. Their detection presents many difficulties: the particles have a great variety of origins, compositions, shapes and sizes, and their low optical thicknesses make accurate results necessary. An inversion algorithm developed in the LPC2E has been adapted to the analysis of level 1b data from GOMOS instrument onboard ENVISAT. The star occultation technique leads to a good (but irregular) spatial and temporal sampling, and a data selection criteria allows the analysis of accurate results, which validation is led for the study of aerosol extinction. A global climatology is then established for the August 2002 to July 2006 period, and shows the permanent presence of aerosol particles around the globe, up to 45 km altitude. The temporal variability shows a progressive enhancement of the mean content from 2002 in the tropics, and was useful to study the influence of the quasi-biennial oscillation in the middle stratosphere, as well as some seasonal features. The study of the spectral dependence informs about the size and nature of the particles, mainly sulfate aerosols, but also soot coming from the troposphere and aerosols of extra-terrestrial origin.

Magnetic Resonance Elastography (MRE) is an emerging medical imaging modality that allows quantification of the mechanical properties of biological tissues in vivo. MRE typically involves time-harmonic tissue excitation followed by the displacement measurements within the tissue obtained by phase-contrast Magnetic Resonance Imaging (MRI) techniques. MRE is believed to have great potential in the detection of wide variety of pathologies, diseases and cancer formations, especially tumors. This thesis concentrates on a thorough assessment and full rheological evaluation of the Rayleigh damping (RD) material model applied to MRE. The feasibility of the RD model to accurately reconstruct viscoelastic and damping properties was assessed. The goal is to obtain accurate quantitative estimates of the mechanical properties for the in vivo healthy brain via the subzone optimization based nonlinear image reconstruction algorithm. The RD model allows reconstruction of not only stiffness distribution of the tissue, but also energy attenuation mechanisms proportionally related to both elastic and inertial effects. The latter allows calculation of the concomitant damping properties of the material. The initial hypothesis behind this research is that accurate reconstruction of the Rayleigh damping parameters may bring additional diagnostic potential with regards to differentiation of various tissue types and more accurate characterisation of certain pathological diseases based on different energy absorbing mechanisms. Therefore, the RD model offers reconstruction of three additional material properties that might be of clinical diagnostic merit and can enhance characterisation of cancer tumors within the brain. A pneumatic-based actuator was specifically developed for in vivo human brain MRE experiments. Performance of the actuator was investigated and the results showed that the actuator produces average displacement in the range of 300 µmicrons and is well suited for generation of shear waves if applied to the human head. Unique features of the the actuator are patient comfort and safety, MRI compatibility, flexible design and good displacement characteristics. In this research, a 3D finite element (FE) subzone-based non-linear reconstruction algorithm using the RD material model has been applied and rigorously assessed to investigate the performance of elastographic based reconstruction to accurately recover mechanical properties and a concomitant damping behaviour of the material. A number of experiments were performed on a variety of homogenous and heterogeneous tissue-simulating damping phantoms comprising a set of materials that mimic range of mechanical properties expected in the brain. The result showed consistent effect of a poor reconstruction accuracy of the RD parameters which suggested the nonidentifiable nature of the RD model. A structural model identifiability analysis further supported the nonidentifiabilty of the RD parameters at a single frequency. Therefore, two approaches were developed to overcome the fundamental identifiability issue. The first one involved application of multiple frequencies over a broad range. The second one was based on parametrisation techniques, where one of the damping parameters was globally defined throughout the reconstruction domain allowing reconstruction of the two remaining parameters. Based on the findings of this research, multi-frequency (MF) elastography was performed on the tissue-simulating phantoms to investigate improvement of the elastographic reconstruction accuracy. Dispersion characteristics of the materials as well as RD changes across different frequencies in various materials were also studied. Simultaneous multi-frequency inversion was undertaken where two models were evaluated: a zero-order model and a power-law model. Furthermore, parametric-based RD reconstruction was carried out to evaluate enhancement of accurate identification of the reconstructed parameters. The results showed that parametric-based RD reconstruction, compared to MF-based RD results, allowed better material characterisation on the reconstructed shear modulus image. Also, significant improvement in material differentiation on the remaining damping parameter image was also observed if the fixed damping parameter was adjusted appropriately. In application to in vivo brain imaging, six repetitive MRE examinations of the in vivo healthy brain demonstrated promising ability of the RD MRE to resolve local variations in mechanical properties of different brain tissue types. Preliminary results to date show that reconstructed real shear modulus and overall damping levels correlate well with the brain anatomical features. Quantified shear stiffness estimates for white and gray matter were found to be 3 kPa and 2.1 kPa, respectively. Due to the non-identifiability of the model at a single frequency, reconstructed RD based parameters limit any physical meaning. Therefore, MF-based and parametric-based cerebral RD elastography was also performed.

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Expanding the standard, single-well recording geometry used to monitor seismicity during hydro-fracture treatments could provide more accurate hypocenter locations and seismic velocities, improving general reservoir characterization. However, for the real, two-well data set obtained for this project, only S-wave picks were available, and testing resulted in anomalous hypocenter location behavior. This study uses a hypocenter location algorithm and both real and synthetic data sets to investigate how the accuracy of the velocity model, starting hypocenter location, recording geometry, and arrival-time picking error affect final hypocenter locations. Hypocenter locations improved using a velocity model that closely matched the observed sonic log rather than a smoothed version of this model. The starting hypocenter location did not affect the final location solution if both starting and final locations were between the wells. Two solutions were possible when the true solution was not directly between the wells. Adding realistic random picking errors to synthetic data closely modeled the dispersed hypocenter error pattern observed in the real data results. Adding data from a third well to synthetic tests dramatically reduced location error and removed horizontal geometric bias observed in the two-well case. Seismic event data recorded during hydro-fracture treatments could potentially be used for three-dimensional joint hypocenter-velocity tomography. This would require observation wells close enough to earthquakes to record P- and S-wave arrivals or wells at orientations sufficient to properly triangulate hypocenter locations. Simulating results with synthetic tests before drilling could optimize survey design to collect data more effectively and make analysis more useful. / Master of Science

The Ku band microwave remote sensor, SeaWinds, was developed at the National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL). Two identical SeaWinds instruments were launched into space. The first was flown onboard NASA QuikSCAT satellite which has been orbiting the Earth since June 1999, and the second instrument flew onboard the Japanese Advanced Earth Observing Satellite II (ADEOS-II) from December 2002 till October 2003 when an irrecoverable solar panel failure caused a premature end to the ADEOS-II satellite mission. SeaWinds operates at a frequency of 13.4 GHz, and was originally designed to measure the speed and direction of the ocean surface wind vector by relating the normalized radar backscatter measurements to the near surface wind vector through a geophysical model function (GMF). In addition to the backscatter measurement capability, SeaWinds simultaneously measures the polarized radiometric emission from the surface and atmosphere, utilizing a ground signal processing algorithm known as the QuikSCAT / SeaWinds Radiometer (QRad / SRad). This dissertation presents the development and validation of a mathematical inversion algorithm that combines the simultaneous active radar backscatter and the passive microwave brightness temperatures observed by the SeaWinds sensor to retrieve the oceanic rainfall. The retrieval algorithm is statistically based, and has been developed using collocated measurements from SeaWinds, the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) rain rates, and Numerical Weather Prediction (NWP) wind fields from the National Centers for Environmental Prediction (NCEP). The oceanic rain is retrieved on a spacecraft wind vector cell (WVC) measurement grid that has a spatial resolution of 25 km. To evaluate the accuracy of the retrievals, examples of the passive-only, as well as the combined active / passive rain estimates from SeaWinds are presented, and comparisons are made with the standard TRMM rain data products. Results demonstrate that SeaWinds rain measurements are in good agreement with the independent microwave rain observations obtained from TMI. Further, by applying a threshold on the retrieved rain rates, SeaWinds rain estimates can be utilized as a rain flag. In order to evaluate the performance of the SeaWinds flag, comparisons are made with the Impact based Multidimensional Histogram (IMUDH) rain flag developed by JPL. Results emphasize the powerful rain detection capabilities of the SeaWinds retrieval algorithm. Due to its broad swath coverage, SeaWinds affords additional independent sampling of the oceanic rainfall, which may contribute to the future NASA's Precipitation Measurement Mission (PMM) objectives of improving the global sampling of oceanic rain within 3 hour windows. Also, since SeaWinds is the only sensor onboard QuikSCAT, the SeaWinds rain estimates can be used to improve the flagging of rain-contaminated oceanic wind vector retrievals. The passive-only rainfall retrieval algorithm (QRad / SRad) has been implemented by JPL as part of the level 2B (L2B) science data product, and can be obtained from the Physical Oceanography Distributed Data Archive (PO.DAAC).

Near field imaging using microwaves in medical applications is of great current interest for its capability and accuracy in identifying features of interest, in comparison with other known screening tools. Many imaging methods have been developed over the past two decades showing the potential of microwave imaging in medical applications such as early breast cancer detection, analysis of cardiac tissues, soft tissues and bones. Microwave imaging uses non-ionizing ultra wideband (UWB) electromagnetic signals and utilises tissue-dependent dielectric contrast to reconstruct signals and images using radar-based or tomographic imaging techniques. Microwave imaging offers low health risk, low operational cost, ease of use and user-friendliness. This study documents microwave imaging experiments for early breast cancer detection and bone fracture detection using radar approach. An actively tuned UWB patch antenna and a UWB Vivaldi antenna are designed and utilised as sensing elements in the aforementioned applications. Both UWB antennas were developed over a range of frequency spectrum, and then characteristics were tested against their ability for microwave imaging applications by reconstructing the 3D Inversion Algorithm. An experiment was conducted using patch antenna to test the detection of variable sizes of cancer tissues based on a simple phantom consisting of a plastic container with a low dielectric material emulating fatty tissue and high dielectric constant object emulating a tumour, is scanned between 4 to 8 GHz with the patch antenna. A 2-D image of the tumour is constructed using the reflected signal response to visualize the location and size of the tumour. A Vivaldi antenna is designed covering 3.1 to 10.6 GHz. The antenna is tested via simulation for detecting bone fractures of various sizes and 2-D images are generated using reflected pulses to show the size of fracture. The Vivaldi antenna is optimised for early breast cancer detection and detailed simulated study is carried out using different breast phantoms and tumour sizes. Simulations are backed with the experimental investigation with the test setup used for patch antenna. Generated images for simulations and experimental investigation show good agreement, and show the presence of tumour with good location accuracy. Measurements indicate that both prototype microwave sensors are good candidates for tested imaging applications.

The dynamic wave model (the complete form of the saint-Venant equations), as applied to flow routing in irrigation canals or flood routing in natural channels, is associated with parameter and model uncertainties. The parameter uncertainty arises due to imprecision in the estimation of Manning’s n used for calculating the friction slope (sf) in the momentum equation of the dynamic wave model. Accurate estimation of n is difficult due to its dependence on several channel and flow characteristics. The model uncertainty of the dynamic wave model arises due to difficulty in applying the momentum equation to curved channels, as it is a vector equation. The one-dimensional form of the momentum equation is derived assuming that the longitudinal axis of the channel is a straight line, so that the net force vector is equal to the algebraic sum of the forces involved. Curved channel reaches have to be discretized into small straight sub-reaches while applying the momentum equation. Otherwise, two- or three-dimensional forms of the momentum equation need to be adopted. A main objective of the study presented in the thesis is to develop a fuzzy dynamic wave model (FDWM), which is capable of overcoming the parameter and model uncertainties of the dynamic wave model mentioned above, specifically for problems of flow routing in irrigation canals and flood routing in natural channels. It has been demonstrated earlier in literature that the problem of parameter uncertainty in infiltration models can be addressed by replacing the momentum equation by a fuzzy rule based model while retaining the continuity equation in its complete form. The FDWM is developed by adopting the same methodology: i.e. By replacing the momentum equation of the dynamic wave model by a fuzzy rule based model while retaining the continuity equation in its complete form. The fuzzy rule based model is developed based on fuzzification of a new equation for wave velocity, to account for the model uncertainty and backwater effects. A fuzzy dynamic wave routing model (FDWRM) is developed based on application of the FDWM to flow routing in irrigation canals. The fuzzy rule based model is developed based on the observation that inertia dominated gravity wave predominates in irrigation canal flows. Development of the FDWRM and the method of computation are explained. The FDWRM is tested by applying it to cases of hypothetical flow routing in a wide rectangular channel and also to a real case of flow routing in a field canal. For the cases of hypothetical flow routing in the wide rectangular channel, the FDWRM results match well with those of an implicit numerical model (INM), which solves the dynamic wave model; but the accuracy of the results reduces with increase in backwater effects. For the case of flow routing in the field canal, the FDWRM outputs match well with measured data and also are much better than those of the INM. A fuzzy dynamic flood routing model (FDFRM) is developed based on application of the FDWM to flood routing in natural channels. The fuzzy rule based model is developed based on the observation that monoclinal waves prevail during floods in natural channels. The natural channel reach is discredited into a number of approximately uniform sub-reaches and the fuzzy rule based model for each sub-reach is obtained using the discharge (q)–area (a) relationship at its mean section, based on the kleitz-seddon principle. Development of the FDFRM and the method of computation are explained. The FDFRM is tested by applying it to cases of flood routing in fictitious channels and to flood routing in a natural channel, which is described in the HEC-RAS (hydrologic engineering center – river analysis system) application guide. For the cases of flood routing in the fictitious channels, the FDFRM outputs match well with the INM results. For the case of flood routing in the natural channel, optimized fuzzy rule based models are derived using a neuro-fuzzy algorithm, to take the heterogeneity of the channel sub-reaches into account. The resulting FDFRM outputs are found to be comparable to the HEC-RAS outputs. Also, in literature, the dynamic wave model has been applied in the inverse direction for the development of centralized control algorithms for irrigation canals. In the present study, a centralized control algorithm based on inversion of the fuzzy dynamic wave model (FDWM) is developed to overcome the drawbacks of the existing centralized control algorithms. A fuzzy logic based dynamic wave model inversion algorithm (FDWMIA) is developed for this purpose, based on the inversion of the FDWM. The FDWMIA is tested by applying it to two canal control problems reported in literature: the first problem deals with water level control in a fictitious canal with a single pool and the second, with water level control in a real canal with a series of pools (ASCE Test Canal 2). In both cases, the FDWMIA results are comparable to those of the existing centralized control algorithms.