The initial question addressed in this thesis is how to take into account the multi-objective aspect of decision problems in inverse optimization. The most straightforward extension consists of finding a minimal adjustment of the objective functions coefficients such that a given feasible solution becomes efficient. However, there is not only a single question raised by inverse multi-objective optimization, because there is usually not a single efficient solution. The way we define inverse multi-objective<p>optimization takes into account this important aspect. This gives rise to many questions which are identified by a precise notation that highlights a large collection of inverse problems that could be investigated. In this thesis, a selection of inverse problems are presented and solved. This selection is motivated by their possible applications and the interesting theoretical questions they can rise in practice. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

Les problèmes inverses liés à des processus physiques sont d'une grande importance dans la plupart des domaines liés au traitement du signal, tels que la tomographie, l'acoustique, les communications sans fil, le radar, l'imagerie médicale, pour n'en nommer que quelques uns. Dans le même temps, beaucoup de ces problèmes soulèvent des défis en raison de leur nature mal posée. Par ailleurs, les signaux émanant de phénomènes physiques sont souvent gouvernées par des lois s'exprimant sous la forme d'équations aux dérivées partielles (EDP) linéaires, ou, de manière équivalente, par des équations intégrales et leurs fonctions de Green associées. De plus, ces phénomènes sont habituellement induits par des singularités, apparaissant comme des sources ou des puits d'un champ vectoriel. Dans cette thèse, nous étudions en premier lieu le couplage entre de telles lois physiques et une hypothèse initiale de parcimonie des origines du phénomène physique. Ceci donne naissance à un concept de dualité des régularisations, formulées soit comme un problème d'analyse coparcimonieuse (menant à la représentation en EDP), soit comme une parcimonie à la synthèse équivalente à la précédente (lorsqu'on fait plutôt usage des fonctions de Green). Nous dédions une part significative de notre travail à la comparaison entre les approches de synthèse et d'analyse. Nous défendons l'idée qu'en dépit de leur équivalence formelle, leurs propriétés computationnelles sont très différentes. En effet, en raison de la parcimonie héritée par la version discrétisée de l'EDP (incarnée par l'opérateur d'analyse), l'approche coparcimonieuse passe bien plus favorablement à l'échelle que le problème équivalent régularisé par parcimonie à la synthèse. Nos constatations sont illustrées dans le cadre de deux applications : la localisation de sources acoustiques, et la localisation de sources de crises épileptiques à partir de signaux électro-encéphalographiques. Dans les deux cas, nous vérifions que l'approche coparcimonieuse démontre de meilleures capacités de passage à l'échelle, au point qu'elle permet même une interpolation complète du champ de pression dans le temps et en trois dimensions. De plus, dans le cas des sources acoustiques, l'optimisation fondée sur le modèle d'analyse \emph{bénéficie} d'une augmentation du nombre de données observées, ce qui débouche sur une accélération du temps de traitement, plus rapide que l'approche de synthèse dans des proportions de plusieurs ordres de grandeur. Nos simulations numériques montrent que les méthodes développées pour les deux applications sont compétitives face à des algorithmes de localisation constituant l'état de l'art. Pour finir, nous présentons deux méthodes fondées sur la parcimonie à l'analyse pour l'estimation aveugle de la célérité du son et de l'impédance acoustique, simultanément à l'interpolation du champ sonore. Ceci constitue une étape importante en direction de la mise en œuvre de nos méthodes en en situation réelle. / Inverse problems related to physical processes are of great importance in practically every field related to signal processing, such as tomography, acoustics, wireless communications, medical and radar imaging, to name only a few. At the same time, many of these problems are quite challenging due to their ill-posed nature. On the other hand, signals originating from physical phenomena are often governed by laws expressible through linear Partial Differential Equations (PDE), or equivalently, integral equations and the associated Green’s functions. In addition, these phenomena are usually induced by sparse singularities, appearing as sources or sinks of a vector field. In this thesis we primarily investigate the coupling of such physical laws with a prior assumption on the sparse origin of a physical process. This gives rise to a “dual” regularization concept, formulated either as sparse analysis (cosparse), yielded by a PDE representation, or equivalent sparse synthesis regularization, if the Green’s functions are used instead. We devote a significant part of the thesis to the comparison of these two approaches. We argue that, despite nominal equivalence, their computational properties are very different. Indeed, due to the inherited sparsity of the discretized PDE (embodied in the analysis operator), the analysis approach scales much more favorably than the equivalent problem regularized by the synthesis approach. Our findings are demonstrated on two applications: acoustic source localization and epileptic source localization in electroencephalography. In both cases, we verify that cosparse approach exhibits superior scalability, even allowing for full (time domain) wavefield interpolation in three spatial dimensions. Moreover, in the acoustic setting, the analysis-based optimization benefits from the increased amount of observation data, resulting in a speedup in processing time that is orders of magnitude faster than the synthesis approach. Numerical simulations show that the developed methods in both applications are competitive to state-of-the-art localization algorithms in their corresponding areas. Finally, we present two sparse analysis methods for blind estimation of the speed of sound and acoustic impedance, simultaneously with wavefield interpolation. This is an important step toward practical implementation, where most physical parameters are unknown beforehand. The versatility of the approach is demonstrated on the “hearing behind walls” scenario, in which the traditional localization methods necessarily fail. Additionally, by means of a novel algorithmic framework, we challenge the audio declipping problemregularized by sparsity or cosparsity. Our method is highly competitive against stateof-the-art, and, in the cosparse setting, allows for an efficient (even real-time) implementation.

[pt] Esta dissertação apresenta uma metodologia para medição visual de campos de deformações (2D) em materiais, por meio da aplicação da técnica SIFT (Scale Invariant Feature Transform). A análise de imagens capturadas é feita por uma câmera digital em estágios diferentes durante o processo de deformação de um material quando este é submetido a esforços mecânicos. SIFT é uma das técnicas modernas de visão computacional e um eficiente filtro para extração e descrição de pontos de características relevantes em imagens, invariantes a transformações em escala, iluminação e rotação. A metodologia é baseada no cálculo do gradiente de funções que representam o campo de deformações em um material durante um ensaio mecânico sob diferentes condições de contorno. As funções são calibradas com a aplicação da análise inversa sobre o conjunto de pontos homólogos de duas imagens extraídos pelo algoritmo SIFT. A formulação da solução ao problema inverso combina os dados experimentais fornecidos pelo SIFT e o método linear de mínimos quadrados para estimação dos parâmetros de deformação. Os modelos propostos para diferentes corpos de prova são avaliados experimentalmente com a ajuda de extensômetros para medição direta das deformações. O campo de deformações identificado pelo sistema de visão computacional é comparado com os valores obtidos pelos extensômetros e por simulações feitas no programa de Elementos Finitos ANSYS. Os resultados obtidos mostram que o campo de deformações pode ser medido utilizando a técnica SIFT, gerando uma nova ferramenta visual de medição para ensaios mecânicos que não se baseia nas técnicas tradicionais de correlação de imagens. / [en] This thesis presents a methodology for measurement of strain fields in materials by applying the SIFT technique (Scale Invariant Feature Transform). The images analyzed are captured by a digital camera at different stages during the deformation process of a material when it is subjected to mechanical stress. SIFT is one of the modern computer vision techniques and an efficient filter for extraction and description of relevant feature points in images. These interest points are largely invariant to changes in scale, illumination and rotation. The methodology is based on the calculation of the gradient of the functions that represents the corresponding strain field in the material during a mechanical test under different boundary conditions. The functions are calibrated with the application of inverse analysis on the set of homologous points of two images extracted by the SIFT algorithm. The formulation of the solution to the inverse problem combines the experimental data processed by the SIFT and linear least squares method for the estimation of strain parameters. The proposed models for different specimens are evaluated experimentally with strain gauges for direct measurement of the deformations. The strain field identified by the computer vision system is compared with values obtained by strain gauges and simulations with the ANSYS finite element program. The proposed models for different types of measurements are experimentally evaluated with strain gages, including the estimation of mechanical properties. The results show that the strain field can be measured using the SIFT technique, developing a new visual tool for measurement of mechanical tests that are not based on traditional techniques of image correlation.

A recording of the defense presentation for this dissertation is available at: http://hdl.handle.net/10754/625197 / Steady-state elliptic partial differential equations (PDEs) are frequently used to model a diverse range of physical phenomena. The source and boundary data estimation problems for such PDE systems are of prime interest in various engineering disciplines including biomedical engineering, mechanics of materials and earth sciences. Almost all existing solution strategies for such problems can be broadly classified as optimization-based techniques, which are computationally heavy especially when the problems are formulated on higher dimensional space domains. However, in this dissertation, feedback based state estimation algorithms, known as state observers, are developed to solve such steady-state problems using one of the space variables as time-like. In this regard, first, an iterative observer algorithm is developed that sweeps over regular-shaped domains and solves boundary estimation problems for steady-state Laplace equation. It is well-known that source and boundary estimation problems for the elliptic PDEs are highly sensitive to noise in the data. For this, an optimal iterative observer algorithm, which is a robust counterpart of the iterative observer, is presented to tackle the ill-posedness due to noise. The iterative observer algorithm and the optimal iterative algorithm are then used to solve source localization and estimation problems for Poisson equation for noise-free and noisy data cases respectively. Next, a divide and conquer approach is developed for three-dimensional domains with two congruent parallel surfaces to solve the boundary and the source data estimation problems for the steady-state Laplace and Poisson kind of systems respectively. Theoretical results are shown using a functional analysis framework, and consistent numerical simulation results are presented for several test cases using finite difference discretization schemes.

This thesis discusses the inverse problem associated with the propagation of field autocorrelation of light through a highly scattering object like tissue. In the first part of the thesis we consider the mathematical issues involved in inverting boundary measurements made from diffuse propagation of light through highly scattering objects for their optical and mechanical properties. We present the convergence analysis of the Gauss-Newton algorithm for the recovery of object properties applicable for both diffuse correlation tomography (DCT) and diffuse optical tomography (DOT). En route to this, we establish the existence of solution and Frechet differentiability of the forward propagation equation. The two cases of the delta source and the Gaussian source illuminations are considered separately and the smoothness of solution of the forward equation in these cases is established. Considering DCT as an example, we establish the feasibility of recovering the particle diffusion coefficient (DB ) through minimizing the data-model mismatch of the field autocorrelation at the boundary using the Gauss-Newton algorithm. Some numerical examples validating the theoretical results are also presented. In the second part of the thesis, we reconstruct optical absorption coefficient, µa, and particle diffusion coefficient, DB , from simulated measurements which are integrals of a quantity computed from the measured intensity and intensity autocorrelation g2(τ ) at the boundary. We also recovered the mean square displacement (MSD) distribution of particles in an inhomogeneous object from the sampled g2(τ ) measured on the boundary. From the MSD, we compute the storage and loss moduli distributions in the object. We have devised computationally easy methods to construct the sensitivity matrices which are used in the iterative reconstruction algorithms for recovering these parameters from these measurements. The results of reconstruction of inhomogeneities in µa, DB , MSD and the visco-elastic parameters, which are presented, show forth reasonably good positional and quantitative accuracy. Finally we introduce a self regularized pseudo-dynamic scheme to solve the above inverse problem, which has certain advantages over the usual minimization method employing a variant of the Newton algorithm. The computational difficulties involved in the inversion of ill-conditioned matrices arising in the nonlinear inverse DCT problem are avoided by introducing artificial dynamics and considering the solution to be the steady-state response (if it exists) of the artificially evolving dynamical system, represented by ordinary differential equations (ODE) in pseudo-time. We show that the asymptotic solution obtained through the pseudo-time marching converges to the optimal solution which minimizes a mean-square error functional, provided the Hessian of the forward equation is positive definite in the neighborhood of this optimal solution. The superior noise tolerance and regularization-insensitive nature of pseudo-dynamic strategy are proven through numerical simulations in the context of DCT.

Dans la première partie, on démontre des inégalités de Carleman pour des systèmes paraboliques. Au chapitre 1, on démontre des inégalités de stabilité pour un système parabolique 2 x 2 en utilisant des inégalités de Carleman avec une seule observation. Il s'agit d'un problème inverse pour l'identification des coefficients et les conditions initiales du système. Le chapitre2 est consacré aux inégalités de Carleman pour des systèmes paraboliques dont les coefficients de diffusion sont de classe C1 par morceaux ou à variations bornées. A la fin, on donne quelques applications à la contrôlabilité à zéro. La seconde partie est consacrée à l'étude d'un problème de diffraction d'ondes acoustiques dans un demi-plan homogène. Il s'agit d'un problème aux limites associé à l'équation de Helmholtz dans le demi-plan supérieur avec une donnée de Neumann non homogène au bord. On apporte des éléments de réponse sur la question d'unicité et d'existence des solutions pour certaines classes de la donnée au bord. / In the first part, we prove Carleman estimates for parabolic systems. In chapter1, we prove stability inequalities for 2 x 2 parabolic system using Carleman estimates with one observation. It is concerns to the identification of the coefficients and initial conditions of the system. The chapter2 is devoted to th Carleman estimates of parabolic systems for which the diffusion coefficients are assumed to be ofclass piecewise C1 or with bounded variations. In the end, we give some applications to the null controllability. The second part is devoted to the study of the scattering problem of acoustics waves in a homogeneous half-plane. It is about a boundary value problem associated to the Helmholtz equation in theupper half-plane with a nonhomogeneous Neumann boundary data. We provide some answers to the question of uniqueness and existence of solutions for some classes of the boundary data.

Typical aero-engine assemblies have at least two nested rotors mounted within a flexible casing via squeeze-film damper (SFD) bearings. As a result, the flexible casing structures become highly sensitive to the vibration excitation arising from the High and Low pressure rotors. Lowering vibrations at the aircraft engine casing can reduce harmful effects on the aircraft engine. Inverse problem techniques provide a means toward solving the unbalance identification problem for a rotordynamic system supported by nonlinear SFD bearings, requiring prior knowledge of the structure and measurements of vibrations at the casing. This thesis presents two inverse solution techniques for the nonlinear rotordynamic inverse problem, which are focused on applications where the rotor is inaccessible under operating conditions, e.g. high pressure rotors. Numerical and experimental validations under hitherto unconsidered conditions have been conducted to test the robustness of each technique. The main contributions of this thesis are:• The development of a non-invasive inverse procedure for unbalance identification and balancing of a nonlinear SFD rotordynamic system. This method requires at least a linear connection to ensure a well-conditioned explicit relationship between the casing vibration and the rotor unbalance via frequency response functions. The method makes no simplifying assumptions made in previous research e.g. neglect of gyroscopic effects; assumption of structural isotropy; restriction to one SFD; circular centred orbits (CCOs) of the SFD. • The identification and validation of the inverse dynamic model of the nonlinear SFD element, based on recurrent neural networks (RNNs) that are trained to reproduce the Cartesian displacements of the journal relative to the bearing housing, when presented with given input time histories of the Cartesian SFD bearing forces.• The empirical validation of an entirely novel approach towards the solution of a nonlinear inverse rotor-bearing problem, one involving an identified empirical inverse SFD bearing model. This method is suitable for applications where there is no adequate linear connection between rotor and casing. Both inverse solutions are formulated using the Receptance Harmonic Balance Method (RHBM) as the underpinning theory. The first inverse solution uses the RHBM to generate the backwards operator, where a linear connection is required to guarantee an explicit inverse solution. A least-squares solution yields the equivalent unbalance distribution in prescribed planes of the rotor, which is consequently used to balance it. This method is successfully validated on distinct rotordynamic systems, using simulated data considering different practical scenarios of error sources, such as noisy data, model uncertainty and balancing errors. Focus is then shifted to the second inverse solution, which is experimentally-based. In contrast to the explicit inverse solution, the second alternative uses the inverse SFD model as an implicit inverse solution. Details of the SFD test rig and its set up for empirical identification are presented. The empirical RNN training process for the inverse function of an SFD is presented and validated as a part of a nonlinear inverse problem. Finally, it is proved that the RNN could thus serve as reliable virtual instrumentation for use within an inverse rotor-bearing problem.

Les tâches qui consistent à comprendre automatiquement le contenu d’un signal naturel, comme une image ou un son, sont en général difficiles. En effet, dans leur représentation naïve, les signaux sont des objets compliqués, appartenant à des espaces de grande dimension. Représentés différemment, ils peuvent en revanche être plus faciles à interpréter. Cette thèse s’intéresse à une représentation fréquemment utilisée dans ce genre de situations, notamment pour analyser des signaux audio : le module de la transformée en ondelettes. Pour mieux comprendre son comportement, nous considérons, d’un point de vue théorique et algorithmique, le problème inverse correspondant : la reconstruction d’un signal à partir du module de sa transformée en ondelettes. Ce problème appartient à une classe plus générale de problèmes inverses : les problèmes de reconstruction de phase. Dans un premier chapitre, nous décrivons un nouvel algorithme, PhaseCut, qui résout numériquement un problème de reconstruction de phase générique. Comme l’algorithme similaire PhaseLift, PhaseCut utilise une relaxation convexe, qui se trouve en l’occurence être de la même forme que les relaxations du problème abondamment étudié MaxCut. Nous comparons les performances de PhaseCut et PhaseLift, en termes de précision et de rapidité. Dans les deux chapitres suivants, nous étudions le cas particulier de la reconstruction de phase pour la transformée en ondelettes. Nous montrons que toute fonction sans fréquence négative est uniquement déterminée (à une phase globale près) par le module de sa transformée en ondelettes, mais que la reconstruction à partir du module n’est pas stable au bruit, pour une définition forte de la stabilité. On démontre en revanche une propriété de stabilité locale. Nous présentons également un nouvel algorithme de reconstruction de phase, non-convexe, qui est spécifique à la transformée en ondelettes, et étudions numériquement ses performances. Enfin, dans les deux derniers chapitres, nous étudions une représentation plus sophistiquée, construite à partir du module de transformée en ondelettes : la transformée de scattering. Notre but est de comprendre quelles propriétés d’un signal sont caractérisées par sa transformée de scattering. On commence par démontrer un théorème majorant l’énergie des coefficients de scattering d’un signal, à un ordre donné, en fonction de l’énergie du signal initial, convolé par un filtre passe-haut qui dépend de l’ordre. On étudie ensuite une généralisation de la transformée de scattering, qui s’applique à des processus stationnaires. On montre qu’en dimension finie, cette transformée généralisée préserve la norme. En dimension un, on montre également que les coefficients de scattering généralisés d’un processus caractérisent la queue de distribution du processus. / Automatically understanding the content of a natural signal, like a sound or an image, is in general a difficult task. In their naive representation, signals are indeed complicated objects, belonging to high-dimensional spaces. With a different representation, they can however be easier to interpret. This thesis considers a representation commonly used in these cases, in particular for theanalysis of audio signals: the modulus of the wavelet transform. To better understand the behaviour of this operator, we study, from a theoretical as well as algorithmic point of view, the corresponding inverse problem: the reconstruction of a signal from the modulus of its wavelet transform. This problem belongs to a wider class of inverse problems: phase retrieval problems. In a first chapter, we describe a new algorithm, PhaseCut, which numerically solves a generic phase retrieval problem. Like the similar algorithm PhaseLift, PhaseCut relies on a convex relaxation of the phase retrieval problem, which happens to be of the same form as relaxations of the widely studied problem MaxCut. We compare the performances of PhaseCut and PhaseLift, in terms of precision and complexity. In the next two chapters, we study the specific case of phase retrieval for the wavelet transform. We show that any function with no negative frequencies is uniquely determined (up to a global phase) by the modulus of its wavelet transform, but that the reconstruction from the modulus is not stable to noise, for a strong notion of stability. However, we prove a local stability property. We also present a new non-convex phase retrieval algorithm, which is specific to the case of the wavelet transform, and we numerically study its performances. Finally, in the last two chapters, we study a more sophisticated representation, built from the modulus of the wavelet transform: the scattering transform. Our goal is to understand which properties of a signal are characterized by its scattering transform. We first prove that the energy of scattering coefficients of a signal, at a given order, is upper bounded by the energy of the signal itself, convolved with a high-pass filter that depends on the order. We then study a generalization of the scattering transform, for stationary processes. We show that, in finite dimension, this generalized transform preserves the norm. In dimension one, we also show that the generalized scattering coefficients of a process characterize the tail of its distribution.

Une variété riemannienne est dite rigide lorsque la longueur des géodésiques périodiques (cas des variétés fermées) ou des géodésiques diffusées (cas des variétés ouvertes) permet de reconstruire globalement la géométrie de la variété. Cette notion trouve naturellement son origine dans des dispositifs d’imagerie numérique tels que la tomographie par rayons X. Grâce une approche résolument analytique initiée par Guillarmou et fondée sur de l’analyse microlocale (plus particulièrement sur certaines techniques récentes dues à Faure-Sjostrand et Dyatlov-Zworski permettant une étude analytique fine des flots Anosov), nous montrons que le spectre marqué des longueurs, c’est-à-dire la donnée des longueurs des géodésiques périodiques marquées par l’homotopie, d’une variété fermée Anosov ou Anosov à pointes hyperboliques détermine localement la métrique de la variété. Dans le cas d’une variété ouverte avec ensemble capté hyperbolique, nous montrons que la distance marquée au bord, c’est-à-dire la donnée de la longueur des géodésiques diffusées marquées par l’homotopie, détermine localement la métrique. Enfin, dans le cas d’une surface asymptotiquement hyperbolique, nous montrons qu’une notion de distance renormalisée entre paire de points au bord à l’infini permet de reconstruire globalement la géométrie de la surface. / A Riemannian manifold is said to be rigid if the length of periodic geodesics (in the case of a closed manifold) or scattered geodesics (in the case of an open manifold) allows to recover the full geometry of the manifold. This notion naturally arises in imaging devices such as X-ray tomography. Thanks to a analytic framework introduced by Guillarmou and based on microlocal analysis (and more precisely on the analytic study of hyperbolic flows of Faure-Sjostrand and Dyatlov-Zworski), we show that the marked length spectrum, that is the lengths of the periodic geodesics marked by homotopy, of a closed Anosov manifold or of an Anosov manifold with hyperbolic cusps locally determines its metric. In the case of an open manifold with hyperbolic trapped set, we show that the length of the scattered geodesics marked by homotopy locally determines the metric. Eventually, in the case of an asymptotically hyperbolic surface, we show that a suitable notion of renormalized distance between pair of points on the boundary at infinity allows to globally reconstruct the geometry of the surface.

Inverse problems are problems where causes for desired or an observed effect are to be determined. They lie at the heart of scientific inquiry and technological development, including radar/sonar, medical imaging, geophysical exploration, invisibility cloaking and remote sensing, to name just a few. In this thesis, we focus on the theoretical study and applications of some intriguing inverse problems. Precisely speaking, we are concerned with two typical kinds of problems in the field of wave scattering and nonlocal inverse problem, respectively. The first topic is on the geometric structures of eigenfunctions and their applications in wave scattering theory, in which the conductive transmission eigenfunctions and Laplacian eigenfunctions are considered. For the study on the intrinsic geometric structures of the conductive transmission eigenfunctions, we first present the vanishing properties of the eigenfunctions at corners both in R2 and R3, based on microlocal analysis with the help of a particular type of planar complex geometrical optics (CGO) solution. This significantly extends the previous study on the interior transmission eigenfunctions. Then, as a practical application of the obtained geometric results, we establish a unique recovery result for the inverse problem associated with the transverse electromagnetic scattering by a single far-field measurement in simultaneously determining a polygonal conductive obstacle and its surface conductive parameter. For the study on the geometric structures of Laplacian eigenfunctions, we separately discuss the two-dimensional case and the three-dimensional case. In R2, we introduce a new notion of generalized singular lines of Laplacian eigenfunctions, and carefully investigate these singular lines and the nodal lines. The studies reveal that the intersecting angle between two of those lines is closely related to the vanishing order of the eigenfunction at the intersecting point. We provide an accurate and comprehensive quantitative characterization of the relationship. In R3, we study the analytic behaviors of Laplacian eigenfunctions at places where nodal or generalized singular planes intersect, which is much more complicated. These theoretical findings are original and of significant interest in spectral theory. Moreover, they are applied directly to some physical problems of great importance, including the inverse obstacle scattering problem and the inverse diffraction grating problem. It is shown in a certain polygonal (polyhedral) setup that one can recover the support of the unknown scatterer as well as the surface impedance parameter by finitely many far-field patterns. Our second topic is concerning the fractional partial differential operators and some related nonlocal inverse problems. We present some prelimilary knowledge on fractional Sobolev Spaces and fractional partial differential operators first. Then we focus on the simultaneous recovery results of two interesting nonlocal inverse problems. One is simultaneously recovering potentials and the embedded obstacles for anisotropic fractional Schrödinger operators based on the strong uniqueness property and Runge approximation property. The other one is the nonlocal inverse problem associated with a fractional Helmholtz equation that arises from the study of viscoacoustics in geophysics and thermoviscous modelling of lossy media. We establish several general uniqueness results in simultaneously recovering both the medium parameter and the internal source by the corresponding exterior measurements. The main method utilized here is the low-frequency asymptotics combining with the variational argument. In sharp contrast, these unique determination results are unknown in the local case, which would be of significant importance in thermo- and photo-acoustic tomography.