Scaling full seismic waveform inversions

The main goal of this research study is to scale full seismic waveform inversions using the adjoint-state method to the data volumes that are nowadays available in seismology. Practical issues hinder the routine application of this, to a certain extent theoretically well understood, method. To a large part this comes down to outdated or flat out missing tools and ways to automate the highly iterative procedure in a reliable way. This thesis tackles these issues in three successive stages. It first introduces a modern and properly designed data processing framework sitting at the very core of all the consecutive developments. The ObsPy toolkit is a Python library providing a bridge for seismology into the scientific Python ecosystem and bestowing seismologists with effortless I/O and a powerful signal processing library, amongst other things. The following chapter deals with a framework designed to handle the specific data management and organization issues arising in full seismic waveform inversions, the Large-scale Seismic Inversion Framework. It has been created to orchestrate the various pieces of data accruing in the course of an iterative waveform inversion. Then, the Adaptable Seismic Data Format, a new, self-describing, and scalable data format for seismology is introduced along with the rationale why it is needed for full waveform inversions in particular and seismology in general. Finally, these developments are put into service to construct a novel full seismic waveform inversion model for elastic subsurface structure beneath the North American continent and the Northern Atlantic well into Europe. The spectral element method is used for the forward and adjoint simulations coupled with windowed time-frequency phase misfit measurements. Later iterations use 72 events, all happening after the USArray project has commenced, resulting in approximately 150`000 three components recordings that are inverted for. 20 L-BFGS iterations yield a model that can produce complete seismograms at a period range between 30 and 120 seconds while comparing favorably to observed data

New Directions for Contact Integrators

Contact integrators are a family of geometric numerical schemes which guarantee the conservation of the contact structure. In this work we review the construction of both the variational and Hamiltonian versions of these methods. We illustrate some of the advantages of geometric integration in the dissipative setting by focusing on models inspired by recent studies in celestial mechanics and cosmology.Comment: To appear as Chapter 24 in GSI 2021, Springer LNCS 1282

Classical and quantum investigations of four-dimensional maps with a mixed phase space

Für das Verständnis einer Vielzahl von Problemen von der Himmelsmechanik bis hin zur Beschreibung von Molekülen spielen Systeme mit mehr als zwei Freiheitsgraden eine entscheidende Rolle. Aufgrund der Dimensionalität gestaltet sich ein Verständnis dieser Systeme jedoch deutlich schwieriger als bei Systemen mit zwei oder weniger Freiheitsgraden. Die vorliegende Arbeit soll zum besseren Verständnis der klassischen und quantenmechanischen Eigenschaften getriebener Systeme mit zwei Freiheitsgraden beitragen. Hierzu werden dreidimensionale Schnitte durch den Phasenraum von 4D Abbildungen betrachtet. Anhand dreier Beispiele, deren Phasenräume zunehmend kompliziert sind, werden diese 3D Schnitte vorgestellt und untersucht. In einer sich anschließenden quantenmechanischen Untersuchung gehen wir auf zwei wichtige Aspekte ein. Zum einen untersuchen wir die quantenmechanischen Signaturen des klassischen "Arnold Webs". Es wird darauf eingegangen, wie die Quantenmechanik dieses Netz im semiklassischen Limes auflösen kann. Darüberhinaus widmen wir uns dem wichtigen Aspekt quantenmechanischer Kopplungen klassisch getrennter Phasenraumgebiete anhand der Untersuchung dynamischer Tunnelraten. Für diese wenden wir sowohl den in der Literatur bekannten "fictitious integrable system approach" als auch die Theorie des resonanz-unterstützen Tunnelns auf 4D Abbildungen an.:Contents ..... v 1 Introduction ..... 1 2 2D mappings ..... 5 2.1 Hamiltonian systems with 1.5 degrees of freedom ..... 5 2.2 The 2D standard map ..... 6 3 Classical dynamics of higher dimensional systems ..... 11 3.1 Coupled standard maps as paradigmatic example ..... 12 Stability of fixed points in 4D maps ..... 13 Center manifolds of elliptic degrees of freedom ..... 13 3.2 Near-integrable systems ..... 15 3.2.1 Analytical description of multidimensional, near-integrable systems ..... 15 Resonance structures in 4D maps ..... 16 3.2.2 Pendulum approximation ..... 18 3.2.3 Normal forms ..... 24 3.2.4 Arnold diffusion and Arnold web ..... 24 3.3 Numerical tools for the analysis of regular and chaotic motion ..... 26 3.3.1 Frequency analysis ..... 26 Aim of the frequency analysis ..... 26 Realizations of the frequency analysis ..... 27 Wavelet transforms ..... 30 3.3.2 Fast Lyapunov indicator ..... 31 3.3.3 Phase-space sections ..... 33 Skew phase-space sections containing invariant eigenspaces ..... 34 3.4 Systems with regular dynamics and a large chaotic sea ..... 35 3.4.1 Designed maps: Map with linear regular region, P_llu ..... 36 Phase space of the designed map with linear regular region ..... 38 FLI values ..... 41 Estimating the size of the regular region ..... 43 3.4.2 Designed maps: Islands with resonances, P_nnc ..... 46 Frequency analysis ..... 46 FLI values and volume of the regular and stochastic region ..... 50 Frequency analysis for rank-2 resonance ..... 52 Phase-space sections at different positions p_1 and p_2 ..... 53 Using color to provide the 4-th coordinate ..... 53 Skew phase-space sections containing invariant eigenspaces ..... 57 Arnold diffusion ..... 58 3.4.3 Generic maps: Coupled standard maps, P_csm ..... 63 FLI values and volume of the regular and stochastic region ..... 63 Analysis of fundamental frequencies ..... 66 Skew phase-space sections containing invariant eigenspaces ..... 69 4 Quantum Mechanics ..... 75 4.1 Quantization of Classical Maps ..... 77 4.2 Eigenstates of the time evolution operator U ..... 79 4.2.1 Eigenstates of P_llu ..... 80 4.2.2 Eigenstates of P_nnc ..... 84 4.2.3 Eigenstates of P_csm ..... 87 4.3 Quantum signatures of the stochastic layer ..... 89 4.3.1 Eigenstates resolving the stochastic layer ..... 90 4.3.2 Wave-packet dynamics into the stochastic layer ..... 94 4.4 Dynamical tunneling rates ..... 98 4.4.1 Numerical calculation of dynamical tunneling rates ..... 99 4.4.2 Direct regular-to-chaotic tunneling rates gamma^d of P_llu ..... 101 4.4.3 Prediction of gamma^d using the fictitious integrable system approach ..... 103 4.4.4 Dynamical tunneling rates of P_nnc ..... 105 4.4.5 Interlude: Theory of resonance assisted tunneling (RAT) ..... 106 4.4.6 Prediction of tunneling rates for P_nnc, RAT ..... 111 Selection rules from nonlinear resonances ..... 111 Energy denominators ..... 114 Estimating the parameters of the pendulum approximation from phase-space properties ..... 116 Prediction ..... 118 4.4.7 Dynamical tunneling rates of P_csm ..... 120 5 Summary and outlook ..... 123 Appendix ..... 125 A Potential of the designed map ..... 125 B Quantum-number assignment-algorithm ..... 128 C Alternate paths due to alternate resonances in the description of RAT ..... 131 D Alternate resonances in the description of RAT leading to different tunneling rates ..... 133 E Tunneling rates of map with nonlinear resonances but uncoupled regular region ..... 133 F Interpolation of quasienergies ..... 135 G 2D Poincar'e map for the pendulum approximation ..... 137 H RAT prediction broken down to single paths ..... 139 I Linearization of the pendulum approximation ..... 140 J Iterative diagonalization schemes for the semiclassical limit ..... 143 Inverse iteration ..... 143 Arnoldi method ..... 144 Lanczos algorithm ..... 144 List of figures ..... 148 Bibliography ..... 163Systems with more than two degrees of freedom are of fundamental importance for the understanding of problems ranging from celestial mechanics to molecules. Due to the dimensionality the classical phase-space structure of such systems is more difficult to understand than for systems with two or fewer degrees of freedom. This thesis aims for a better insight into the classical as well as the quantum mechanics of 4D mappings representing driven systems with two degrees of freedom. In order to analyze such systems, we introduce 3D sections through the 4D phase space which reveal the regular and chaotic structures. We introduce these concepts by means of three example mappings of increasing complexity. After a classical analysis the systems are investigated quantum mechanically. We focus especially on two important aspects: First, we address quantum mechanical consequences of the classical Arnold web and demonstrate how quantum mechanics can resolve this web in the semiclassical limit. Second, we investigate the quantum mechanical tunneling couplings between regular and chaotic regions in phase space. We determine regular-to-chaotic tunneling rates numerically and extend the fictitious integrable system approach to higher dimensions for their prediction. Finally, we study resonance-assisted tunneling in 4D maps.:Contents ..... v 1 Introduction ..... 1 2 2D mappings ..... 5 2.1 Hamiltonian systems with 1.5 degrees of freedom ..... 5 2.2 The 2D standard map ..... 6 3 Classical dynamics of higher dimensional systems ..... 11 3.1 Coupled standard maps as paradigmatic example ..... 12 Stability of fixed points in 4D maps ..... 13 Center manifolds of elliptic degrees of freedom ..... 13 3.2 Near-integrable systems ..... 15 3.2.1 Analytical description of multidimensional, near-integrable systems ..... 15 Resonance structures in 4D maps ..... 16 3.2.2 Pendulum approximation ..... 18 3.2.3 Normal forms ..... 24 3.2.4 Arnold diffusion and Arnold web ..... 24 3.3 Numerical tools for the analysis of regular and chaotic motion ..... 26 3.3.1 Frequency analysis ..... 26 Aim of the frequency analysis ..... 26 Realizations of the frequency analysis ..... 27 Wavelet transforms ..... 30 3.3.2 Fast Lyapunov indicator ..... 31 3.3.3 Phase-space sections ..... 33 Skew phase-space sections containing invariant eigenspaces ..... 34 3.4 Systems with regular dynamics and a large chaotic sea ..... 35 3.4.1 Designed maps: Map with linear regular region, P_llu ..... 36 Phase space of the designed map with linear regular region ..... 38 FLI values ..... 41 Estimating the size of the regular region ..... 43 3.4.2 Designed maps: Islands with resonances, P_nnc ..... 46 Frequency analysis ..... 46 FLI values and volume of the regular and stochastic region ..... 50 Frequency analysis for rank-2 resonance ..... 52 Phase-space sections at different positions p_1 and p_2 ..... 53 Using color to provide the 4-th coordinate ..... 53 Skew phase-space sections containing invariant eigenspaces ..... 57 Arnold diffusion ..... 58 3.4.3 Generic maps: Coupled standard maps, P_csm ..... 63 FLI values and volume of the regular and stochastic region ..... 63 Analysis of fundamental frequencies ..... 66 Skew phase-space sections containing invariant eigenspaces ..... 69 4 Quantum Mechanics ..... 75 4.1 Quantization of Classical Maps ..... 77 4.2 Eigenstates of the time evolution operator U ..... 79 4.2.1 Eigenstates of P_llu ..... 80 4.2.2 Eigenstates of P_nnc ..... 84 4.2.3 Eigenstates of P_csm ..... 87 4.3 Quantum signatures of the stochastic layer ..... 89 4.3.1 Eigenstates resolving the stochastic layer ..... 90 4.3.2 Wave-packet dynamics into the stochastic layer ..... 94 4.4 Dynamical tunneling rates ..... 98 4.4.1 Numerical calculation of dynamical tunneling rates ..... 99 4.4.2 Direct regular-to-chaotic tunneling rates gamma^d of P_llu ..... 101 4.4.3 Prediction of gamma^d using the fictitious integrable system approach ..... 103 4.4.4 Dynamical tunneling rates of P_nnc ..... 105 4.4.5 Interlude: Theory of resonance assisted tunneling (RAT) ..... 106 4.4.6 Prediction of tunneling rates for P_nnc, RAT ..... 111 Selection rules from nonlinear resonances ..... 111 Energy denominators ..... 114 Estimating the parameters of the pendulum approximation from phase-space properties ..... 116 Prediction ..... 118 4.4.7 Dynamical tunneling rates of P_csm ..... 120 5 Summary and outlook ..... 123 Appendix ..... 125 A Potential of the designed map ..... 125 B Quantum-number assignment-algorithm ..... 128 C Alternate paths due to alternate resonances in the description of RAT ..... 131 D Alternate resonances in the description of RAT leading to different tunneling rates ..... 133 E Tunneling rates of map with nonlinear resonances but uncoupled regular region ..... 133 F Interpolation of quasienergies ..... 135 G 2D Poincar'e map for the pendulum approximation ..... 137 H RAT prediction broken down to single paths ..... 139 I Linearization of the pendulum approximation ..... 140 J Iterative diagonalization schemes for the semiclassical limit ..... 143 Inverse iteration ..... 143 Arnoldi method ..... 144 Lanczos algorithm ..... 144 List of figures ..... 148 Bibliography ..... 16

Experimental and Numerical Investigation of Shallow Mixing Layer

When two nearly parallel streams of different velocities join together at a river confluence, a mixing layer develops along the joining interface. The mixing layer is termed a shallow mixing layer (SML) when the flow has a small depth and thus is significantly influenced by riverbed friction. SML has impacts on the riverine environment, river ecosystems, and hydraulic engineering design. The effects of the velocity ratio (Vr) between the two incoming streams on SML characteristics have not been addressed adequately from both Lagrangian and Eulerian perspectives. This study employs the Lagrangian and Eulerian approaches to explore the SML. Specifically, this study aims to answer these main questions: How does SML behave under different ratios of incoming flow velocities? How does the shear caused by the velocity gradient influence the fluid particles in SML? To what extend previous relations proposed to determine mixing layer width are practical? What factors control the pairing of adjacent eddies and the growth of large-scale coherent structures? How are the fluid mass and momentum exchanges affected by Vr? The dye visualisation and particle tracking velocimetry (PTV) techniques were used in the laboratory experiments of SML. In the dye visualisation technique, a tracer’s motion is recorded using a single camera. PTV is considered an optical flow measurement technique in which neutrally buoyant particles are tracked in consecutive image frames. PTV provides the essential means for Lagrangian studies of SML. For numerical simulations of SML, the smoothed particle hydrodynamics (SPH) model was used. SPH is considered a Lagrangian CFD method in which a continuum is discretised using a set of material points or particles. Laboratory experiments of SML were conducted at three velocity ratios: Vr = 1, 1.14, and 1.5, and PTV measurements were made from the region between the joining location (x = 0 m) and 0.3 m downstream (x = 0.3 m) in the confluence. The dye visualisation experiments consisted of the same three velocity ratios, and the visualisation covered the region of x = 0–1.2 m. The SPH simulations included three velocity ratios: Vr = 1.14, 1.5, and 3; the domain covered x = 0–1 m. The PTV measurements show that the boundary layers, which develop on the sidewalls of a splitter plate used to separate the two incoming streams before joining, and the wake effect cause a velocity deficit in the confluence and limit the mixing layer growth. The SPH results reveal that a smaller velocity ratio results in a more visible velocity deficit in streamwise velocity profiles due to the relative importance of the wake versus velocity gradient in the SML. PTV application for SML investigation has been found to require special technical considerations, some of which were introduced in this study. The technical measures in the data acquisition and the Python script developed for particle trajectory analysis improved the PTV technique in studying SML. Finite-time Lyapunov exponents (FTLE) results indicate that for particle trajectories located inside the mixing layer, a divergence was evident with a positive value of FTLE. However, for the particle trajectories out of the mixing layer, both positive and negative FTLE can be observed. The dye visualisation results show that turbulent instabilities still form in the absence of velocity gradient (Vr = 1). When the velocity gradient exists (Vr > 1), the instabilities persist, and a pairing of eddies is observed. The intermittency of SML is observed with a lack of a temporally fixed pattern in the vortex arrangement at a fixed location. The dye visualisation results show a linear relation between eddy spacing and downstream distance, with the most frequent eddy spacing being 0.42x. A new approach to the determination of mixing layer width was proposed based on the boundary layer definition. Results show that for smaller velocity ratios, the mixing layer width determined by the boundary layer method is smaller than those from existing empirical relations. A pairing process of vortices occurs less often when Vr is as small as 1.14, compared to that for Vr = 1.5 and 3. The results also show that pairing activities in SML are affected mainly by the average vorticity magnitude of two neighbouring eddies rather than their relative distance. The Okubo-Weiss parameter of SML indicates that the general form of an eddy in SML consists of an inner vorticity-dominated region at the core and an outer region, which is strain-dominated and surrounding the inner region. The strain-dominated boundary of the eddies performs such a barrier for the particles inside the eddy until the eddies decay or are paired with other eddies. Smaller velocity ratios result in lower mass transfer with a constant rate from the tip of the splitter plate to the downstream whilst for Vr = 3, the mass transfer rate increases moving downstream due to the larger eddies and more profound pairing process. In the SML, the intermittent crests and troughs in momentum transfer indicate the evolution of eddies

Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

The use of machine learning in mechanics is booming. Algorithms inspired by developments in the field of artificial intelligence today cover increasingly varied fields of application. This book illustrates recent results on coupling machine learning with computational mechanics, particularly for the construction of surrogate models or reduced order models. The articles contained in this compilation were presented at the EUROMECH Colloquium 597, « Reduced Order Modeling in Mechanics of Materials », held in Bad Herrenalb, Germany, from August 28th to August 31th 2018. In this book, Artificial Neural Networks are coupled to physics-based models. The tensor format of simulation data is exploited in surrogate models or for data pruning. Various reduced order models are proposed via machine learning strategies applied to simulation data. Since reduced order models have specific approximation errors, error estimators are also proposed in this book. The proposed numerical examples are very close to engineering problems. The reader would find this book to be a useful reference in identifying progress in machine learning and reduced order modeling for computational mechanics

Scaling full seismic waveform inversions

The main goal of this research study is to scale full seismic waveform inversions using the adjoint-state method to the data volumes that are nowadays available in seismology. Practical issues hinder the routine application of this, to a certain extent theoretically well understood, method. To a large part this comes down to outdated or flat out missing tools and ways to automate the highly iterative procedure in a reliable way. This thesis tackles these issues in three successive stages. It first introduces a modern and properly designed data processing framework sitting at the very core of all the consecutive developments. The ObsPy toolkit is a Python library providing a bridge for seismology into the scientific Python ecosystem and bestowing seismologists with effortless I/O and a powerful signal processing library, amongst other things. The following chapter deals with a framework designed to handle the specific data management and organization issues arising in full seismic waveform inversions, the Large-scale Seismic Inversion Framework. It has been created to orchestrate the various pieces of data accruing in the course of an iterative waveform inversion. Then, the Adaptable Seismic Data Format, a new, self-describing, and scalable data format for seismology is introduced along with the rationale why it is needed for full waveform inversions in particular and seismology in general. Finally, these developments are put into service to construct a novel full seismic waveform inversion model for elastic subsurface structure beneath the North American continent and the Northern Atlantic well into Europe. The spectral element method is used for the forward and adjoint simulations coupled with windowed time-frequency phase misfit measurements. Later iterations use 72 events, all happening after the USArray project has commenced, resulting in approximately 150`000 three components recordings that are inverted for. 20 L-BFGS iterations yield a model that can produce complete seismograms at a period range between 30 and 120 seconds while comparing favorably to observed data