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Imaging the Earth’s Near Surface with Dense Seismic Observation
Get PDFUnderstanding the Earth's near surface is critical for assessing seismic hazards and ensuring environmental sustainability. In this thesis, I explore the use of advanced observation and analysis techniques for near-surface imaging with big seismic data.
Chapters 2-6 focus on the applications of Distributed Acoustic Sensing (DAS). DAS is an emerging sensing technology that transforms fiber-optic cables into dense seismic arrays. In Chapter 2, I introduce a high-performance Python tool for computing seismic ambient noise cross-correlation with large volumes of DAS data. In Chapter 3, I perform ambient noise tomography using a DAS array in Ridgecrest, California, to resolve spatial variation of the near-surface structure, revealing its correlation with earthquake ground shaking amplification. In Chapter 4, I use surface wave scattering observed in the DAS noise cross-correlation for fault zone detection and characterization. In Chapter 5, I analyze three years of DAS noise cross-correlation to monitor seismic velocity changes, providing insights into vadose zone soil moisture dynamics and water resource management in the context of climate change. In Chapter 6, I use a DAS array at the South Pole to characterize firn structure for a better understanding of cryosphere mass balance.
Chapters 7 and 8 focus on imaging geological structures in the urban Los Angeles region using dense arrays of geophones. Chapter 7 uses converted S-to-p phases recorded by a dense network of low-cost accelerometers to map the basin depth. Chapter 8 investigates shallow seismicity in the Long Beach area to illuminate complex fault structures.
In Chapters 9 and 10, I apply a state-of-the-art machine learning framework known as a neural operator for solving seismic wave equations. The trained neural operator enables full seismic waveform modeling with substantial advancements over conventional numerical techniques including its fast speed, generalizability, and convenient differentiability for full waveform inversion.</p
Distinct Patterns of Overlapping Neural Representation Of Sensorimotor Variables in Primary and Associative Motor Areas: Insights from Chronic Intracortical Recordings in the Human Brain
Get PDFAlthough many of the movements we make are produced without much conscious thought, motor control requires the coordination of multiple brain areas and several complex processes to occur as seamlessly as it does, two of which are primary motor cortex (MC) and posterior parietal cortex (PPC). Traditional views of the organization of these areas have mapped separate parts of the body, or effectors, onto separate areas of cortex. However, recent findings that show extensively overlapping representations of different effectors within small populations of neurons in both motor and posterior parietal cortices have reignited a debate over the organization of each area. The studies in this thesis aim to reconcile these conflicting records through a unique opportunity to directly compare between single neuron recordings in both areas in human participants chronically implanted with intracortical electrode arrays. The functional organization of these areas was investigated during movement of different parts of the body in different contexts. In the first study, I found that the entire body is represented within small patches of both MC and PPC, but with a clear emphasis on a single part of the body in MC. In PPC, although single neurons showed specialization for particular effectors, there were an equal number of neurons specialized for every effector resulting in an equal strength in representation of the population across effectors. In the second study, I investigated how spatial information was represented across different effectors. In particular, it has previously been reported that some areas within PPC represent location of an object in space relative to the position of one's eyes, or in an eye-centered coordinate frame, while other areas represent location in space as relative to the position of one's body, for example a hand-centered coordinate frame. We find that the population in PPC flexibly changes the coordinate frame it encodes the location of a visual target in from hand centered during a reach paradigm to eye-centered during a delayed saccade paradigm. In contrast to the multiple coordinate frames coded by the population in PPC, in MC the population predominantly encoded spatial location in hand-centered coordinates during reaches. The flexibility seen in the population results in PPC motivate the study of Chapter 4, where I explore these changing coordinate frames in more detail at the single neuron level. I found that the distinct coordinate frames are encoded by almost entirely separate sets of neurons, with very few neurons engaged in both task. Overall, these results show clearly distinct organization of motor variables within MC and PPC, and offer important insights into the possible functions of each region both within and beyond motor control. In addition, they highlight a need to continue exploring how neurons within a defined region respond beyond their traditionally associated functional roles
Numerical Analyses of Frictional Sliding on Rate-and-State Interfaces: Fluid Effects, Dynamic Weakening, and Potential-Based Formulation Through Machine Learning
Get PDFRate-and-state friction formulations have been widely used to reproduce a number of observations on faulting in the earth's crust, including earthquake nucleation, creeping fault segments, dynamic earthquake rupture, aftershock sequences, and episodic slow slip events. The formulations have also been used to explain the motion of landslides and glaciers. In this thesis, we use numerical simulations to study various factors that can affect the stability of fault slip with rate-and-state friction, including poroelastic bulk properties and dilatation/compaction of the fault material in the presence of fluids, fault healing, injection rate when there is fluid injected into the fault, as well as dynamic weakening of the fault gouge. We also seek to optimize simulations with rate-and-state friction by developing a potential-based formulation using machine learning.
First, we study the stability of frictional fault slip in the presence of fluids, with a focus on fault loading due to fluid injection into the fault as done in many field and laboratory experiments. In Chapter 2, we present a boundary-integral approach on simulating frictional fault slip in a permeable shear layer surrounded by poroelastic bulk. The approach is then used to explore the effects of poroelasticity and inelastic dilatancy on the stability of frictional fault slip in a fluid-injection problem. We find that the diffusion into and poroelastic properties of the bulk can significantly stabilize fault slip, with the stabilization by bulk diffusion and poroelastic properties comparable to the well-known stabilizing effects of the dilatancy mechanism.
In Chapter 3, we further develop the boundary integral code to allow for purely elastic bulk with the same fluid transport properties as the poroelastic bulk material and consider the effect of fault healing and fluid injection rate on fault slip. We show that the poroelastic bulk effects can be very closely captured by using the undrained value of Poisson’s ratio in an elastic bulk model with the same fluid mass diffusivity of the bulk. We find that fault healing significantly delays the onset of dynamic slip events and restricts their spatial extent, making the initial response of the fault to fluid injection much different than its longer-term response. While this is an expected conclusion, fault healing is not typically accounted for in fluid injection modeling which often uses simpler slip-dependent friction laws. We also find that faster or intermittent injection rates lead to more frequent but more spatially constrained dynamic slip events, for the same injected fluid mass, motivating further investigations into injection strategies that would optimize fault stability.
Second, in Chapter 4, we numerically simulate a laboratory experiment of spontaneous dynamic rupture by developing a 3D finite-element model of the experiment with rate-and-state friction. In the experiment, a dynamic rupture is initiated on a Homalite-100 interface and then produces an intermittent slip in the rock gouge embedded into a part of the interface. Our simulations show that the laboratory findings are consistent with rock gouge which is rate-strengthening at low slip rates but dynamically weakening at high slip rates through the mechanism similar to flash heating. However, to fit the experimental results, the traditional flash-heating formulation needs to be substantially modified, potentially due to effects of localization and delocalization of slip in the rock gouge.
The third part of the thesis focuses on identifying a potential-based formulation for the rate-and-state friction laws. Due to their empirical derivation, the rate-and-state friction laws cannot be written as the gradients of a potential, which leads to difficulties in implicit solution of dynamic frictional problems. In Chapter 5, we present a potential-based formulation for the rate-and-state friction law through Neural Network approximation and training on datasets generated by a one-degree of-freedom spring-slider system with the rate-and-state friction law. The learnt potential is able to reproduce the results with rate-and-state friction law, and indeed facilitates an implicit solution of dynamic problems. However, the training of the potential requires a much larger dataset than fitting the original rate-and-state friction law.
Overall, our modeling significantly advances our understanding of the factors that control stability of frictional sliding on natural faults and suggests promising machine-learning directions in replacing the empirical rate-and-state formulations with the ones based on thermodynamic potentials.</p
Chasing After the Wind: Flow Structure Detection Strategies for Autonomous Mobile Flow Field Measurements
Get PDFModern flow measurement technology enables studies of fluid motion that, half a century ago, would have seemed unfathomable. However, despite staggering capabilities, measuring many natural flows in the field remains challenging. In particular, resolving coherent flow structures within physical scales ranging from meters to kilometers is not readily achieved. This dissertation proposes autonomous mobile flow field measurements (AMFM) as a paradigm for expanding flow field measurement capabilities into this range of scales. In the AMFM framework, a mobile platform such as a drone would identify critical flow structures and follow them autonomously as they evolve; the device would be taught, in a sense, to chase after the wind for the sake of measuring it. The greatest theoretical challenge to AMFM is that of flow structure detection: what, after all, should be identified in the flow? How is it to be measured? Answering these questions is the overarching motivation of this dissertation. In response, two principal contributions are developed. The first is a theoretical approach to gradient estimation labeled Lagrangian gradient regression (LGR), which enables instantaneous and finite-time flow gradients to be approximated from sparse flow observations. The second is a semantic approach to flow measurement, which provides the ability to discern fluid motion from complex natural images using arbitrarily defined flow tracers. Together, these tools enable a range of studies which would be difficult to conduct otherwise. To demonstrate their combined ability, two experiments are performed. The first examines the motion of imperfect surface tracers measured by the proposed methods relative to sub-surface flows measured by conventional techniques. The second experiment analyzes flow features in the Caltech turtle ponds using only tracers naturally occurring on its surface. While it is demonstrated that the methods and results obtained in this work are meritorious in their own right, they also provide a framework from which future AMFM technologies can be built
Classical Representation and Manipulation of Quantum Many Body States and High Dimensional Data
Get PDFThis thesis contains several developments in extending the capability of classical simulations for representing and manipulating quantum many-body states and high dimensional data. In Chapter 1, we introduce the different types of problems considered in quantum chemistry (with ab initio molecular Hamiltonian) and condensed matter physics (with lattice model Hamiltonian) as well as a classical scenario of high-dimensional function integration. In each case, we briefly introduce a corresponding anstaz for representing either the quantum many-body wavefunction or the classical high-dimensional integrand, which provides context for more detailed discussion in subsequent chapters.
Chapter 2 describes a technical improvement on an existing formulation of the coupled cluster method, known as a popular wavefunction ansatz in quantum chemistry, for simulating finite-temperature non-equilibrium ab initio Hamiltonian dynamics. We adapt a technique from zero-temperature dynamics to the non-equilibrium finite-temperature coupled cluster method, thereby restoring conservation laws for 1-particle properties which were previously broken, and stabilizing the numerical behavior of the method for moderate time propagation. We demonstrated the capability of the method on both ab initio molecular systems such as field-driven H2 and electron transport in silicon cell, and model Hamiltonian such as the moderately interacting single impurity Anderson model (SIAM). We were able to perform stable dynamics simulation for sufficient amount of time to extract qualitatively correct physics, such as band population transport in silicon, and Kondo physics in the SIAM.
Chapter 3 and Chapter 4 introduce developments in tensor network state (TNS) methods for lattice model Hamiltonian. Typically, TNS are constructed to correctly represent the entanglement structure of target physical state, whose computation of e.g., amplitude and expectation value, can only be performed approximately. A representative example is the projected entangled pair state (PEPS) for representing ground states on 2-dimensional lattices. In Chapter 3, we describe several aspects in PEPS (and TNS in general) construction and computation, including approximate contraction and derivative computation, as well as encoding of Abelian symmetry and fermion statistics. We also introduce a change of perspective of TNS ansatz that restores its exact variationality which was hitherto considered only approximate due to the need of approximate contraction. With such new perspective on TNS ansatz, Chapter 4 then focuses on stochastic optimization of TNS using variational Monte Carlo (VMC). In particular, we investigate the convergence behavior of first- and second order update methods under stochastic noise, which was in turn affected by several factors such as sample size, system size, wavefunction quality and variational expressivity of the ansatz. We hope that the developments described in Chapter 3 and Chapter 4 can allow efficient large scale PEPS simulation of highly entangled states on 2-dimensional lattices, such as spin liquids, ground state of fermi-Hubbard model, and phases of uniform electron gas.
Chapter 5 introduces a constructive approach for representing high dimensional classical functions with tensor network, and perform integration with approximate contraction. Previous attempts of using tensor network for high dimensional integration typically fit a predetermined form of exactly contractable tensor network to the target function, where error is mainly due to the limited expressivity of the tensor network ansatz. On the other hand, our constructive approach is in principle free of representation error for any function that admits polynomial decomposition into small function blocks. The returned tensor network representation is of arbitrary geometry, where the error is mainly due to approximate contraction, which will benefit greatly from new developments in tenor network approximate contraction techniques.</p
Mechanistic Studies of Membrane Protein Biogenesis at the ER and Mitochondria
Get PDFEukaryotic cells are organized into membrane-enclosed compartments with elaborate networks of integral membrane proteins. From synthesis, localization, and insertion into designated cellular membranes, to proper folding and assembly of the membrane proteins, the successful biogenesis of membrane proteins is crucial for defining the organellar compartments and for overall proteostasis. Recent advances in the field of membrane protein biogenesis in both the endoplasmic reticulum (ER) and the mitochondria have identified novel machineries involved in the membrane insertion step. These are the ER membrane protein complex (EMC) at the ER and the mitochondrial carrier homolog 2 (MTCH2) at the outer mitochondrial membrane (OMM). In this thesis, we employ a combination of biochemical, cell biological, structural, and genetic techniques to explore in mechanistic detail the insertase function of the EMC and MTCH2 at the molecular level. In the first part of the thesis, our work on the EMC maps out the pathway of a tail-anchored (TA) protein through the insertase and revealed a selectivity filter that provides the biochemical basis for how the EMC protects compartment integrity. The selectivity filter of the EMC limits TA protein mislocalization and prevents topological errors of multi-pass membrane proteins. In the second part, ongoing work on MTCH2 reveals the absence of a prominent selectivity mechanism and provides insight into a regulatory mechanism of MTCH2, which seems to be conserved across metazoan MTCH2 homologs
Gene Regulatory Analysis of the Developing Enteric Nervous System of Zebrafish (Danio rerio)
No full textNeural crest cells give rise to the neurons of the enteric nervous system (ENS) that innervate the gastrointestinal tract to regulate gut motility. The immense size and distinct subregions of the gut present a challenge to understanding the spatial organization and sequential differentiation of different neuronal subtypes. To tackle this, we profile enteric neurons and progenitors at single cell resolution during zebrafish embryonic and larval development to provide a near complete picture of transcriptional changes that accompany emergence of ENS neurons throughout the gastrointestinal tract. Multiplex spatial RNA transcript analysis was then used to reveal the temporal order and distinct localization patterns of neuronal subtypes along the length of the gut. Next, we show that functional perturbation of select transcription factors Ebf1a, Gata3 and Satb2 alters the cell fate choice, respectively, of inhibitory, excitatory and serotonergic neuronal subtypes in the developing ENS. To decipher the molecular mechanism underlying the development of ENS, we further performed single cell ATAC-seq to profile the epigenetic landscape of the developing ENS. Together with CUT&RUN results, we found the master regulator Phox2bb harbors extensive binding sites throughout the genome and plays versatile roles in neuronal differentiation, including regulating progenitor gene Sox10, activating transcription factors Phox2a and Insm1b for early neural development and regulating genes Etv1 and Hmx3a for neuronal differentiation. Integrated with single cell RNA-seq analysis, we further reconstruct a putative gene regulatory circuit involving in the specification and maturation of ENS neurons
Fast Algorithms for Spanwise Periodic Incompressible External Flows: From Simulation to Analysis
Get PDFExternal flows over spanwise-homogeneous geometries are ubiquitous in science and engineering applications. In this thesis, we propose algorithms to simulate and analyze these flows using the lattice Green's function (LGF) approach. The LGF is the analytical inverse of a discrete elliptic operator that automatically incorporates exact far-field boundary conditions and minimizes computational expense by allowing snug computational regions encompassing only vortical flow regions. By combining LGFs with adaptive mesh refinement (AMR) and immersed boundary (IB) methods, we present two numerical algorithms specially designed for spanwise periodic incompressible external flows: one to directly solve the nonlinear equations of motion and one to compute stability and resolvent analyses.
For these algorithms, the LGFs of the screened Poisson equation must be computed at runtime. To enable efficient flow simulation and analysis algorithms, we propose a fast numerical algorithm to tabulate these LGFs. We derive convergence results for the algorithms and show that they are orders of magnitude faster than existing algorithms. Armed with the LGF for the screened Poisson equation, we further develop algorithms to solve the Navier-Stokes equations and associated linearized eigenvalue problems.
We present two applications of these algorithms. We perform simulations to validate the starting vortex theory proposed by Pullin and Sader (2021), and we perform stability analyses of flow past a rotating cylinder with a control cylinder in its wake.</p
Ultrafast Quantum State Generation and Measurement in Nonlinear Nanophotonics
Get PDFWhile many physical systems, including superconductors, trapped atoms, molecules, and acoustic resonators can process quantum information, photonics holds several fundamental advantages. Most photonics systems not only offer the convenience of room temperature operation but also shed the scalability limitations imposed by cryogenic and high vacuum environments. Integrated photonics has shrunk room-sized experiments to a chip-scale device while improving performance and versatility. Operating at optical frequencies offers information bandwidths orders of magnitude larger than what is achievable with microwave or trapped atom experiments.
In this thesis, we propose nanophotonic optical parametric amplifiers (OPAs) on a thin-film lithium niobate (TFLN) chip-scale platform for quantum information processing. Through dispersion-engineering, we achieve the distortion-free propagation of ultrafast pulses necessary for information clock rates above 1 THz. We investigate OPAs as ultrashort entangled pair sources and generate biphotons with a 165-fs temporal duration. We show that their generation efficiency and signal-to-noise performance is state-of-the-art at 2 µm and on-par with contemporary telecom-band sources. We explore OPAs as quantum measurement devices, and demonstrate all-optical single-photon level detection with a dead time of 75 fs. Finally, we show that OPAs can be used to recover continuous-variable quantum information by reconstructing the Wigner function of a 2.41 dB squeezed state encoded in a 154-fs pulse. This technique is loss-tolerant and offers a maximum clock speed of 6.5 THz. TFLN hosts a variety of high-performance optical devices including filters, modulators, resonators, III-V gain media, all of which are compatible with OPAs. Our results highlight ultrafast OPAs as the fundamental building blocks needed to realize large-scale circuits for all-optical quantum information processing.</p
Essays on Political Accountability and Representation
Get PDFThis dissertation studies political accountability and representation, two fundamental principles of democratic government. It consists of four independent chapters, each structured as an academic article that addresses a distinct research question. The chapters are organized into two thematic sections. On the one hand, Chapters 1 and 2 study the Question Period, a key institution in Canadian politics, analyzing the behavior of its participants and its role in upholding political accountability and representation. In particular, Chapter 1 assesses how responsive politicians are to the public salience of climate change in determining which topics to address in their Question Period interventions. Chapter 2 proposes a new approach for measuring the quality of answers in political question-and-answer sessions with large language models, using the Question Period as a case study. On the other hand, Chapters 3 and 4 explore the tensions that may arise between political accountability and representation in a context of asymmetric information using theoretical models of political agency with adverse selection. Chapter 3 demonstrates that endogenous challenger entry generally weakens electoral accountability but may paradoxically improve policymaking and voter welfare. Chapter 4 investigates how candidates for elected office can strategically weaken electoral accountability by voluntarily pledging to self-imposed term limits to their benefit and that of voters