Automatic Decomposition of Geodetic Time Series for Studies of Ground Deformation

Geodetic measurements of surface deformation have been used for several decades to study how the Earth's surface responds to a wide range of geophysical processes. Geodetic time series acquired over a finite spatial extent can be used to quantify the time dependence of surface strain for a wide range of spatial and temporal scales. In this thesis, we present a new method for automatically decomposing geodetic time series into temporal components corresponding to different geophysical processes. This method relies on constructing an overcomplete temporal dictionary of reference functions such that any geodetic signal can be described by a linear combination of the functions in the dictionary. By solving a linear least squares problem with sparsity-inducing regularization, we can limit the total number of dictionary elements needed to reconstruct a signal. In Chapter 2, we present the development of this method in the context of transient detection, where we define transient deformation as nonperiodic, nonsecular accumulation of strain in the crust. The sparsity regularization term automatically localizes the dominant timescales and onset times of any transient signals. We apply this method to Global Positioning System (GPS) data for a slow slip event in the Cascadia subduction zone while incorporating a spatial weighting scheme that filters for spatially coherent signals. In Chapter 3, we use a combination of unique space geodetic measurements and seismic observations to study the 2014 collapse of Bárðarbunga Caldera in Iceland associated with a major eruption event. The eruption sequence, which involved deflation of a magma chamber underneath the caldera and emplacement of a dike leading to lava flow, resulted in rapid subsidence of the glacier surface overlying the caldera and wide-scale ground deformation encompassing the rift zone associated with the dike emplacement. We present a model of the collapse that suggests that the majority of the observed subsidence occurs aseismically via a deflating sill-like magma chamber. In Chapter 4, we extend upon the transient detection framework presented in Chapter 2 to study complex surface deformation over groundwater basins near Los Angeles, California. We develop a distributed time series analysis framework based on the sparse estimation techniques of Chapter 2 and apply it to an 18-year interferometric synthetic aperture radar (InSAR) time series covering the Los Angeles area. We compare long- and short-term ground deformation signals to hydraulic head data from monitoring wells to understand the mechanical link between pressure variations in subsurface aquifers and observed ground deformation

Development of multi-channel radio frequency technology for sodium and potassium magnetic resonance imaging at 7.0 Tesla: design and clinical application

Sodium (Na+) and potassium (K+) ions play key roles in the physiology and metabolism of living cells. Primary active transport, which is carried out by sodium/potassium pumps (Na+/K+-ATPase), maintains the ion concentration gradient between intra- and extracellular space. Changes in Na+ and K+ concentration (and distribution) might reflect ongoing pathological processes within a tissue what might be relevant for various types of cardiovascular and ocular disorders. Ultrahigh magnetic resonance imaging (UHF-MRI) provides new opportunities to non-invasively investigate changes in Na+ and K+ concentrations with spatial resolution and within total scan times that are reaching ranges acceptable for clinical applications. Despite an intrinsic to UHF-MRI gain in signal-to-noise ratio (SNR), nuclear magnetic resonance (NMR) signals of sodium (23Na) and potassium (39K) being detected remain very weak. The NMR sensitivity of 23Na is about 9%, while 39K is 0.05% the one of proton (1H). Therefore, radio frequency (RF) coils, which are used to capture these signals, should be optimized for a given anatomical structure in order to improve the SNR. The goal of this work is to develop two separate RF coils which would enable high-resolution in vivo 23Na MRI of the human eye and in vivo 39K MRI of the human heart at 7.0 Tesla. To achieve these goals, the six-channel transmit receive 23Na coil array and a four/two-channel 1H/39K coil array have been designed, built and tested. The performance of the developed RF coils has been evaluated using RF circuit, electromagnetic field (EMF) and specific absorption rate (SAR) simulations. Phantom as well as in vivo experiments involving several healthy volunteers have been conducted. The experiments have revealed that the developed six-channel transmit/receive coil array supports in vivo 23Na MRI of the human eye with nominal spatial resolution of (1.0 x 1.0 x 1.0) mm3 and within scan time of 10 minutes. This work also demonstrates that the proposed four/two-channel 1H/39K coil array enabled obtaining the world’s first in vivo 39K image of the human heart with nominal spatial resolution of (14.5 x 14.5 x 14.5) mm3 and within total scan time of 30 minutes. The results demonstrate that sodium content in the lens is distinguishable from sodium content in the aqueous and vitreous humor. There is strong evidence that sodium concentration in the compartments of the eye should change in diseases like cataract, glaucoma and ocular melanoma. The broad roles of this element in processes related to eye physiology suggest a range of questions for ophthalmological investigations. This work also shows that in vivo potassium MRI of the human heart is feasible. Previous reports, suggesting that potassium concentration is expected to alter in arrhythmia, ischemia or irreversible injury to miocytes, provides encouragement for future in vivo studies involving patients who suffer from various cardiovascular disorders.Natrium- (Na+) und Kaliumionen (K+) spielen kritische Rollen in der Physiologie und dem Metabolismus lebender Zellen. Der primär-aktive Transport, der den Ionenkonzentrationsgradienten zwischen intra- und extrazellulärer Maxtrix aufrecht hält, wird von Natrium/Kaliumpumpen durchgeführt. Änderungen der Na+ - und K+-Konzentration und -Verteilung könne auf pathologische Prozesse in einem Gewebe zurückzuführen sein. Dies ist sehr relevant für eine Vielzahl von Krankheiten, einschließlich Herz-Kreislauf- und Augenerkrankungen. Ultrahohe Magnetresonanztomographie (UHF-MRT) bietet neue Möglichkeiten zur nicht-invasiven Untersuchung von Änderungen in Na+- und K+-Konzentrationen mit hoher räumlicher Auflösung, die innerhalb für klinische Anwendungen akzeptabler Gesamtabtastzeiten durchgeführt werden können. Trotz eines UHF-MRT-spezifischen Anstiegs des Signal-Rausch-Verhältnisses (SNR) bleiben die nachgewiesenen kernmagnetischen Resonanzsignale (NMR) von Natrium (23Na) und Kalium (39K) sehr schwach. Die NMR-Empfindlichkeit von 23Na beträgt etwa 9%, während 39K - 0.05% des Protons (1H) beträgt. Daher sollten Hochfrequenzspulen (HF), die zur Erfassung dieser Signale verwendet werden, für eine bestimmte anatomische Struktur optimiert werden, um das SNR zu verbessern. Ziel dieser Arbeit ist es, zwei separate HF-Spulen zu entwickeln, die eine hochauflösende in vivo 23Na-MRT des menschlichen Auges und eine in vivo 39K-MRT des menschlichen Herzens bei 7.0 Tesla ermöglichen. Um diese Ziele zu erreichen, wurden das/ein 23Na-Spulenarray mit sechs Sende- und Empfangskanälen und ein 1H/39K-Spulenarray mit vier/zwei Kanälen entworfen, gebaut und getestet. Es wurden Experimente an Messphantomen sowie In-vivo-Testmessungen von mehreren gesunden Freiwilligen durchgeführt. Die Messungen haben gezeigt, dass das in dieser Arbeit entwickelte 6-Kanal-Sende- / Empfangsspulenarray in vivo 23Na MRT des menschlichen Auges mit einer nominalen räumlichen Auflösung von (1.0x1.0x1.0) mm3 innerhalb der Scanzeit von 10 Minuten ermöglicht. Diese Arbeit zeigt auch, dass es dank des 1H/39K -Spulenarray mit vier/zwei Kanälen gelang, das weltweit erste 39K -Bild des menschlichen Herzens in vivo mit einer nominalen räumlichen Auflösung von (14.5x14.5x14.5) mm3 und einer Gesamtabtastzeit von zu 30 Minuten aufzunehmen. Die Ergebnisse zeigen, dass der Natriumgehalt in der Linse vom Natriumgehalt im wässrigen und im Glaskörper unterscheidbar ist. Es gibt starke Hinweise darauf, dass sich die Natriumkonzentration in den Kompartimenten des Auges bei Erkrankungen wie Katarakt, Glaukom und okularem Melanom ändern sollte. Diese Arbeit zeigt auch, dass eine in vivo Kalium-MRT des menschlichen Herzens möglich ist. Frühere Berichte, aus denen hervorgeht, dass sich die Kaliumkonzentration voraussichtlich bei Arrhythmie, Ischämie oder irreversiblen Verletzungen der Miozyten ändert, ermutigen zukünftige In-vivo-Studien mit Patienten, die an verschiedenen Herz-Kreislauf-Erkrankungen leiden

Advanced Techniques for Ground Penetrating Radar Imaging

Ground penetrating radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in non-destructive testing (NDT), since it is able to detect both metallic and nonmetallic targets. GPR for NDT has been successfully introduced in a wide range of sectors, such as mining and geology, glaciology, civil engineering and civil works, archaeology, and security and defense. In recent decades, improvements in georeferencing and positioning systems have enabled the introduction of synthetic aperture radar (SAR) techniques in GPR systems, yielding GPR–SAR systems capable of providing high-resolution microwave images. In parallel, the radiofrequency front-end of GPR systems has been optimized in terms of compactness (e.g., smaller Tx/Rx antennas) and cost. These advances, combined with improvements in autonomous platforms, such as unmanned terrestrial and aerial vehicles, have fostered new fields of application for GPR, where fast and reliable detection capabilities are demanded. In addition, processing techniques have been improved, taking advantage of the research conducted in related fields like inverse scattering and imaging. As a result, novel and robust algorithms have been developed for clutter reduction, automatic target recognition, and efficient processing of large sets of measurements to enable real-time imaging, among others. This Special Issue provides an overview of the state of the art in GPR imaging, focusing on the latest advances from both hardware and software perspectives

A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images

Speckle is a granular disturbance, usually modeled as a multiplicative noise, that affects synthetic aperture radar (SAR) images, as well as all coherent images. Over the last three decades, several methods have been proposed for the reduction of speckle, or despeckling, in SAR images. Goal of this paper is making a comprehensive review of despeckling methods since their birth, over thirty years ago, highlighting trends and changing approaches over years. The concept of fully developed speckle is explained. Drawbacks of homomorphic filtering are pointed out. Assets of multiresolution despeckling, as opposite to spatial-domain despeckling, are highlighted. Also advantages of undecimated, or stationary, wavelet transforms over decimated ones are discussed. Bayesian estimators and probability density function (pdf) models in both spatial and multiresolution domains are reviewed. Scale-space varying pdf models, as opposite to scale varying models, are promoted. Promising methods following non-Bayesian approaches, like nonlocal (NL) filtering and total variation (TV) regularization, are reviewed and compared to spatial- and wavelet-domain Bayesian filters. Both established and new trends for assessment of despeckling are presented. A few experiments on simulated data and real COSMO-SkyMed SAR images highlight, on one side the costperformance tradeoff of the different methods, on the other side the effectiveness of solutions purposely designed for SAR heterogeneity and not fully developed speckle. Eventually, upcoming methods based on new concepts of signal processing, like compressive sensing, are foreseen as a new generation of despeckling, after spatial-domain and multiresolution-domain method

Patch-Like Reduction (PLR): A SAR offset tracking amplitude filter for deformation monitoring

As complementary to Synthetic Aperture Radar (SAR) Differential Interferometry (DInSAR), SAR Offset Tracking (OT) is an efficient tool for large ground deformation monitoring in situations when DInSAR cannot work. However, SAR images are affected by speckle noise and some strong point-like scatters which can cause what is known as Patch Like (PL), a kind of errors that can be seen as homogeneous patches of almost constant deformation in the results. These errors are clearly visible in the results as non-consistent deformations along time, but they are difficult to detect with the traditional metrics that evaluate the cross-correlation results, like the Signal to Noise Ratio (SNR). This paper addresses this problem and proposes a simple amplitude filter to reduce PL named as Patch Like Reduction (PLR). The main idea is to find a sensor and scene independent threshold to remove the high amplitude pixels prone to cause PL. Five different SAR data sets and in-field GPS measurements are used to determine the optimal threshold and evaluate the performance of the proposed method. The results show that PL effects can be reduced with the proposed amplitude filter. The processing parameters of the improved OT processing chain are optimized as well to preserve the results resolution as much as possible.This work has been financially supported by China Scholarship Council (Grant No. 201806420035), the Spanish Ministry of Science and Innovation (MCINN) and the State Research Agency (AEI) project PID2020-117303GB-C21 MCIN/AEI/10.13039/501100011033. This work has also been financially supported by the Natural Science Foundation of China (Grant No. 42004011), China Postdoctoral Science Foundation (Grant No. 2020M671646), Centro para el Desarrollo Tecnológico Industrial and Ministry of Science and Technology of the People’s Republic of China (Spanish-Chinese CHINEKA project No. 2022YFE0102600), and the Ministry of Education of the People’s Republic of China (Construction Program of Space-Air-Ground-Well Cooperative Awareness Spatial Information Project B20046)Peer ReviewedPostprint (published version

Unsupervised Classification of SAR Images using Hierarchical Agglomeration and EM

We implement an unsupervised classification algorithm for high resolution Synthetic Aperture Radar (SAR) images. The foundation of algorithm is based on Classification Expectation-Maximization (CEM). To get rid of two drawbacks of EM type algorithms, namely the initialization and the model order selection, we combine the CEM algorithm with the hierarchical agglomeration strategy and a model order selection criterion called Integrated Completed Likelihood (ICL). We exploit amplitude statistics in a Finite Mixture Model (FMM), and a Multinomial Logistic (MnL) latent class label model for a mixture density to obtain spatially smooth class segments. We test our algorithm on TerraSAR-X data

Unsupervised Learning of Generalized Gamma Mixture Model with Application in Statistical Modeling of High-Resolution SAR Images

International audienceThe accurate statistical modeling of synthetic aperture radar (SAR) images is a crucial problem in the context of effective SAR image processing, interpretation and application. In this paper a semi-parametric approach is designed within the framework of finite mixture models based on the generalized Gamma distribution (GΓD) in view of its flexibility and compact form. Specifically, we develop a generalized Gamma mixture model (GΓMM) to implement an effective statistical analysis of high-resolution SAR images and prove the identifiability of such mixtures. A low-complexity unsupervised estimation method is derived by combining the proposed histogram-based expectation-conditional maximization (ECM) algorithm and the Figueiredo-Jain algorithm. This results in a numerical maximum likelihood (ML) estimator that can simultaneously determine the ML estimates of component parameters and the optimal number of mixture components. Finally, the state-of-the-art performance of this proposed method is verified by experiments with a wide range of high-resolution SAR images. Index Terms Synthetic aperture radar (SAR) images, finite mixture model, generalized Gamma distribution, expectation-conditional maximization (ECM) algorithm, minimum message length (MML), probability density function estimation , unsupervised learning

Synthetic Aperture Radar Image Classification via Mixture Approaches

International audienceIn this paper we focus on the fundamental synthetic aperture radars (SAR) image processing problem of supervised classification. To address it we consider a statistical finite mixture approach to probability density function estimation. We develop a generalized approach to address the problem of mixture estimation and consider the use of several different classes of distributions as the base for mixture approaches. This allows performing the maximum likelihood classification which is then refined by Markov random field approach, and optimized by graph cuts. The developed method is experimentally validated on high resolution SAR imagery acquired by Cosmo-SkyMed and TerraSAR-X satellite sensors