Feature detection algorithms in computed images

The problem of sensing a medium by several sensors and retrieving interesting features is a very general one. The basic framework of the problem is generally the same for applications from MRI, tomography, Radar SAR imaging to subsurface imaging, even though the data acquisition processes, sensing geometries and sensed properties are different. In this thesis we introduced a new perspective to the problem of remote sensing and information retrieval by studying the problem of subsurface imaging using GPR and seismic sensors. We have shown that if the sensed medium is sparse in some domain then it can be imaged using many fewer measurements than required by the standard methods. This leads to much lower data acquisition times and better images representing the medium. We have used the ideas from Compressive Sensing, which show that a small number of random measurements about a signal is sufficient to completely characterize it, if the signal is sparse or compressible in some domain. Although we have applied our ideas to the subsurface imaging problem, our results are general and can be extended to other remote sensing applications. A second objective in remote sensing is information retrieval which involves searching for important features in the computed image of the medium. In this thesis we focus on detecting buried structures like pipes, and tunnels in computed GPR or seismic images. The problem of finding these structures in high clutter and noise conditions, and finding them faster than the standard shape detecting methods like the Hough transform is analyzed. One of the most important contributions of this thesis is, where the sensing and the information retrieval stages are unified in a single framework using compressive sensing. Instead of taking lots of standard measurements to compute the image of the medium and search the necessary information in the computed image, a much smaller number of measurements as random projections are taken. The data acquisition and information retrieval stages are unified by using a data model dictionary that connects the information to the sensor data.Ph.D.Committee Chair: McClellan, James H.; Committee Member: Romberg, Justin K.; Committee Member: Scott, Waymond R. Jr.; Committee Member: Vela, Patricio A.; Committee Member: Vidakovic, Bran

Characterization of Ground Deformation Associated with Shallow Groundwater Processes Using Satellite Radar Interferometry

Shallow groundwater processes maylead to ground deformation and even geohazards. With the features of day-and-night accessibility and large-scale coverage, time-series interferometric synthetic aperture radar (InSAR) has proven a useful tool for mapping the deformation over various landscapes at cm to mm level with weekly to monthly updates. However, it has limitations such as, decorrelation,atmospheric artifacts, topographic errors, andunwrapping errors, in particular for the hilly, vegetated, and complicated deformation patterns. In this dissertation, I focus on characterizing the ground deformation over landslides, aquifer systems, and mine tailings impoundment, using the designed advanced time-series InSAR strategy, as well as theinterdisciplinary knowledge of geodesy, hydrology, geophysics, and geology. Northwestern USA has been exposed to extreme landslide hazards due to steep terrain, high precipitation, and loose root support after wildfire. I characterize the rainfall-triggered movements of Crescent Lake landslide, Washington State. The seasonal deformation at the lobe, with larger magnitudes than the downslope riverbank, suggests an amplified hydrological loading effect due to a thicker unconsolidated zone. High-temporal-resolution InSAR and GPS data reveal dynamic landslide motions. Threshold rainfall intensities and durations wet seasons have been associated with observed movement upon shearing: antecedent rainfall triggered precursory slope-normal subsidence, and the consequent increase in pore pressure at the basal surface reduces friction and instigates downslope slip over the course of less than one month. In addition, a quasi-three-dimensional deformation field is created using multiple spaceborne InSAR observations constrained by the topographical slope, and is further used to invert for the complex geometry of landslide basal surface based on mass conservation. Aquifer skeletons deform in response to hydraulic head changes with various time scales of delay and sensitivity. I investigate the spatio-temporal correlation among deformation, hydrological records and earthquake records over Salt Lake Valley, Utah State. A clear long-term and seasonal correlation exists between surface uplift/subsidence and groundwater recharge/discharge, allowing me to quantify hydrogeological properties. Long-term uplift reflects the net pore pressure increase associated with prolonged water recharge, probably decades ago. The distributions of previously and newly mapped faults suggest that the faultsdisrupt the groundwater flow andpartition hydrological units. Mine tailings gradual settle as the pore pressure dissipates and the terrain subsides, andtailings embankment failures can be extremely hazardous. I investigate the dynamics of consolidation settlement over the tailings impoundment in the vicinity of Great Salt Lake, Utah State, as well as its associated impacts to the surrounding infrastructures. Largest subsidence has been observed around the low-permeable decant pond clay at the northeast corner.The geotechnical consolidation model reveals and predicts the long-term exponentially decaying settlement process. My studies have demonstrated that InSAR methods can advance our understanding about the potential anthropogenic impacts and natural hydrological modulations on various geodynamic settings in geodetic time scale

Incremental elasticity for array databases

Relational databases benefit significantly from elasticity, whereby they execute on a set of changing hardware resources provisioned to match their storage and processing requirements. Such flexibility is especially attractive for scientific databases because their users often have a no-overwrite storage model, in which they delete data only when their available space is exhausted. This results in a database that is regularly growing and expanding its hardware proportionally. Also, scientific databases frequently store their data as multidimensional arrays optimized for spatial querying. This brings about several novel challenges in clustered, skew-aware data placement on an elastic shared-nothing database. In this work, we design and implement elasticity for an array database. We address this challenge on two fronts: determining when to expand a database cluster and how to partition the data within it. In both steps we propose incremental approaches, affecting a minimum set of data and nodes, while maintaining high performance. We introduce an algorithm for gradually augmenting an array database's hardware using a closed-loop control system. After the cluster adds nodes, we optimize data placement for n-dimensional arrays. Many of our elastic partitioners incrementally reorganize an array, redistributing data only to new nodes. By combining these two tools, the scientific database efficiently and seamlessly manages its monotonically increasing hardware resources.Intel Corporation (Science and Technology Center for Big Data

A Data-Virtualization System for Large Model Visualization

Interactive scientific visualizations are widely used for the visual exploration and examination of physical data resulting from measurements or simulations. Driven by technical advancements of data acquisition and simulation technologies, especially in the geo-scientific domain, large amounts of highly detailed subsurface data are generated. The oil and gas industry is particularly pushing such developments as hydrocarbon reservoirs are increasingly difficult to discover and exploit. Suitable visualization techniques are vital for the discovery of the reservoirs as well as their development and production. However, the ever-growing scale and complexity of geo-scientific data sets result in an expanding disparity between the size of the data and the capabilities of current computer systems with regard to limited memory and computing resources. In this thesis we present a unified out-of-core data-virtualization system supporting geo-scientific data sets consisting of multiple large seismic volumes and height-field surfaces, wherein each data set may exceed the size of the graphics memory or possibly even the main memory. Current data sets fall within the range of hundreds of gigabytes up to terabytes in size. Through the mutual utilization of memory and bandwidth resources by multiple data sets, our data-management system is able to share and balance limited system resources among different data sets. We employ multi-resolution methods based on hierarchical octree and quadtree data structures to generate level-of-detail working sets of the data stored in main memory and graphics memory for rendering. The working set generation in our system is based on a common feedback mechanism with inherent support for translucent geometric and volumetric data sets. This feedback mechanism collects information about required levels of detail during the rendering process and is capable of directly resolving data visibility without the application of any costly occlusion culling approaches. A central goal of the proposed out-of-core data management system is an effective virtualization of large data sets. Through an abstraction of the level-of-detail working sets, our system allows developers to work with extremely large data sets independent of their complex internal data representations and physical memory layouts. Based on this out-of-core data virtualization infrastructure, we present distinct rendering approaches for specific visualization problems of large geo-scientific data sets. We demonstrate the application of our data virtualization system and show how multi-resolution data can be treated exactly the same way as regular data sets during the rendering process. An efficient volume ray casting system is presented for the rendering of multiple arbitrarily overlapping multi-resolution volume data sets. Binary space-partitioning volume decomposition of the bounding boxes of the cube-shaped volumes is used to identify the overlapping and non-overlapping volume regions in order to optimize the rendering process. We further propose a ray casting-based rendering system for the visualization of geological subsurface models consisting of multiple very detailed height fields. The rendering of an entire stack of height-field surfaces is accomplished in a single rendering pass using a two-level acceleration structure, which combines a minimum-maximum quadtree for empty-space skipping and sorted lists of depth intervals to restrict ray intersection searches to relevant height fields and depth ranges. Ultimately, we present a unified rendering system for the visualization of entire geological models consisting of highly detailed stacked horizon surfaces and massive volume data. We demonstrate a single-pass ray casting approach facilitating correct visual interaction between distinct translucent model components, while increasing the rendering efficiency by reducing processing overhead of potentially invisible parts of the model. The combination of image-order rendering approaches and the level-of-detail feedback mechanism used by our out-of-core data-management system inherently accounts for occlusions of different data types without the application of costly culling techniques. The unified out-of-core data-management and virtualization infrastructure considerably facilitates the implementation of complex visualization systems. We demonstrate its applicability for the visualization of large geo-scientific data sets using output-sensitive rendering techniques. As a result, the magnitude and multitude of data sets that can be interactively visualized is significantly increased compared to existing approaches

Multilayer representation for geological information systems

En esta tesis se propone el uso de la Representación de Terrenos Basada en Stacks (SBRT, de sus siglas en inglés) para datos geológicos volumétricos. Esta estructura de datos codifica estructuras geológicas representadas como stacks utilizando una compacta representación de datos. A continuación, hemos formalizado la SBRT con un esquema basado en la teoría de geo-átomos para proporcionar una definición precisa y determinar sus propiedades. Esta tesis también introduce una nueva estructura de datos llamada QuadStack, mejorando los resultados de compresión proporcionados por la SBRT al aprovechar la redundancia de información que a menudo se encuentra en los datos distribuidos por capas. También se han proporcionado métodos de visualización para estas representaciones basados en el conocido algoritmo de visualización raycasting. Al mantener los datos en todo momento en la memoria de la GPU de forma compacta, los métodos propuestos son lo suficientemente rápidos como para proporcionar velocidades de visualización interactivas.In this thesis we propose the use of the Stack-Based Representation of Terrains (SBRT) for volumetric geological data. This data structure encodes geological structures represented as stacks using a compact data representation. The SBRT is further formalized with a framework based on the geo-atom theory to provide a precise definition and determine its properties. Also, we introduce QuadStacks, a novel data structure that improves the compression results provided by the SBRT, by exploiting in its data arrangement the redundancy often found in layered dataset. This thesis also provides direct visualization methods for the SBR and QuadStacks based on the well-known raycasting algorithm. By keeping the whole dataset in the GPU in a compact way, the methods are fast enough to provide real-time frame rates.Tesis Univ. Jaén. Departamento de Informática. Leída el 19 de septiembre de 2019

Activation of optimally and unfavourably oriented faults in a uniform local stress field during the 2011 Prague, Oklahoma, sequence

The orientations of faults activated relative to the local principal stress directions can provide insights into the role of pore pressure changes in induced earthquake sequences. Here, we examine the 2011 M 5.7 Prague earthquake sequence that was induced by nearby wastewater disposal. We estimate the local principal compressive stress direction near the rupture as inferred from shear wave splitting measurements at spatial resolutions as small as 750 m. We find that the dominant azimuth observed is parallel to previous estimates of the regional compressive stress with some secondary azimuths oriented subparallel to the strike of the major fault structures. From an extended catalogue, we map ten distinct fault segments activated during the sequence that exhibit a wide array of orientations. We assess whether the five near-vertical fault planes are optimally oriented to fail in the determined stress field. We find that only two of the fault planes, including the M   5.7 main shock fault, are optimally oriented. Both the M 4.8 foreshock and M   4.8 aftershock occur on fault planes that deviate 20–29° from the optimal orientation for slip. Our results confirm that induced event sequences can occur on faults not optimally oriented for failure in the local stress field. The results suggest elevated pore fluid pressures likely induced failure along several of the faults activated in the 2011 Prague sequence

Three-dimensional anatomical atlas of the human body

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsAnatomical atlases allow mapping the anatomical structures of the human body. Early versions of these systems consisted of analogic representations with informative text and labelled images of the human body. With the advent of computer systems, digital versions emerged and the third dimension was introduced. Consequently, these systems increased their efficiency, allowing more realistic visualizations with improved interactivity. The development of anatomical atlases in geographic information systems (GIS) environments allows the development of platforms with a high degree of interactivity and with tools to explore and analyze the human body. In this thesis, a prototype for the human body representation is developed. The system includes a 3D GIS topological model, a graphical user interface and functions to explore and analyze the interior and the surface of the anatomical structures of the human body. The GIS approach relies essentially on the topological characteristics of the model and on the kind of available functions, which include measurement, identification, selection and analysis. With the incorporation of these functions, the final system has the ability to replicate the kind of information provided by the conventional anatomical atlases and also provides a higher level of functionality, since some of the atlases limitations are precisely features offered by GIS, namely, interactive capabilities, multilayer management, measurement tools, edition mode, allowing the expansion of the information contained in the system, and spatial analyzes

Citizen seismology helps decipher the 2021 Haiti earthquake

5 pages, 4 figures, supplementary materials https://doi.org/10.1126/science.abn1045.-- Data and materials availability: All data and code used in this study are openly available. RADAR data can be obtained through ESA (Sentinel) or JAXA (Alos-2). Aftershock data can be obtained from https://ayiti.unice.fr/ayiti-seismes/ (7). The codes used to process or model the data are published and public (8). The catalog of high-precision earthquake relocated with the NLL-SSST-coherence procedure (SM4) is available as supplementary dataOn 14 August 2021, the moment magnitude (Mw) 7.2 Nippes earthquake in Haiti occurred within the same fault zone as its devastating 2010 Mw 7.0 predecessor, but struck the country when field access was limited by insecurity and conventional seismometers from the national network were inoperative. A network of citizen seismometers installed in 2019 provided near-field data critical to rapidly understand the mechanism of the mainshock and monitor its aftershock sequence. Their real-time data defined two aftershock clusters that coincide with two areas of coseismic slip derived from inversions of conventional seismological and geodetic data. Machine learning applied to data from the citizen seismometer closest to the mainshock allows us to forecast aftershocks as accurately as with the network-derived catalog. This shows the utility of citizen science contributing to our understanding of a major earthquakeThis work was supported by the Centre National de la Recherche Scientifique (CNRS) and the Institut de Recherche pour le Développement (IRD) through their “Natural Hazard” program (E.C., S.S., T.M., B.D., F.C., J.P.A., J.C., A.D., D.B., S.P.); the FEDER European Community program within the Interreg Caraïbes “PREST” project (E.C., S.S., D.B.); Institut Universitaire de France (E.C., R.J.); Université Côte d’Azur and the French Embassy in Haiti (S.P.); the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant no. 758210, Geo4D project to R.J. and grant no. 805256 to Z.D.); the French National Research Agency (project ANR-21-CE03-0010 “OSMOSE” to E.C. and ANR-15-IDEX-01 “UCAJEDI Investments in the Future” to Q.B.); the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant no. 949221 to Q.B.); and HPC resources of IDRIS (under allocations 2020-AD011012142, 2021-AP011012536, and 2021-A0101012314 to Q.B.With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)Peer reviewe