Evaluation of aircraft microwave data for locating zones for well stimulation and enhanced gas recovery

Imaging radar was evaluated as an adjunct to conventional petroleum exploration techniques, especially linear mapping. Linear features were mapped from several remote sensor data sources including stereo photography, enhanced LANDSAT imagery, SLAR radar imagery, enhanced SAR radar imagery, and SAR radar/LANDSAT combinations. Linear feature maps were compared with surface joint data, subsurface and geophysical data, and gas production in the Arkansas part of the Arkoma basin. The best LANDSAT enhanced product for linear detection was found to be a winter scene, band 7, uniform distribution stretch. Of the individual SAR data products, the VH (cross polarized) SAR radar mosaic provides for detection of most linears; however, none of the SAR enhancements is significantly better than the others. Radar/LANDSAT merges may provide better linear detection than a single sensor mapping mode, but because of operator variability, the results are inconclusive. Radar/LANDSAT combinations appear promising as an optimum linear mapping technique, if the advantages and disadvantages of each remote sensor are considered

Kinematic and Tectonic Significance of the Fold- and Fault- Related Fracture Systems in the Zagros Mountains, Southern Iran

Enhancement methods applied on various satellite images (ASTER, ETM and RADAR SAT-1) facilitated the identification and mapping of tectonic fractures in the Zagros fold-and-thrust belt in southwest Iran. The results of the fracture analysis on these enhanced images reveal four principal fracture sets within each fold structure: (i) an axial set defined by normal faults oriented parallel to the fold axial trace, (ii) a cross-axial, extensional fracture set oriented perpendicular to the fold axial trace, (iii) and two sets of intersecting shear fractures, oriented at an acute angle to the cross-axial set. Study of the enhanced images also revealed five fracture sets along the Kazerun fault zone: (i) Riedel R- and R\u27-shear fracture sets, (ii) extensional T fracture set oriented at a high angle to the trace of the main Kazerun fault, (iii) oblique, synthetic P-shear fracture set, at a low angle to the trace of the main Kazerun fault, and (iv) synthetic Y-shear displacement fracture set, oriented sub-parallel to the main trace of the fault. The estimated mean azimuths of the shortening that developed the fold- and fault-related fracture systems are remarkably close, and are oriented perpendicular to the general NW-SE trend of the Zagros fold-and-thrust belt. The sampling and analysis of the fold- and fault-related fracture systems were done in a GIS environment. This study shows that an analysis of enhanced satellite images can reveal significant information on the deformation style, timing, and kinematics of the Zagros fold-and-thrust belt. This study suggests that the Zagros orogenic belt, which has mainly been forming since Miocene, due to the convergence of the Iranian and Arabian subplates, has evolved both by thin- and thick-skinned tectonics. Reconfiguration of the Precambrian basement blocks, and the ensuing slip and rotation along the Precambrian faults during the Zagros orogeny, have deformed the folds, and redistributed the fold-related fractures through rigid-body rotation

Structural Response of Slotted Waveguide Antenna Stiffened Structure Components under Compression

The Slotted Waveguide Antenna Stiffened Structure (SWASS) is an aircraft system that can provide the capabilities of a stiffened panel skin structure and a slotted waveguide radar antenna simultaneously. The system made from carbon fiber reinforced polymers is designed around a 10 GHz radar frequency in the X-band range and uses a WR-90 waveguide as a baseline for design. The system is designed for integration into fuselage or wing sections of intelligence, surveillance, and reconnaissance (ISR) aircraft and would increase the system performance through the availability of increased area and decreased system weight. Elemental parts of the SWASS structure were tested in compression after preliminary testing was completed for material characterization of a resin reinforced plain woven carbon fiber fabric made from Grafil 34-700 fibers and a Tencate RS-36 resin with a resin mass ratio of 30%. Testing included finite element stress and strain field characterization of seven single slot configurations, and results showed the longitudinal 90° slot was the best structural slot by about 30% in terms of maximum von Mises stress. Single waveguides were tested in the non-slotted configuration and a configuration including a five longitudinal slot array in one waveguide wall. Finite element results were compared with experimental results and showed good comparisons in all areas. The slot array was determined to have a decrease in nonlinear limit load of 8% from the finite element simulations and 12% from the experimental results. All waveguides showed the characteristics of local wall buckling as the initial failure mechanism and had significant buckling features before ultimate material failure occurred. Nonlinear limit load values were only slightly lower than linear bifurcation values, by less than 1% for both the slotted and non-slotted configurations. The imperfections from laboratory preparation caused a drop in the predicted limit load by about 30% showing the need for extreme care in advanced composite construction. Overall, results proved meaningful and the degradation in compressive performance due to the slot array is acceptable and promising. Future research is encouraged in the form of material tailoring, panel integration, and system optimization among others

Investigating Key Techniques to Leverage the Functionality of Ground/Wall Penetrating Radar

Ground penetrating radar (GPR) has been extensively utilized as a highly efficient and non-destructive testing method for infrastructure evaluation, such as highway rebar detection, bridge decks inspection, asphalt pavement monitoring, underground pipe leakage detection, railroad ballast assessment, etc. The focus of this dissertation is to investigate the key techniques to tackle with GPR signal processing from three perspectives: (1) Removing or suppressing the radar clutter signal; (2) Detecting the underground target or the region of interest (RoI) in the GPR image; (3) Imaging the underground target to eliminate or alleviate the feature distortion and reconstructing the shape of the target with good fidelity. In the first part of this dissertation, a low-rank and sparse representation based approach is designed to remove the clutter produced by rough ground surface reflection for impulse radar. In the second part, Hilbert Transform and 2-D Renyi entropy based statistical analysis is explored to improve RoI detection efficiency and to reduce the computational cost for more sophisticated data post-processing. In the third part, a back-projection imaging algorithm is designed for both ground-coupled and air-coupled multistatic GPR configurations. Since the refraction phenomenon at the air-ground interface is considered and the spatial offsets between the transceiver antennas are compensated in this algorithm, the data points collected by receiver antennas in time domain can be accurately mapped back to the spatial domain and the targets can be imaged in the scene space under testing. Experimental results validate that the proposed three-stage cascade signal processing methodologies can improve the performance of GPR system

Clutter removal of near-field UWB SAR imaging for pipeline penetrating radar

Recently, ultrawideband (UWB) near-field synthetic aperture radar (SAR) imaging has been proposed for pipeline penetrating radar applications thanks to its capability in providing suitable resolution and penetration depth. Because of geometrical restrictions, there are many complicated sources of clutter in the pipe. However, this issue has not been investigated yet. In this article, we investigate some well-known clutter removal algorithms using full-wave simulated data and compare their results considering image quality, signal to clutter ratio and contrast. Among candidate algorithms, two-dimensional singular spectrum analysis (2-D SSA) shows a good potential to improve the signal to clutter ratio. However, basic 2-D SSA produces some artifacts in the image. Therefore, to mitigate this issue, we propose “modified 2-D SSA.” After developing the suitable clutter removal algorithm, wepropose a complete algorithm chain for pipeline imaging. An UWB nearfieldSARmonitoring system including anUWBM-sequence sensor and automatic positioner are implemented and the image of drilled perforations in a concrete pipe mimicking oil well structure as a case study is reconstructed to test the proposed algorithm. Compared to the literature, a comprehensive near-field SAR imaging algorithm including new clutter removal is proposed and its performance is verified by obtaining high-quality images in experimental results

Sparse and Redundant Representations for Inverse Problems and Recognition

Sparse and redundant representation of data enables the description of signals as linear combinations of a few atoms from a dictionary. In this dissertation, we study applications of sparse and redundant representations in inverse problems and object recognition. Furthermore, we propose two novel imaging modalities based on the recently introduced theory of Compressed Sensing (CS). This dissertation consists of four major parts. In the first part of the dissertation, we study a new type of deconvolution algorithm that is based on estimating the image from a shearlet decomposition. Shearlets provide a multi-directional and multi-scale decomposition that has been mathematically shown to represent distributed discontinuities such as edges better than traditional wavelets. We develop a deconvolution algorithm that allows for the approximation inversion operator to be controlled on a multi-scale and multi-directional basis. Furthermore, we develop a method for the automatic determination of the threshold values for the noise shrinkage for each scale and direction without explicit knowledge of the noise variance using a generalized cross validation method. In the second part of the dissertation, we study a reconstruction method that recovers highly undersampled images assumed to have a sparse representation in a gradient domain by using partial measurement samples that are collected in the Fourier domain. Our method makes use of a robust generalized Poisson solver that greatly aids in achieving a significantly improved performance over similar proposed methods. We will demonstrate by experiments that this new technique is more flexible to work with either random or restricted sampling scenarios better than its competitors. In the third part of the dissertation, we introduce a novel Synthetic Aperture Radar (SAR) imaging modality which can provide a high resolution map of the spatial distribution of targets and terrain using a significantly reduced number of needed transmitted and/or received electromagnetic waveforms. We demonstrate that this new imaging scheme, requires no new hardware components and allows the aperture to be compressed. Also, it presents many new applications and advantages which include strong resistance to countermesasures and interception, imaging much wider swaths and reduced on-board storage requirements. The last part of the dissertation deals with object recognition based on learning dictionaries for simultaneous sparse signal approximations and feature extraction. A dictionary is learned for each object class based on given training examples which minimize the representation error with a sparseness constraint. A novel test image is then projected onto the span of the atoms in each learned dictionary. The residual vectors along with the coefficients are then used for recognition. Applications to illumination robust face recognition and automatic target recognition are presented

Geodetic monitoring of complex shaped infrastructures using Ground-Based InSAR

In the context of climate change, alternatives to fossil energies need to be used as much as possible to produce electricity. Hydroelectric power generation through the utilisation of dams stands out as an exemplar of highly effective methodologies in this endeavour. Various monitoring sensors can be installed with different characteristics w.r.t. spatial resolution, temporal resolution and accuracy to assess their safe usage. Among the array of techniques available, it is noteworthy that ground-based synthetic aperture radar (GB-SAR) has not yet been widely adopted for this purpose. Despite its remarkable equilibrium between the aforementioned attributes, its sensitivity to atmospheric disruptions, specific acquisition geometry, and the requisite for phase unwrapping collectively contribute to constraining its usage. Several processing strategies are developed in this thesis to capitalise on all the opportunities of GB-SAR systems, such as continuous, flexible and autonomous observation combined with high resolutions and accuracy. The first challenge that needs to be solved is to accurately localise and estimate the azimuth of the GB-SAR to improve the geocoding of the image in the subsequent step. A ray tracing algorithm and tomographic techniques are used to recover these external parameters of the sensors. The introduction of corner reflectors for validation purposes confirms a significant error reduction. However, for the subsequent geocoding, challenges persist in scenarios involving vertical structures due to foreshortening and layover, which notably compromise the geocoding quality of the observed points. These issues arise when multiple points at varying elevations are encapsulated within a singular resolution cell, posing difficulties in pinpointing the precise location of the scattering point responsible for signal return. To surmount these hurdles, a Bayesian approach grounded in intensity models is formulated, offering a tool to enhance the accuracy of the geocoding process. The validation is assessed on a dam in the black forest in Germany, characterised by a very specific structure. The second part of this thesis is focused on the feasibility of using GB-SAR systems for long-term geodetic monitoring of large structures. A first assessment is made by testing large temporal baselines between acquisitions for epoch-wise monitoring. Due to large displacements, the phase unwrapping can not recover all the information. An improvement is made by adapting the geometry of the signal processing with the principal component analysis. The main case study consists of several campaigns from different stations at Enguri Dam in Georgia. The consistency of the estimated displacement map is assessed by comparing it to a numerical model calibrated on the plumblines data. It exhibits a strong agreement between the two results and comforts the usage of GB-SAR for epoch-wise monitoring, as it can measure several thousand points on the dam. It also exhibits the possibility of detecting local anomalies in the numerical model. Finally, the instrument has been installed for continuous monitoring for over two years at Enguri Dam. An adequate flowchart is developed to eliminate the drift happening with classical interferometric algorithms to achieve the accuracy required for geodetic monitoring. The analysis of the obtained time series confirms a very plausible result with classical parametric models of dam deformations. Moreover, the results of this processing strategy are also confronted with the numerical model and demonstrate a high consistency. The final comforting result is the comparison of the GB-SAR time series with the output from four GNSS stations installed on the dam crest. The developed algorithms and methods increase the capabilities of the GB-SAR for dam monitoring in different configurations. It can be a valuable and precious supplement to other classical sensors for long-term geodetic observation purposes as well as short-term monitoring in cases of particular dam operations