In a periglacial environment it is important to know the thickness, orientation and structureof sediments when assessing the landscape and its hydrological pathways. Using a groundpenetratingradar (GPR) I have profiled large areas of the subsurface in a catchment area to alake on western Greenland. Post-processing and calculations of the gathered data has revealedthat the sediment thickness is maximum 15 meters in the valleys. Due to the fact that nocorrelation data is available, such as boreholes or pits, this estimation has large error limits butthe profiles gathered reveals the structure in the subsurface to a great extent.

Master of Science / Department of Civil Engineering / Eric J. Fitzsimmons / The rail industry's recent shift towards larger and heavier railcars has influenced Class III / short line railroad operation and track maintenance costs. Class III railroads earn less than $38.1 million in in annual revenue and generally operate first and last leg shipping for their customers. In Kansas, Class III railroads operate approximately 40 percent of the roughly 2,800 miles (4,500 km) of rail; however, due to the current Class III track condition they move lighter railcars at lower speeds than Class I railroads. The State of Kansas statutorily allots $5 million to support rail improvement projects, primarily for Class III railroads. Therefore, the objective of this study was to conduct an inventory of Kansas’s Class III rail network to identify the track segments in need of this support that would be most beneficial to the rail system. Representatives of each railroad were contacted and received a survey requesting information regarding the operational and structural status of their systems. The data collected were organized and processed to determine the sections of track that can accommodate the heavier axle load cars that are currently being utilized by Class I railroads. This study identified that Class III railroads shipped over 155,000 carloads of freight in 2016 and 30 percent of Kansas’s Class III track can currently accommodate heavy axle cars. The increased load from the increased railcar size has also increased the risk of damage to railroad’s track structure. Railroad ballast is the free draining granular material that supports the track structure. As the track ages, small particles can fill the voids of the granular material which is a process known as fouling. Established methods for determining the fouling of a section of ballast are destructive tests that usually require the railroad to restrict or reroute traffic on its network. Ground Penetrating Radar (GPR) is a nondestructive geophysical surveying method that measures the time required for electromagnetic wave impulses to reflect off differing subsurface interfaces. Historically, GPR surveys of track structures primarily determine the depth of ballast and track geometry. The objective of this study was to determine the viability of utilizing the laboratory’s existing GPR equipment to develop a methodology of measuring ballast fouling nondestructively. A 48 x 48 x 48 in (1.2 x 1.2 x 1.2 m) test box was built. The test box was filled with 48 in (1.2 m) of clean and ballast. Tests were run on dry and partially saturated material, wetted using 6 gallons (22.7 L). GPR data were collected hourly for the first 6 hours, then at the multiples of 12 and 24 hour marks for one week. Sand was chosen as an absorbent geologic material for the second stage of testing since no fouled ballast could be acquired at the time of the study. A 27 x18 x 18 in (0.69 x 0.46 x 0.046 m) box was filled with sand and wetted with water in one gallon (7.5 L) increments. GPR scans and samples to determine the water content were collected after the addition of each gallon. The data collected were processed to determine soil properties. Preliminary results from this research indicate that the GPR set up utilized can effectively determine the dielectric constant of geologic materials including ballast, although the dielectric constant is highly dependent on the volumetric moisture content of the material.

Tato práce se zabývá úlohou odhadování prostorové funkce z hlediska regrese pomocí Gaussovských procesů (GPR) za současné nejistoty tréninkových pozic (pozic senzorů). Nejdříve je zde popsána teorie v pozadí GPR metody pracující se známými tréninkovými pozicemi. Tato teorie je poté aplikována při odvození výrazů prediktivní distribuce GPR v testovací pozici při uvážení nejistoty tréninkových pozic. Kvůli absenci analytického řešení těchto výrazů byly výrazy aproximovány pomocí metody Monte Carlo. U odvozené metody bylo demonstrováno zlepšení kvality odhadu prostorové funkce oproti standardnímu použití GPR metody a také oproti zjednodušenému řešení uvedenému v literatuře. Dále se práce zabývá možností použití metody GPR s nejistými tréninkovými pozicemi v~kombinaci s výrazy s dostupným analytickým řešením. Ukazuje se, že k dosažení těchto výrazů je třeba zavést značné předpoklady, což má od počátku za následek nepřesnost prediktivní distribuce. Také se ukazuje, že výsledná metoda používá standardní výrazy GPR v~kombinaci s upravenou kovarianční funkcí. Simulace dokazují, že tato metoda produkuje velmi podobné odhady jako základní GPR metoda uvažující známé tréninkové pozice. Na druhou stranu prediktivní variance (nejistota odhadu) je u této metody zvýšena, což je žádaný efekt uvážení nejistoty tréninkových pozic.

Maintenance of aging buried infrastructure and reinforced concrete are critical issues in the United States. Inexpensive non-destructive techniques for mapping and imaging infrastructure and defects are an integral component of maintenance. Ground penetrating radar (GPR) is a widely-used non-destructive tool for locating buried infrastructure and for imaging rebar and other features of interest to civil engineers. Conventional acquisition and interpretation of GPR profiles is based on the arrival times of strong reflected/diffracted returns, and qualitative interpretation of return amplitudes. Features are thereby generally well located, but their material properties are only qualitatively assessed. For example, in the typical imaging of buried pipes, the average radar wave velocity through the overlying soil is estimated, but the properties of the pipe itself are not quantitatively resolved. For pipes on the order of the radar wavelength (<5-35 cm), pipe dimensions and infilling material remain ambiguous. Full waveform inversion (FWI) methods exploit the entire radar return rather than the time and peak amplitude. FWI can generate better quantitative estimates of subsurface properties. In recent decades FWI methods, developed for seismic oil exploration, have been adapted and advanced for GPR with encouraging results. To date, however, FWI methods for GPR data have not been specifically tuned and applied on surface collected common offset GPR data, which are the most common type of GPR data for engineering applications. I present an effective FWI method specifically tailored for common-offset GPR data. This method is composed of three main components, the forward modeling, wavelet estimation and inversion tools. For the forward modeling and iterative data inversion I use two open-source software packages, gprMax and PEST. The source wavelet, which is the most challenging component that guarantees the success of the method, is estimated with a novel Sparse Blind Deconvolution (SBD) algorithm that I have developed. The present dissertation indicates that with FWI, GPR can yield better quantitative estimates, for example, of both the diameters of small pipes and rebar and their electromagnetic properties (permittivity, conductivity). Also better estimates of electrical properties of the surrounding media (i.e. soil or concrete) are achieved with FWI.

Ground Penetrating Radar is a tool for mapping the subsurface in a noninvasive way. The radar instrument transmits electromagnetic waves and records the resulting scattered field. Unfortunately, the data from a survey can be hard to interpret, and this holds extra true for non-experts in the field. The data are also usually in 2.5D, or pseudo 3D, meaning that the vast majority of the scanned volume is missing data. Interpolation algorithms can, however, approximate the missing data, and the result can be visualized in an application and in this way ease the interpretation. This report has focused on comparing different interpolation algorithms, with extra focus on behaviour when the data get sparse. The compared methods were: Linear, inverse distance weighting, ordinary kriging, thin plate splines and fk domain zone-pass POCS. They were all found to have some strengths and weaknesses in different aspects, although ordinary kriging was found to be the most accurate and created the least artefacts. Inverse distance weighting performed surprisingly well considering its simplicity and low computational cost. A web-based, easy-to-use visualization application was developed in order to view the results from the interpolations. Some of the tools implemented include time slice, crop of a 3D cube, and iso surface.

The enrichment of ore produces large amounts of sulfur and metal-rich residual waste called tailings, which need to be deposited and stored for a long time. When the tailing is oxidized, large amounts of protons and metals are dissolved and diffuse to the groundwater. This poses a major environmental threat to biological life forms in the downstream ecosystem (Karltorp, 2008). In this study, leachate plumes and geological structures surrounding the tailings impoundment at the Kringelgruvan mine in northern Sweden have been successfully mapped using geophysical methods. Three methods have been used in parallel, slingram, ground penetrating radar (GPR) and electrical resistivity measurements, known as continuous vertical electrical sounding (CVES). The resulting data from GPR and CVES have been co-analyzed using Matlab. Algorithms have been produced that plots underground structures from CVES and compares them with interpreted structures from GPR. Studies have shown that the GPR is more sensitive than CVES to local variations of substructures when used in shallow soil cover, while CVES gives considerably more information regarding localization of the leachates and other electrically conductive materials, such as ore. Slingram EM31 has been shown to be the most time-efficient method to localize groundwater flow.

Ground Penetrating Radar (GPR) is an effective tool to measure the geological properties. A lot of information can be interpreted from the GPR data, such as soil water content. One of the common approaches is to determine the apparent electrical permittivity from the transmission velocity of the impulse electromagnetic wave, and to use empirical relationships to estimate the soil water content. For example, Ferre equation &amp; Topp equation are all expressing the relationship between soil water content and electrical permittivity. However, this method has some limitations; most notably the necessity to determine the velocity from a known depth to a reflecting surface. Therefore, another approach using the frequency dependent attenuation represented by a parameter called Q* was tested and studied in this thesis. The Q* method was evaluated using laboratory measurements, which consists of a series of experiments. A new empirical model was established using experiments where Q* was estimated from measurements on a soil sample with known water contents using two types of antennas (1.6 GHz &amp; 2.3 GHz). Finally, the adaptability of Topp equation and Ferre equation were verified, and a new empirical equation was defined. What’s more, the other method using Q* was proved to be feasible.

Ground penetrating radar (GPR) is limited by depth penetration and signal-to-noise ratio (SNR), impacting the ability to resolve subsurface features. Stacking, a process of averaging multiple scans in the same location, improves SNR. Digital antennas are capable of stacking at much higher rates than analog antennas. Four sites were examined using a GSSI SIR-4000 GPR unit with a 400 MHz analog antenna and a 350 MHz digital “hyperstacking” (350 HS) antenna. Sites represent various soil conditions, with known features. Data were compared qualitatively and quantitatively for differences in antenna outputs. Visual inspection of radargrams indicate a reduction in noise in the 350 HS data compared to the 400 MHz data. Quantitative assessments identified significant differences in standard deviation of radar reflection amplitude occurring at depth with both antennas and a reduction in noise and marginal increases in depth of penetration in low-loss conditions with the 350 MHz HS antenna.

Carter Mansion is a well-known historic site in Tennessee. It is estimated to have been built in the 1770s and is believed to be the oldest frame house, a house with a wooden skeleton for the base, in the state. This house was built by John and Landon Carter, father, and son respectively, who were well known influential leaders of the Watauga Settlement in the late 18th century. Prior to their arrival, the area was home to Native Americans. The aim of this research was to perform a geophysical survey of a previously unresearched area of the site, providing guidance for future development of the property. A ground penetrating radar (GPR) survey was conducted with a GSSI SIR4000 with 400 MHz antennas. Previous research was able to identify both historic and prehistoric graves in other areas of the property. This project extends this knowledge and aims to determine if there are graves associated with the headstones of the Carters on the eastern margin of the property, thus enriching the history and prehistory of the site without disturbing the grounds.

Explosive hazards, above and below ground, are a serious threat to civilians and soldiers. In an attempt to mitigate these threats, different forms of explosive hazard detection (EHD) exist; e.g, multi-sensor hand-held platforms, downward looking and forward looking vehicle mounted platforms, etc. Robust detection of these threats resides in the processing and fusion of different data from multiple sensing modalities, e.g., radar, infrared, electromagnetic induction (EMI), etc. The focus of this thesis is on the implementation of two new algorithms to form a new energy-based prescreener in hand-held ground penetrating radar (GPR). First, B-scan signal data is curvelet filtered using either Reverse- Reconstruction followed by Enhancement (RRE) or selectivity with respect to wedge information in the Curvelet transform, Wedge Selection (WS). Next, the result of a bank of matched filter are aggregated and run a size contrast filter with Bhattacharyya distance. Alarms are then combined using weighted mean shift clustering. Results are demonstrated in the context of receiver operating characteristics (ROC) curve performance on data from a U.S. Army test site that contains multiple target and clutter types, burial depths, and times of the day.