The aim of the study was to assess the possibilities in using ground-penetrating radar to identify small-scale stratigraphy in the upper 1 m of a soil profile and to statistically identify differences in the stratigraphic units discovered using the GPR unit. The study area is ca 15 km southwest of Lycksele near a gravel pit on the banks of Örån. It was located on top of a large (>5 m thick) glacifluvial deposit of indeterminate age overlayed by till from the latest deglaciation. The data sampled included 22 radargrams depicting a total length of >1000 m as well as soil samples from three stratigraphic units from three different trenches (9 samples in total). Visual analysis of the stratigraphy took place via trenches as well as by studying the radar images. The radar images show that three stratigraphic units can be identified clearly (ablation till, S1, a transitional layer of mixed till and glacifluvium, S2, and the underlying glacifluvium, S3) but that the border between the two lower units can be opaque at times. Field observations showed this to be due to the genesis of the topmost unit, the ablation till. Observations in the field also showed relict podsolization in a kettle in the northern part of the study area. Grain-size comparison of the three stratigraphic units identified was performed via sieving. Calculations of the weight percentage were then used for statistical analysis to identify any differences between the strata. Results show that there are differences regarding fine material (ø <0.074 mm, p=0.038), gravel (ø > 2 mm, p<0.0001) and sand (p=0.027) within these three stratigraphic units.

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.

Scărişoara Ice Cave has been a catalyst of scientific intrigue and effort for over 150 years. These efforts have revealed and described countless natural phenomena – and in the process have made it one of the most studied caves in the world. Of especial interest is the massive ice block located within its Great Hall and scientific reservations. The ice block, which is the oldest and largest known to exist in a cave, has been the focus of multiple surveying and mapping efforts, typically ones utilizing traditional equipment. In this study, the goals were to reconstruct the ice block/cave floor interface and to estimate the volume of the ice block. Once the models were constructed, we aimed to study the relationships between the cave and ice block morphologies. In order to accomplish this goal, three (3) main datasets were collected, processed, and amalgamated. Ground penetrating radar data was used to discern the floor morphology below the ice block. Over 1,500 photographs were collected in the cave and used with Structure from Motion photogrammetry software to construct a texturized 3D model of the cave and ice surfaces. And a total station survey was performed to scale, georeference, and validate each model. Once georeferenced, the data was imported into an ArcGIS geodatabase for further analysis. The methodology described within this study provides a powerful set of instructions for producing highly valuable scientific data, especially related to caves. Here, we describe in detail the novel tools and software used to validate, inspect, manipulate, and measure morphological information while immersed in a fully 3D experience. With this methodology, it is possible to easily and inexpensively create digital elevation models of underground rooms and galleries, to measure the differences between surfaces, to create 3D models from the combination of surfaces, and to intimately inspect a subject area without actually being there. At the culmination of these efforts, the partial ice block volume was estimated to be 118,000 m3 with an uncertainty of ± 9.5%. The volume computed herein is significantly larger than previously thought and the total volume is likely significantly larger, since certain portions were not modeled during this study. In addition, the morphology of ceiling enlargement was linked to areas of high elevation at the base of the ice block. A counterintuitive depression was recognized at the base of the Entrance Shaft. The thickest areas of the ice were identified for future coring projects. And combining all this a new informational allowed us to propose a new theory on the formation of the ice block and to decipher particular speleogenetic aspects.

This study presents a comprehensive overview of the current state of the Milne Ice Shelf and how it has changed over the last 59 years. The 205 ±1 km2 ice shelf experienced a 28% (82 ±0.8 km2) reduction in area between 1950 – 2009, and a 20% (2.5 ±0.9km3 water equivalent (w.e.)) reduction in volume between 1981 – 2008/2009, suggesting a long-term state of negative mass balance. Comparison of mean annual specific mass balances (up to -0.34 m w.e. yr-1) with surface mass balance measurements for the nearby Ward Hunt Ice Shelf suggest that basal melt is a key contributor to total ice shelf thinning. The development and expansion of new and existing surface cracks, as well as ice-marginal and epishelf lake development, indicate significant ice shelf weakening. Over the next few decades it is likely that the Milne Ice Shelf will continue to deteriorate.

This study presents the first comprehensive assessment of the Petersen Ice Shelf and the Petersen Bay epishelf lake, and examines their current characteristics and changes to their structure between 1959 and 2012. The surface of the Petersen Ice Shelf is characterized by a rolling topography of ridges and troughs, which is balanced by a rolling basal topography, with thicker ice under the surface ridges and thinner ice under the surface troughs. Based on thickness measurements collected in 2011 and area measurements from August 2012, the Petersen Ice Shelf has a surface area of 19.32 km2 and a mean thickness of 29 m, with the greatest thicknesses (>100 m) occurring at the fronts of tributary glaciers feeding into the ice shelf. The tributary glaciers along the northern coast of Petersen Bay contributed an estimated area-averaged 7.89 to 13.55 cm yr-1 of ice to the ice shelf between 2011 and 2012. This input is counteracted by a mean surface ablation of 1.30 m yr-1 between 2011 and 2012, suggesting strongly negative current mass balance conditions on the ice shelf. The Petersen Ice Shelf remained relatively stable until 2005 when the first break-up in recent history occurred, removing >8 km2 of ice shelf surface area. This break-up led to the drainage of the epishelf lake once the ice shelf separated from the southern coast, providing a conduit through which the freshwater from the lake escaped. More break-ups occurred in summers 2008, 2011 and 2012, which resulted in a >31.2 km2 loss in surface area (~63% of June 2005 area). While ephemeral regions of freshwater have occurred along the southern coast of Petersen Bay since 2005 (with areas ranging from 0.32-0.53 km2), open water events and a channel along the southern coast have prevented the epishelf lake from reforming. Based on these past and present observations it is unlikely that Petersen Ice Shelf will continue to persist long into the future.

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.

Acquiring GPR data at multiple frequencies is useful because higher-frequency profiles have better spatial resolution, although they suffer from reduced depth penetration. Lower-frequencies can generally resolve to greater depths, but at the cost of spatial resolution. For concise presentation of GPR data, it would be useful to combine the best features of each profile into a composite radargram. This study explores effective ways to present GPR data acquired at multiple frequencies. An example is shown from a survey of hurricane overwash deposits from Santa Rosa Island, Florida. The methodology used to create a composite radargram is dependent on which of two goals the composite radargram is designed to achieve. These goals are broadening the spectral bandwidth of GPR data to increase the effectiveness of deconvolution and enhancing the resolution and depth of GPR data by plotting high-frequency data at early two-way travel times, low-frequency data at late two-way travel times, and using filters to smoothly transition from high-frequency to lower-frequency data. The steps towards creating a composite radargram include: 1) applying standard processing to nominal frequency data sets, 2) creating spatially coincident data sets, 3) equalizing the amplitude spectra among each nominal frequency data set, and 4) summing nominal frequency data sets together. Spectral bandwidth broadening is achieved by applying optical spectral whitening and summing nominal frequency data sets using a single ramped. Deconvolving this composite radargram did not show the same success observed by Booth et al. (2009). Enhancing the resolution and depth of GPR data can be achieved by applying amplitude envelope equalization (AEE) and summation using double ramped filters. AEE calculates the coefficients required to make equivalent average amplitude envelopes for GPR data that has been gained with automatic gain control . Double ramped filters suppress low-frequency energy for two-way travel times when a higher-frequency data set has adequate signal strength and higher frequency energy for two-way travel times when higher- frequency energy exhibits significant attenuation. A composite radargram built with AEE and double ramped filters achieves the goal enhancing resolution and depth of GPR data. Shallow reflections are interpreted as dune and hurricane overwash stratigraphy.

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.

Runion is a protohistoric Native American village located on the floodplain of the Nolichucky River in western Washington County. Previous archaeological excavations and radiocarbon dates suggest that the village was occupied during the mid-16th to mid-17th century. The Nolichucky River, in contrast, has been flowing through the area for millennia. Geophysical surveys are used to image the subsurface non-invasively, without disturbing protected land and/or organisms. Preliminary geophysical data collected at Runion include ground penetrating radar (GPR), electromagnetic induction (EMI), and magnetometry. These data show a linear feature surrounding the protohistoric village. Given its placement around the margins of the village, the feature could be interpreted as a fortification ditch, which is often paired with a palisade wall to defend a village from attack. The feature is also consistent with typical meandering floodplain stratigraphy, where sections of channel are often abandoned to form oxbow lakes. Over time these abandoned channels fill in and are called paleochannels. Each geophysical method measures the properties and characteristics of the linear feature, a presumed paleochannel. GPR sends electromagnetic radar waves into the ground, which reflect off different subsurface layers and are recorded as radargrams. Magnetometry measures subtle changes in earth magnetism, including the magnetization of rocks, soils, and/or ferrous objects. EMI systems transmit low frequency electromagnetic waves to measure both electrical conductivity (EC) and magnetic susceptibility (MS). Each of these instruments are used to collect data in transects and then processed to produce profiles, maps and, in the case of GPR, three-dimensional datasets of the subsurface. It is anticipated that GPR will reveal details about the stratigraphy of the linear feature. Magnetic, EC, and MS measurements will further help to interpret the GPR data by distinguishing between different types of sediments. These data may show if the feature is a paleochannel or a ditch excavated into older stratigraphic layers by village inhabitants for fortification. Ultimately, the feature will be tested with soil cores to study the sediments directly. At this preliminary stage the feature is interpreted to be a paleochannel. The stratigraphic layers revealed by GPR show a broad depression with stratigraphic layers characteristic of a paleochannel. In addition, magnetic readings are anomalously low on the eastern margin (closer to the modern river channel) and high on the western margin. This could indicate paired point bar sands and paleochannel fill, respectively. This interpretation is still tentative, however, because we have not yet integrated the EMI data, extracted soil cores, or dated the feature. Radiocarbon dates might help determine the relative age of the feature if organic carbon is present. In conclusion, preliminary data currently suggests that the structure is geological rather than archaeological. In the coming months we will collect more GPR data with different frequency GPR antennas, integrate the EMI data, and test the findings by extracting soil cores and reconstructing the stratigraphy.