Geophysical remote sensing of North Carolina’s historic cultural landscapes: studies at house in the Horseshoe State historic site

This dissertation is written in accordance with the three article option offered by the Geography Department at UNC Greensboro. It contains three manuscripts to be submitted for publication. The articles address specific research issues within the remote sensing process described by Jensen (2016) as they apply to subsurface geophysical remote sensing of historic cultural landscapes, using the buried architectural features of House in the Horseshoe State Historic Site in Moore County, North Carolina. The first article compares instrument detection capabilities by examining subsurface structure remnants as they appear in single band ground-penetrating radar (GPR), magnetic gradiometer, magnetic susceptibility and conductivity images, and also demonstrates how excavation strengthens geophysical image interpretation. The second article examines the ability of GPR to estimate volumetric soil moisture (VSM) in order to improve the timing of data collection, and also examines the visible effect of variable moisture conditions on the interpretation of a large historic pit feature, while including the relative soil moisture continuum concepts common to geography/geomorphology into a discussion of GPR survey hydrologic conditions. The third article examines the roles of scientific visualization and cartography in the production of knowledge and the presentation of maps using geophysical data to depict historic landscapes. This study explores visualization techniques pertaining to the private data exploration view of the expert, and to the simplified public facing view

Theoretical Developments in Electromagnetic Induction Geophysics with Selected Applications in the Near Surface

Near-surface applied electromagnetic geophysics is experiencing an explosive period of growth with many innovative techniques and applications presently emergent and others certain to be forthcoming. An attempt is made here to bring together and describe some of the most notable advances. This is a difficult task since papers describing electromagnetic induction methods are widely dispersed throughout the scientific literature. The traditional topics discussed herein include modeling, inversion, heterogeneity, anisotropy, target recognition, logging, and airborne electromagnetics (EM). Several new or emerging techniques are introduced including landmine detection, biogeophysics, interferometry, shallow-water electromagnetics, radiomagnetotellurics, and airborne unexploded ordnance (UXO) discrimination. Representative case histories that illustrate the range of exciting new geoscience that has been enabled by the developing techniques are presented from important application areas such as hydrogeology, contamination, UXO and landmines, soils and agriculture, archeology, and hazards and climat

Quantitative Integration of Multiple Near-Surface Geophysical Techniques for Improved Subsurface Imaging and Reducing Uncertainty in Discrete Anomaly Detection

Currently there is no systematic quantitative methodology in place for the integration of two or more coincident data sets collected using near-surface geophysical techniques. As the need for this type of methodology increases—particularly in the fields of archaeological prospecting, UXO detection, landmine detection, environmental site characterization/remediation monitoring, and forensics—a detailed and refined approach is necessary. The objective of this dissertation is to investigate quantitative techniques for integrating multi-tool near-surface geophysical data to improve subsurface imaging and reduce uncertainty in discrete anomaly detection. This objective is fulfilled by: (1) correlating multi-tool geophysical data with existing well-characterized “targets”; (2) developing methods for quantitatively merging different geophysical data sets; (3) implementing statistical tools within Statistical Analysis System (SAS) to evaluate the multiple integration methodologies; and (4) testing these new methods at several well-characterized sites with varied targets (i.e., case studies). Three geophysical techniques utilized in this research are: ground penetrating radar (GPR), electromagnetic (ground conductivity) methods (EM), and magnetic gradiometry. Computer simulations are developed to generate synthetic data with expected parameters such as heterogeneity of the subsurface, type of target, and spatial sampling. The synthetic data sets are integrated using the same methodologies employed on the case-study sites to (a) further develop the necessary quantitative assessment scheme, and (b) determine if these merged data sets do in fact yield improved results. A controlled setting within The University of Tennessee Geophysical Research Station permits the data (and associated anomalous bodies) to be spatially correlated with the locations of known targets. Error analysis is then conducted to guide any modifications to the data integration methodologies before transitioning to study sites of unknown subsurface features. Statistical analysis utilizing SAS is conducted to quantitatively evaluate the effectiveness of the data integration methodologies and determine if there are significant improvements in subsurface imaging, thus resulting in a reduction in the uncertainty of discrete anomaly detection

EVALUATING FROZEN SOIL PROPERTIES WITH ELECTRICAL RESISTIVITY MEASUREMENT AND ELECTRO-MAGNETIC INDUCTION METHODS

Electromagnetic induction was utilized in the past by the United States Army Corps of Engineers as a method of detecting unexploded ordinance, while it has the potential to act as a novel method of investigating frozen soils in cold regions. In this study, we performed lab-scale 1D electrical resistivity measurements under freeze-thaw circumstances on frost-susceptible soils with varied soil properties. We implemented an empirical model from our experiments into a COMSOL finite element model at both laboratory and field scales to simulate soil electrical resistivity response under both short-term and long-term sub-freezing conditions. Dynamic temperature-dependent soil properties, most notably unfrozen water content, exert significant influences on the observed electrical resistivity. We also characterized the evolution of electrical resistivity during the freeze-thaw cycle with empirical models. Laboratory and field experiments were made to validate the effectiveness of the iFrost Mapper device in detecting typical patterns of metal, liquid, and soil samples of different concentrations and temperatures. The original data were processed by considering both inphase and quadrature responses. Meanwhile, simulation studies with similar parameters to the laboratory tests, including geometry, material properties, and physical conditions, and the samples were made in COMSOL Multiphysics to compare the analytical solutions and experimental data

Application of Polarimetric GPR to detection of subsurface objects

Tohoku University佐藤源之課

Electromagnetic Imaging of the marine subsurface : a novel approach to assess sediment patterns and dynamics on clastic shelf systems

Electromagnetic (EM) imaging is a new approach to investigate marine near-surface sediments. The EM data provide information about electric conductivity and magnetic susceptibility of the sediments. Both are important physical parameters in exploration geophysics. Electric conductivity of marine sediments is a function of porosity, tortuosity and chemistry of the pore fluid. Magnetic susceptibility indicates the magnetic particle concentration and is hence related to the mineral composition of the sediment. In this thesis data processing, inversion and machine learning methods for a novel marine EM profiling system are developed, with the goal to explore the internal structure and spatial variability of sediment patterns in coastal and shelf regions. The investigated EM data were acquired on the NW Iberian shelf during the Meteor cruise M84/4b with the bottom towed electromagnetic profiler MARUM NERIDIS III. This non-conductive, non-magnetic fiberglass sled accommodates a controlled source electromagnetic system based on a frequency-domain concentric-loop EM induction sensor. In order to estimate quantitative seafloor sediment properties from the NERIDIS III EM data, the approach developed in this thesis follows three main steps: The first step is to calibrate the EM data such that instrument related bias is removed and the EM response is solely controlled by the frequency of the source signal, the system geometry, the electric conductivity and magnetic susceptibility of the seawater and the sediment. Calibration is necessary to make data from different measurements and surveys comparable and to enable solving of the ill-posed inverse problem for electric conductivity and magnetic susceptibility. This thesis shows that calibrating the primary EM field alone, by means of independently measured water conductivity and constant water susceptibility, is not sufficient. Therefore, a calibration methodology is developed which firstly calibrates the recorded EM data to compensate for bias in the primary EM field followed by a secondary EM field calibration by means of ground-truth data. The second step involves the inversion of the EM data, which can be subdivided into a half-space and 1-D inversion. The half-space inversion aims for the reconstruction of bulk sediment conductivity and susceptibility of the uppermost approximately 0.5 to 1 m. It is demonstrated that recovered half-space conductivity and susceptibility well reflect the main sediment patterns on the NW Iberian shelf and allow the reconstruction of sediment pathways. The 1-D inversion can be used to reconstruct the vertical conductivity structure of the subsurface. An algorithm is developed which employs the half-space susceptibility as a priori information and hence allows the utilisation of the in-phase component of the complex earth response increasing the depth of investigation. It is shown that vertical conductivity variations down to approximately 3 m below the seafloor can be reconstructed revealing the internal structure of the Galician Mud Belt. The third step covers the predictive modelling of grain-size from the electric conductivity and magnetic susceptibility of the sediment. Correlation analyses are carried out which reveal a strong relation between the electromagnetic and textural sediment properties. A radial basis function network is developed which predicts the entire grain-size distribution for each EM measurement location along shelf wide survey lines. The predicted grain-size distributions are used to identify the well-known sediment facies on the NW Iberian shelf and give new insights into their distribution and transitions

Context-dependent fusion with application to landmine detection.

Traditional machine learning and pattern recognition systems use a feature descriptor to describe the sensor data and a particular classifier (also called expert or learner ) to determine the true class of a given pattern. However, for complex detection and classification problems, involving data with large intra-class variations and noisy inputs, no single source of information can provide a satisfactory solution. As a result, combination of multiple classifiers is playing an increasing role in solving these complex pattern recognition problems, and has proven to be viable alternative to using a single classifier. In this thesis we introduce a new Context-Dependent Fusion (CDF) approach, We use this method to fuse multiple algorithms which use different types of features and different classification methods on multiple sensor data. The proposed approach is motivated by the observation that there is no single algorithm that can consistently outperform all other algorithms. In fact, the relative performance of different algorithms can vary significantly depending on several factions such as extracted features, and characteristics of the target class. The CDF method is a local approach that adapts the fusion method to different regions of the feature space. The goal is to take advantages of the strengths of few algorithms in different regions of the feature space without being affected by the weaknesses of the other algorithms and also avoiding the loss of potentially valuable information provided by few weak classifiers by considering their output as well. The proposed fusion has three main interacting components. The first component, called Context Extraction, partitions the composite feature space into groups of similar signatures, or contexts. Then, the second component assigns an aggregation weight to each detector\u27s decision in each context based on its relative performance within the context. The third component combines the multiple decisions, using the learned weights, to make a final decision. For Context Extraction component, a novel algorithm that performs clustering and feature discrimination is used to cluster the composite feature space and identify the relevant features for each cluster. For the fusion component, six different methods were proposed and investigated. The proposed approached were applied to the problem of landmine detection. Detection and removal of landmines is a serious problem affecting civilians and soldiers worldwide. Several detection algorithms on landmine have been proposed. Extensive testing of these methods has shown that the relative performance of different detectors can vary significantly depending on the mine type, geographical site, soil and weather conditions, and burial depth, etc. Therefore, multi-algorithm, and multi-sensor fusion is a critical component in land mine detection. Results on large and diverse real data collections show that the proposed method can identify meaningful and coherent clusters and that different expert algorithms can be identified for the different contexts. Our experiments have also indicated that the context-dependent fusion outperforms all individual detectors and several global fusion methods

New Global Perspectives on Archaeological Prospection

This volume is a product of the 13th International Conference on Archaeological Prospection 2019, which was hosted by the Department of Environmental Science in the Faculty of Science at the Institute of Technology Sligo. The conference is held every two years under the banner of the International Society for Archaeological Prospection and this was the first time that the conference was held in Ireland. New Global Perspectives on Archaeological Prospection draws together over 90 papers addressing archaeological prospection techniques, methodologies and case studies from 33 countries across Africa, Asia, Australasia, Europe and North America, reflecting current and global trends in archaeological prospection. At this particular ICAP meeting, specific consideration was given to the development and use of archaeological prospection in Ireland, archaeological feedback for the prospector, applications of prospection technology in the urban environment and the use of legacy data. Papers include novel research areas such as magnetometry near the equator, drone-mounted radar, microgravity assessment of tombs, marine electrical resistivity tomography, convolutional neural networks, data processing, automated interpretive workflows and modelling as well as recent improvements in remote sensing, multispectral imaging and visualisation

Advances in Monitoring Dynamic Hydrologic Conditions in the Vadose Zone through Automated High-Resolution Ground-Penetrating Radar Images and Analysis

This body of research focuses on resolving physical and hydrological heterogeneities in the subsurface with ground-penetrating radar (GPR). Essentially, there are two facets of this research centered on the goal of improving the collective understanding of unsaturated flow processes: i) modifications to commercially available equipment to optimize hydrologic value of the data and ii) the development of novel methods for data interpretation and analysis in a hydrologic context given the increased hydrologic value of the data. Regarding modifications to equipment, automation of GPR data collection substantially enhances our ability to measure changes in the hydrologic state of the subsurface at high spatial and temporal resolution (Chapter 1). Additionally, automated collection shows promise for quick high-resolution mapping of dangerous subsurface targets, like unexploded ordinance, that may have alternate signals depending on the hydrologic environment (Chapter 5). Regarding novel methods for data inversion, dispersive GPR data collected during infiltration can constrain important information about the local 1D distribution of water in waveguide layers (Chapters 2 and 3), however, more data is required for reliably analyzing complicated patterns produced by the wetting of the soil. In this regard, data collected in 2D and 3D geometries can further illustrate evidence of heterogeneous flow, while maintaining the content for resolving wave velocities and therefore, water content. This enables the use of algorithms like reflection tomography, which show the ability of the GPR data to independently resolve water content distribution in homogeneous soils (Chapter 5). In conclusion, automation enables the non-invasive study of highly dynamic hydrologic processes by providing the high resolution data required to interpret and resolve spatial and temporal wetting patterns associated with heterogeneous flow. By automating the data collection, it also allows for the novel application of established GPR data algorithms to new hydrogeophysical problems. This allows us to collect and invert GPR data in a way that has the potential to separate the geophysical data inversion from our ideas about the subsurface; a way to remove ancillary information, e.g. prior information or parameter constraints, from the geophysical inversion process

A generic framework for context-dependent fusion with application to landmine detection.

For complex detection and classification problems, involving data with large intra-class variations and noisy inputs, no single source of information can provide a satisfactory solution. As a result, combination of multiple classifiers is playing an increasing role in solving these complex pattern recognition problems, and has proven to be a viable alternative to using a single classifier. Over the past few years, a variety of schemes have been proposed for combining multiple classifiers. Most of these were global as they assign a degree of worthiness to each classifier, that is averaged over the entire training data. This may not be the optimal way to combine the different experts since the behavior of each one may not be uniform over the different regions of the feature space. To overcome this issue, few local methods have been proposed in the last few years. Local fusion methods aim to adapt the classifiers\u27 worthiness to different regions of the feature space. First, they partition the input samples. Then, they identify the best classifier for each partition and designate it as the expert for that partition. Unfortunately, current local methods are either computationally expensive and/or perform these two tasks independently of each other. However, feature space partition and algorithm selection are not independent and their optimization should be simultaneous. In this dissertation, we introduce a new local fusion approach, called Context Extraction for Local Fusion (CELF). CELF was designed to adapt the fusion to different regions of the feature space. It takes advantage of the strength of the different experts and overcome their limitations. First, we describe the baseline CELF algorithm. We formulate a novel objective function that combines context identification and multi-algorithm fusion criteria into a joint objective function. The context identification component thrives to partition the input feature space into different clusters (called contexts), while the fusion component thrives to learn the optimal fusion parameters within each cluster. Second, we propose several variations of CELF to deal with different applications scenario. In particular, we propose an extension that includes a feature discrimination component (CELF-FD). This version is advantageous when dealing with high dimensional feature spaces and/or when the number of features extracted by the individual algorithms varies significantly. CELF-CA is another extension of CELF that adds a regularization term to the objective function to introduce competition among the clusters and to find the optimal number of clusters in an unsupervised way. CELF-CA starts by partitioning the data into a large number of small clusters. As the algorithm progresses, adjacent clusters compete for data points, and clusters that lose the competition gradually become depleted and vanish. Third, we propose CELF-M that generalizes CELF to support multiple classes data sets. The baseline CELF and its extensions were formulated to use linear aggregation to combine the output of the different algorithms within each context. For some applications, this can be too restrictive and non-linear fusion may be needed. To address this potential drawback, we propose two other variations of CELF that use non-linear aggregation. The first one is based on Neural Networks (CELF-NN) and the second one is based on Fuzzy Integrals (CELF-FI). The latter one has the desirable property of assigning weights to subsets of classifiers to take into account the interaction between them. To test a new signature using CELF (or its variants), each algorithm would extract its set of features and assigns a confidence value. Then, the features are used to identify the best context, and the fusion parameters of this context are used to fuse the individual confidence values. For each variation of CELF, we formulate an objective function, derive the necessary conditions to optimize it, and construct an iterative algorithm. Then we use examples to illustrate the behavior of the algorithm, compare it to global fusion, and highlight its advantages. We apply our proposed fusion methods to the problem of landmine detection. We use data collected using Ground Penetration Radar (GPR) and Wideband Electro -Magnetic Induction (WEMI) sensors. We show that CELF (and its variants) can identify meaningful and coherent contexts (e.g. mines of same type, mines buried at the same site, etc.) and that different expert algorithms can be identified for the different contexts. In addition to the land mine detection application, we apply our approaches to semantic video indexing, image database categorization, and phoneme recognition. In all applications, we compare the performance of CELF with standard fusion methods, and show that our approach outperforms all these methods