4-D quantitative GPR analyses to study the summer mass balance of a glacier: A case history

In order to assess the seasonal changes of the topography, the inner structure and the physical properties of a small glacier in the Eastern Alps, we performed a 4-D multi frequency GPR survey by repeating the same data acquisition in four different periods of the year 2013. The usual glacier mass balance estimation encompasses only topographic variations, but the real evolution is much more complex and includes surface melting and refreezing, snow metamorphism, and basal melting. We analyzed changes in both the imaged geometrical-morphological structures and the densities, estimated from GPR data inversion. The inversion algorithm uses reflection amplitudes and traveltimes to extract the electromagnetic velocity in the interpreted layers and the densities of the frozen materials through empirical relations. The obtained results have been compared and validated with direct measures like snow thickness surveys, density logs within snow pits and ablation stakes. This study demonstrates that GPR techniques are a fast and effective tool not only for glacial qualitative studies, but also for detailed glacier monitoring and accurate quantitative analyses of crucial glaciological parameters like density distribution and water runoff

Automated Reflection Picking and Inversion Applied to Glaciological GPR Surveys

The purpose of this thesis is to present an automated picking and inversion procedure, which is designed to accurately and objectively identify the main reflections within Ground Penetrating Radar (GPR) data sets; to characterize them in terms of their arrival times, peak amplitudes, and polarities; and to recover from these and other quantities the internal stratigraphy and EM properties of the subsurface. In this text the main features and formulas of the developed algorithms are presented, while also highlighting both the advantages and limitations of the proposed auto-picking and inversion procedure with respect to other commonly used methods. In particular, the algorithms are tested on a synthetic GPR profile and their performance is assessed by comparing the inversion results with the initial model. The main uncertainty factors of the procedure are also analyzed, with a particular focus on sampling-related signal distortions, leading to the definition of a recommended minimum threshold for the sampling rate selected during data acquisition. The procedure is also applied to a glaciological 3-D GPR data set, in order to study the internal stratigraphy, density distribution, total volume, and water content of an alpine glacier

Auto-picking and phase assessment by means of attribute analysis applied to GPR pavement inspection

We propose an automated procedure, based on attribute analysis, to quickly and objectively detect and characterize reflections along GPR profiles. The process uses the cosine of the instantaneous phase to mark as a horizon any event that shows lateral phase continuity, defining its time-space positions and peak amplitudes, to be used in interpretation and inversion processes. Such attribute allows to efficiently track even the weakest events, as well as those showing large lateral amplitude variations. Furthermore, the algorithm is able to extract the polarity of each reflection, by identifying their actual initial phase. This analysis is done by assessing the behavior of the cosine phase in the vicinity of each picked horizon, searching for other sub-parallel horizons that can be grouped into the same event. The proposed procedure is mostly independent from the interpreter, except for a few required thresholds. Moreover, since it uses only the cosine phase, it can be applied to data sets after basic processing without the need of any amplitude recovery, which introduces a certain degree of subjectivity on the results and prevents further quantitative analyses and data inversion. In this paper we validate the method and discuss its accuracy, as well as its limitations and possible applications. The algorithm successfully tracked events with lateral phase continuity in the tests performed on GPR data acquired on an airport runway

The search for brines: GPR markers, proxies, and challenges

GPR is one of the most applicable geophysical techniques to detect brines at shallow depths due to their peculiar electromagnetic characteristics, in particular related to overall high electrical conductivity. However, the range of chemical and physical parameters of brines is very wide and can change with time, thus making their imaging and characterization sometimes challenging. We analyze different GPR datasets collected in continental Antarctica, focusing on amplitude and spectral behavior, also calculating several GPR attributes to detect shallow brine ponds. We show that the geophysical signature of the brines is quite elusive, being sometimes characterized by clear strong reflectors, by extremely high attenuation of the GPR signal with a remarkable spectral shift, or by high scattering. Results are validated by ice coring, direct sampling, and by means of physical and chemical analyses of the brines. We demonstrate the existence of a relevant temporal and spatial variability of the brines, which requires integrated data analyses to achieve a robust GPR data interpretation. The integrated study of amplitude, frequency, and phase behavior is described in detail for different case studies, always trying to get general outcomes and site-independent interpretation criteria

Automated diffraction tracking and inversion for EM velocity estimation

We apply an automated diffraction tracking and inversion algorithm to oversampled GPR data sets, in order to assess the influence of time and space sampling on the resulting EM velocity model. The accuracy of such model depends on the actual presence and regular distribution of undistorted diffraction hyperbolas within the recorded CO profiles. Nevertheless, hyperbolic inversion techniques on CO data are the only alternative to amplitude inversion methods in absence of clearly defined reflections. Commonly used diffraction tracking methods include manual picking, automated hyperbola fitting, edge detection, and machine learning techniques. The presented auto-picking algorithm tracks potential diffractions by transforming the coordinates of the profile so that the hyperbolas are turned into straight lines. Through a linear fit in the transformed space, the algorithm is then able to recover the average EM velocity above the tracked diffractors. We apply the procedure on GPR data sets acquired on urban environments, observing a significant impact of both temporal and spatial sampling intervals on the resulting average EM velocities and uncertainty values

Automated reflection picking and polarity assessment through attribute analysis: Theory and application to synthetic and real ground-penetrating radar data

We have developed a procedure to automatically detect, pick, and characterize reflections in either seismic or ground-penetrating radar (GPR) data sets. Accurate picking results are essential in many research and application fields and are mandatory to recover the underground stratigraphy and reflectivity and, therefore, to create a more constrained subsurface model. We use the cosine of the instantaneous phase to track events with lateral phase continuity and record them in terms of time-space positions, amplitude, and polarity. Moreover, we use the cosine phase to reconstruct the shape of the reflected wavelets by averaging it along the picked horizons, to recover the initial phase of each reflection and, therefore, their polarities. By comparing the polarities of the transmitted and reflected wavelets, we can recover the impedance contrasts in the subsurface and evaluate the properties of the materials. We applied the picking and polarity assessment algorithms to a synthetic data set. We inverted the picked amplitudes and traveltimes to test the accuracy of the procedure by comparing the calculated stratigraphy and velocity distribution with the initial model. The inverted results were consistent with the original data, with most discordances in the stratigraphy within a few tens of centimeters, using a wavelet with a central frequency equal to 300 MHz. Local larger discrepancies were probably caused by interferences altering the amplitudes and resulting in the overestimation of the impedance contrasts. We also applied automatic picking and phase assessment procedures to real glaciological and archaeological GPR surveys, in which the influence of noise and diffractions was critically evaluated by comparing the picked results obtained from raw and processed data

A new methodology to estimate the EM velocity from Common Offset GPR: Theory and application on synthetic and real data

We implemented and validated a new method to estimate the velocity distribution from CO GPR data by using the reflection amplitudes and traveltimes picked on the interpreted interfaces in a GPR profiles. Since the method assumes the picked amplitudes to be related only to the reflection coefficients, an accurate data processing is essential before any amplitude picking, the most important steps being the amplitude recovery and the removal of scattering effects. The method also requires as input: 1) the value of the offset, 2) the velocity of the EM wave in the shallow layer, 3) the peak amplitude of the wavelet incident at the first interface. The error associated to the velocities in the first layer has the major influence on the uncertainties of the final results. Nevertheless, this error can be reduced by combining different independent measurements, like CMP gathers, TDR measurements, or dedicated trans-illumination experiments. The assumption of 1-D model in the proximity of each trace position, as well as the small spread approximation used in the algorithm, are acceptable for most of the real GPR applications. In fact, given the small offsets normally used for CO GPR surveys, the incident angles on the various interfaces are small even for shallow targets. The method assumes the subsurface material as lossless and non-dispersive. The latter assumption is satisfied for most practical applications, but most geological media are characterized by high intrinsic attenuation. In such conditions, the procedure could be still valid if applied on data properly corrected for dissipation effects. Further research must address this topic for a better understanding not only of the kinematic, but also of the dynamic behavior of the EM waves in real media at practical field conditions

A new fast methodology to estimate the density of frozen materials by means of common offset GPR data

We propose a methodology to estimate the density of frozen media (snow, firn and ice) using common offset (CO) GPR data. The technique is based on reflection amplitude analysis to calculate the series of reflection coef- \u2423cients used to estimate the dielectric permittivity of each layer. We determine the vertical density variations for all the GPR traces by applying an empirical equation. We are thus able to infer the nature of frozen materials, from fresh snow to firn and ice. The proposed technique is critically evaluated and validated on synthetic data and further tested on real data of the Glacier of Mt. Canin (South-Eastern Alps). Despite the simplifying hypotheses and the necessary approximations, the average values of density for different levels are calculated with acceptable accuracy. The resulting large-scale density data are fundamental to estimate the water equivalent (WE), which is an essential parameter to determine the actual water mass within a certain frozen volume. Moreover, this analysis can help to find and locate debris or moraines embedded within the ice bodies

Automated phase attribute-based picking applied to reflection seismics

We have applied an attribute-based autopicking algorithm to reflection seismics with the aim of reducing the influence of the user\u2019s subjectivity on the picking results and making the interpretation faster with respect to manual and semiautomated techniques. Our picking procedure uses the cosine of the instantaneous phase to automatically detect and mark as a horizon any recorded event characterized by lateral phase continuity. A patching procedure, which exploits horizon parallelism, can be used to connect consecutive horizons marking the same event but separated by noise-related gaps. The picking process marks all coherent events regardless of their reflection strength; therefore, a large number of independent horizons can be constructed. To facilitate interpretation, horizons marking different phases of the same reflection can be automatically grouped together and specific horizons from each reflection can be selected using different possible methods. In the phase method, the algorithm reconstructs the reflected wavelets by averaging the cosine of the instantaneous phase along each horizon. The resulting wavelets are then locally analyzed and confronted through crosscorrelation, allowing the recognition and selection of specific reflection phases. In case the reflected wavelets cannot be recovered due to shape-altering processing or a low signal-to-noise ratio, the energy method uses the reflection strength to group together subparallel horizons within the same energy package and to select those satisfying either energy or arrival time criteria. These methods can be applied automatically to all the picked horizons or to horizons individually selected by the interpreter for specific analysis. We show examples of application to 2D reflection seismic data sets in complex geologic and stratigraphic conditions, critically reviewing the performance of the whole process

Velocity analysis on common offset GPR data: A deep learning approach

We implemented a Deep Learning algorithm to estimate the subsurface EM velocity field from common offset GPR profiles. The Deep Learning approach is based on a Bi-Directional Long Short-Term Memory (LSTM) Neural Network (NN) architecture trained on simple synthetic profiles randomly generated. The trained network is then applied to each A-Scan of 2D or even 3D GPR datasets. We trained the network on a synthetic dataset with different numbers of reflectors, wavelets, Signal-to-Noise ratios. The application of the network to synthetic and field data successfully predicts the velocity model and provides a computationally effective alternative to classic methods