Matteo Dossi

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 velocities 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.

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.

Automated reflection picking and inversion: Application to ground and airborne GPR surveys

We apply an automated picking and inversion algorithm to ground-based and airborne glaciological GPR surveys, in order to recover the internal stratigraphy, density distribution, and water content of alpine glaciers. Current glacier monitoring techniques encompass topographic mapping, direct measurements, and GPR surveys. However, the resulting models strongly depend on the assumptions made about the glaciers internal EM velocity and density distributions, which are usually set either constant or slow-varying, with the only constraints given by locally sampled values. Our inversion procedure uses amplitudes and timespace positions of the recorded reflections to recover the EM velocity and thickness of each layer by reconstructing the travel path of each reflected wavelet. The internal density distribution of glaciers is then recovered using well-known empirical formulas. The input reflections are automatically picked using an algorithm designed to detect and track any recorded event characterized by lateral phase continuity. Such a procedure is mostly independent of the interpreter and only requires a few input parameters and thresholds. High data densities lead to accurate and statistically sound models, while 4-D GPR surveys allow monitoring of the temporal variations of a glacier and the estimation of its mass balance.

Quantitative Analysis of GPR Signals: Transmitted Wavelet, Amplitude Decay, and Sampling-Related Amplitude Distortions

We study the importance of accurately recording signal amplitudes for the quantitative analysis of GPR data sets. Specifically, we measure the peak amplitudes of signals emitted by GPR antennas with different central frequencies and study their amplitude decay with distance, in order to extrapolate the peak amplitude of the wavelet initially transmitted by each antenna. The purpose is to compare the reference and reflected amplitudes in order to accurately estimate the subsurface EM impedance contrasts. Moreover, we study how sampling-related amplitude distortions can affect the quantitative analysis, and subsequently the resulting subsurface models, even in the absence of aliasing effects. The well-known Nyquist–Shannon theorem gives practical lower limits for the sampling rate in order to preserve the spectral content of a digitized signal; however, we show that it does not prevent possible amplitude distortions. In particular, we demonstrate that significant and unrecoverable loss of amplitude information occurs even at sampling rates well above the Nyquist–Shannon threshold. Interpolation may theoretically reduce such amplitude distortions; however, its accuracy would depend on the implemented algorithm and it is not verifiable in real data sets, since the actual amplitude information is limited to the sampled values. Moreover, re-sampling the interpolated signal simply reintroduces the initial problem, when a new sampling rate is selected. Our analysis suggests that, in order to limit the maximum peak amplitude error within 5%, the sampling rate selected during data acquisition must be at least 12 times the signal central frequency, which is higher than the commonly adopted standards.

Integrated attribute analysis for improved GPR data interpretation

We apply integrated attribute analysis to extract more reliable quantitative information from processed GPR data, and obtain a better characterization of subsurface structures. A multi-attribute approach is used to characterize the subsurface through different attribute categories, including instantaneous, coherency, and textural attributes applied to quantities related not only to amplitude, phase, and frequency, but also to other parameters calculated from the original data. The different attributes can be integrated into a single view with composite displays (overlays and mixed displays). The proposed procedure is tested in different environments namely: archaeological areas to characterize cultural heritage buried in highly heterogeneous subsurface environments, and glaciers to image their inner structure and to monitor the seasonal changes of firn layers. The results from two case studies demonstrate that the proposed integrated attribute analysis can highlight zones characterized by different electromagnetic parameters, better visualize and quantify GPR features in an automatic and objective manner, and enhance GPR data interpretation in different application fields.