Helicopter-borne ground-penetrating radar investigations on temperate alpine glaciers: A comparison of different systems and their abilities for bedrock mapping

Geophysics ◽  
2016 ◽  
Vol 81 (1) ◽  
pp. WA119-WA129 ◽  
Author(s):  
Anja Rutishauser ◽  
Hansruedi Maurer ◽  
Andreas Bauder
Keyword(s):  
Ground Penetrating Radar ◽  
Low Frequency ◽  
Large Data ◽  
Internal Reflection ◽  
Swiss Alps ◽  
Reflection Band ◽  
Data Set ◽  
Investigation Area ◽  
Attractive Option ◽  
Ground Penetrating

On the basis of a large data set, comprising approximately 1200 km of profile lines acquired with different helicopter-borne ground-penetrating radar (GPR) systems over temperate glaciers in the western Swiss Alps, we have analyzed the possibilities and limitations of using helicopter-borne GPR surveying to map the ice-bedrock interface. We have considered data from three different acquisition systems including (1) a low-frequency pulsed system hanging below the helicopter (BGR), (2) a stepped frequency system hanging below the helicopter (Radar Systemtechnik GmbH [RST]), and (3) a commercial system mounted directly on the helicopter skids (Geophysical Survey Systems Incorporated [GSSI]). The systems showed considerable differences in their performance. The best results were achieved with the BGR system. On average, the RST and GSSI systems yielded comparable results, but we observed significant site-specific differences. A comparison with ground-based GPR data found that the quality of helicopter-borne data is inferior, but the compelling advantages of airborne surveying still make helicopter-borne data acquisition an attractive option. Statistical analyses concerning the bedrock detectability revealed not only large differences between the different acquisition systems but also between different regions within our investigation area. The percentage of bedrock reflections identified (with respect to the overall profile length within a particular region) varied from 11.7% to 68.9%. Obvious factors for missing the bedrock reflections included large bedrock depths and steeply dipping bedrock interfaces, but we also observed that internal features within the ice body may obscure bedrock reflections. In particular, we identified a conspicuous “internal reflection band” in many profiles acquired with the GSSI system. We attribute this feature to abrupt changes of the water content within the ice, but more research is required for a better understanding of the nature of this internal reflection band.

scholarly journals Ice volume and basal topography estimation using geostatistical methods and ground-penetrating radar measurements: application to the Tsanfleuron and Scex Rouge glaciers, Swiss Alps

2021 ◽  
Vol 15 (11) ◽  
pp. 5169-5186
Author(s):  
Alexis Neven ◽  
Valentin Dall'Alba ◽  
Przemysław Juda ◽  
Julien Straubhaar ◽  
Philippe Renard
Keyword(s):  
Ground Penetrating Radar ◽  
Volume Estimation ◽  
Swiss Alps ◽  
Data Set ◽  
Mountain Glacier ◽  
Ice Volume ◽  
Bedrock Topography ◽  
Direct Sampling ◽  
Ground Penetrating ◽  
Geomorphological Features

Abstract. Ground-penetrating radar (GPR) is widely used for determining mountain glacier thickness. However, this method provides thickness data only along the acquisition lines, and therefore interpolation has to be made between them. Depending on the interpolation strategy, calculated ice volumes can differ and can lack an accurate error estimation. Furthermore, glacial basal topography is often characterized by complex geomorphological features, which can be hard to reproduce using classical interpolation methods, especially when the field data are sparse or when the morphological features are too complex. This study investigates the applicability of multiple-point statistics (MPS) simulations to interpolate glacier bedrock topography using GPR measurements. In 2018, a dense GPR data set was acquired on the Tsanfleuron Glacier (Switzerland). These data were used as the source for a bedrock interpolation. The results obtained with the direct-sampling MPS method are compared against those obtained with kriging and sequential Gaussian simulations (SGSs) on both a synthetic data set – with known reference volume and bedrock topography – and the real data underlying the Tsanfleuron Glacier. Using the MPS modeled bedrock, the ice volume for the Scex Rouge and Tsanfleuron glaciers is estimated to be 113.9 ± 1.6 million cubic meters. The direct-sampling approach, unlike the SGS and kriging, allowed not only an accurate volume estimation but also the generation of a set of realistic bedrock simulations. The complex karstic geomorphological features are reproduced and can be used to significantly improve for example the precision of subglacial flow estimation.

scholarly journals Ice thickness distribution of all Swiss glaciers based on extended ground-penetrating radar data and glaciological modeling

2021 ◽  
pp. 1-19
Author(s):  
Melchior Grab ◽  
Enrico Mattea ◽  
Andreas Bauder ◽  
Matthias Huss ◽  
Lasse Rabenstein ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Thickness Distribution ◽  
Radar Data ◽  
Tourism Industry ◽  
Swiss Alps ◽  
Ice Thickness ◽  
Hazard Management ◽  
Bed Topography ◽  
Ice Volume ◽  
Ground Penetrating

Abstract Accurate knowledge of the ice thickness distribution and glacier bed topography is essential for predicting dynamic glacier changes and the future developments of downstream hydrology, which are impacting the energy sector, tourism industry and natural hazard management. Using AIR-ETH, a new helicopter-borne ground-penetrating radar (GPR) platform, we measured the ice thickness of all large and most medium-sized glaciers in the Swiss Alps during the years 2016–20. Most of these had either never or only partially been surveyed before. With this new dataset, 251 glaciers – making up 81% of the glacierized area – are now covered by GPR surveys. For obtaining a comprehensive estimate of the overall glacier ice volume, ice thickness distribution and glacier bed topography, we combined this large amount of data with two independent modeling algorithms. This resulted in new maps of the glacier bed topography with unprecedented accuracy. The total glacier volume in the Swiss Alps was determined to be 58.7 ± 2.5 km3 in the year 2016. By projecting these results based on mass-balance data, we estimated a total ice volume of 52.9 ± 2.7 km3 for the year 2020. Data and modeling results are accessible in the form of the SwissGlacierThickness-R2020 data package.

Seismic reflection and ground‐penetrating radar imaging of a shallow aquifer

Geophysics ◽  
1998 ◽  
Vol 63 (4) ◽  
pp. 1310-1317 ◽  
Author(s):  
Steven J. Cardimona ◽  
William P. Clement ◽  
Katharine Kadinsky‐Cade
Keyword(s):  
Water Table ◽  
Ground Penetrating Radar ◽  
Low Frequency ◽  
Shallow Aquifer ◽  
Cone Penetrometer ◽  
Gradual Change ◽  
Cone Penetration ◽  
Geotechnical Data ◽  
Radar Images ◽  
Ground Penetrating

In 1995 and 1996, researchers associated with the US Air Force’s Phillips and Armstrong Laboratories took part in an extensive geophysical site characterization of the Groundwater Remediation Field Laboratory located at Dover Air Force Base, Dover, Delaware. This field experiment offered an opportunity to compare shallow‐reflection profiling using seismic compressional sources and low‐frequency ground‐penetrating radar to image a shallow, unconfined aquifer. The main target within the aquifer was the sand‐clay interface defining the top of the underlying aquitard at 10 to 14 m depth. Although the water table in a well near the site was 8 m deep, cone penetration geotechnical data taken across the field do not reveal a distinct water table. Instead, cone penetration tests show a gradual change in electrical properties that we interpret as a thick zone of partial saturation. Comparing the seismic and radar data and using the geotechnical data as ground truth, we have associated the deepest coherent event in both reflection data sets with the sand‐clay aquitard boundary. Cone penetrometer data show the presence of a thin lens of clays and silts at about 4 m depth in the north part of the field. This shallow clay is not imaged clearly in the low‐frequency radar profiles. However, the seismic data do image the clay lens. Cone penetrometer data detail a clear change in the soil classification related to the underlying clay aquitard at the same position where the nonintrusive geophysical measurements show a change in image character. Corresponding features in the seismic and radar images are similar along profiles from common survey lines, and results of joint interpretation are consistent with information from geotechnical data across the site.

scholarly journals Particle Center Supported Plane for Subsurface Target Classification based on Full Polarimetric Ground Penetrating Radar

2019 ◽  
Vol 11 (4) ◽  
pp. 405
Author(s):  
Xuan Feng ◽  
Haoqiu Zhou ◽  
Cai Liu ◽  
Yan Zhang ◽  
Wenjing Liang ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Decomposition Methods ◽  
Support Vector ◽  
Classification Methods ◽  
Target Classification ◽  
Data Set ◽  
Particle Center ◽  
Ground Penetrating ◽  
New Criterion

The subsurface target classification of ground penetrating radar (GPR) is a popular topic in the field of geophysics. Among the existing classification methods, geometrical features and polarimetric attributes of targets are primarily used. As polarimetric attributes contain more information of targets, polarimetric decomposition methods, such as H-Alpha decomposition, have been developed for target classification of GPR in recent years. However, the classification template used in H-Alpha classification is preset depending on the experience of synthetic aperture radar (SAR); therefore, it may not be suitable for GPR. Moreover, many existing classification methods require excessive human operation, particularly when outliers exist in the sample (the data set containing the features of targets); therefore, they are not efficient or intelligent. We herein propose a new machine learning method based on sample centers, i.e., particle center supported plane (PCSP). The sample center is defined as the point with the smallest sum of distances from all points in the same sample, which is considered as a better representation of the sample without significant effect of the outliers. In this proposed method, particle swarm optimization (PSO) is performed to obtain the sample centers; the new criterion for subsurface target classification is achieved. We applied this algorithm to full polarimetric GPR data measured in the laboratory and outdoors. The results indicate that, comparing with support vector machine (SVM) and classical H-Alpha classification, this new method is more efficient and the accuracy is relatively high.

On the use of dispersive APVO GPR curves for thin-bed properties estimation: Theory and application to fracture characterization

Geophysics ◽  
2009 ◽  
Vol 74 (1) ◽  
pp. J1-J12 ◽  
Author(s):  
Jacques Deparis ◽  
Stéphane Garambois
Keyword(s):  
Ground Penetrating Radar ◽  
Inverse Method ◽  
Phase Variation ◽  
Fractured Media ◽  
Data Set ◽  
Fracture Characterization ◽  
Propagation Effects ◽  
Phase Liquid ◽  
Ground Penetrating ◽  
Homogeneous Formation

The presence of a thin layer embedded in any formation creates complex reflection patterns caused by interferences within the thin bed. The generated reflectivity amplitude variations with offset have been increasingly used in seismic interpretation and more recently tested on ground-penetrating radar (GPR) data to characterize nonaqueous-phase liquid contaminants. Phase and frequency sensitivities of the reflected signals are generally not used, although they contain useful information. The present study aims to evaluate the potential of these combined properties to characterize a thin bed using GPR data acquired along a common-midpoint (CMP) survey, carried out to assess velocity variations in the ground. It has been restricted to the simple case of a thin bed embedded within a homogeneous formation, a situation often encountered in fractured media. Dispersive properties ofthe dielectric permittivity of investigated materials (homogeneous formation, thin bed) are described using a Jonscher parameterization, which permitted study of the dependency of amplitude and phase variation with offset (APVO) curves on frequency and thin-bed properties (filling nature, aperture). In the second part, we discuss and illustrate the validity of the thin-bed approximation as well as simplify assumptions and make necessary careful corrections to convert raw CMP data into dispersive APVO curves. Two different strategies are discussed to correct the data from propagation effects: a classical normal-moveout approach and an inverse method. Finally, the proposed methodology is applied to a CMP GPR data set acquired along a vertical cliff. It allowed us to extract the characteristics of a subvertical fracture with satisfying resolution and confidence. The study motivates interest to use dispersion dependency of the reflection coefficient variations for thin-bed characterization.

Interpolating wide-aperture ground-penetrating radar beyond aliasing

Geophysics ◽  
2015 ◽  
Vol 80 (2) ◽  
pp. H13-H22 ◽  
Author(s):  
Saulo S. Martins ◽  
Jandyr M. Travassos
Keyword(s):  
Ground Penetrating Radar ◽  
Real Data ◽  
Velocity Model ◽  
Data Sets ◽  
Inversion Problem ◽  
Data Set ◽  
Isolated Points ◽  
Antarctic Continent ◽  
Ground Penetrating ◽  
The Antarctic

Most of the data acquisition in ground-penetrating radar is done along fixed-offset profiles, in which velocity is known only at isolated points in the survey area, at the locations of variable offset gathers such as a common midpoint. We have constructed sparse, heavily aliased, variable offset gathers from several fixed-offset, collinear, profiles. We interpolated those gathers to produce properly sampled counterparts, thus pushing data beyond aliasing. The interpolation methodology estimated nonstationary, adaptive, filter coefficients at all trace locations, including at the missing traces’ corresponding positions, filled with zeroed traces. This is followed by an inversion problem that uses the previously estimated filter coefficients to insert the new, interpolated, traces between the original ones. We extended this two-step strategy to data interpolation by employing a device in which we used filter coefficients from a denser variable offset gather to interpolate the missing traces on a few independently constructed gathers. We applied the methodology on synthetic and real data sets, the latter acquired in the interior of the Antarctic continent. The variable-offset interpolated data opened the door to prestack processing, making feasible the production of a prestack time migrated section and a 2D velocity model for the entire profile. Notwithstanding, we have used a data set obtained in Antarctica; there is no reason the same methodology could not be used somewhere else.

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