Integrated Ground Penetrating Radar (GPR) imaging and characterization of glacial and periglacial environments

This PhD research is based on Ground Penetrating Radar (GPR) imaging and characterization of glacial and periglacial environments. Its main focus is the assessment of the physical meaning of electromagnetic (EM) facies of frozen materials, proving that a detailed analysis of the geophysical data and often the integration with other prospecting techniques is essential because inferences on some kind of facies could not be clearly unambiguous. Chapters 2 and 3 of this dissertation present the characterization of a high scattered facies within an ice body, which was proved to be not straightforwardly related to warm ice and the presence of liquid water, as usually occurs in GPR data. An investigation approach based on differential diagnosis of the information obtained by different techniques (as GPR, photogrammetry, geomorphology) was proposed, representing something completely new for geophysical applications. Once such a facies was related to englacial debris within the ice, GPR modelling and inversion were fundamental to provide a first quantification of the debris causing the high scattered zone through a scattering amplitude inversion approach based on the combined analysis of synthetic and field data. It resulted that just a percentage below 10% in volume can produce the high scattered facies imaged on GPR data. A second focus of the research, arising from the main one, gets the issue of the ambiguity of the interpretation, the integration of techniques and the role of debris in a glacial body for improving both the characterization of an Alpine glacier and the geometrical imaging of Antarctic environments. The outcome of these researches, which are still ongoing, points out the relation between some surficial structures and the subsurface, revealing much more complex settings than expected just from geomorphological analysis and local drilling. As a matter of fact, this research deepened the knowledge in the identification of peculiar EM facies, including dead ice patches, and morphologies which affect the occurrence of periglacial elements and mixed glacial and fluvio-glacial features. Such research allowed to develop dedicated and new methodology of data analysis, considering GPR attribute analysis, differential diagnosis and the scattering amplitude approach for GPR inversion. The outcomes reached through this research are innovative, as they open new research possibilities and define the road ahead not only for future GPR glaciological researches, but also for different practical applications.This PhD research is based on Ground Penetrating Radar (GPR) imaging and characterization of glacial and periglacial environments. Its main focus is the assessment of the physical meaning of electromagnetic (EM) facies of frozen materials, proving that a detailed analysis of the geophysical data and often the integration with other prospecting techniques is essential because inferences on some kind of facies could not be clearly unambiguous. Chapters 2 and 3 of this dissertation present the characterization of a high scattered facies within an ice body, which was proved to be not straightforwardly related to warm ice and the presence of liquid water, as usually occurs in GPR data. An investigation approach based on differential diagnosis of the information obtained by different techniques (as GPR, photogrammetry, geomorphology) was proposed, representing something completely new for geophysical applications. Once such a facies was related to englacial debris within the ice, GPR modelling and inversion were fundamental to provide a first quantification of the debris causing the high scattered zone through a scattering amplitude inversion approach based on the combined analysis of synthetic and field data. It resulted that just a percentage below 10% in volume can produce the high scattered facies imaged on GPR data. A second focus of the research, arising from the main one, gets the issue of the ambiguity of the interpretation, the integration of techniques and the role of debris in a glacial body for improving both the characterization of an Alpine glacier and the geometrical imaging of Antarctic environments. The outcome of these researches, which are still ongoing, points out the relation between some surficial structures and the subsurface, revealing much more complex settings than expected just from geomorphological analysis and local drilling. As a matter of fact, this research deepened the knowledge in the identification of peculiar EM facies, including dead ice patches, and morphologies which affect the occurrence of periglacial elements and mixed glacial and fluvio-glacial features. Such research allowed to develop dedicated and new methodology of data analysis, considering GPR attribute analysis, differential diagnosis and the scattering amplitude approach for GPR inversion. The outcomes reached through this research are innovative, as they open new research possibilities and define the road ahead not only for future GPR glaciological researches, but also for different practical applications

The Use of Coincident Synthetic Aperture Radar and Visible Imagery to Aid in the Analysis of Photon-Counting Lidar Data Sets Over Complex Ice/Snow Surfaces

Qualitative and quantitative analysis of multi-sensor data is becoming increasingly useful as a method of improving our understanding of complex environments, and can be an effective tool in the arsenal to help climate scientists to predict sea level rise due to change in the mass balance of large glaciers in the Arctic and Antarctic. A novel approach to remote sensing of the continuously changing polar environment involves the use of coincident RADARSAT-2 synthetic aperture radar (SAR) imagery and Landsat 7 visible/near-infrared imagery, combined with digital elevation models (DEM) developed from Multiple Altimeter Beam Experimental Lidar (MABEL) data sets. MABEL is a scaled down model of the lidar altimeter that will eventually be flown on ICESat-2, and provides dense along-track and moderate slope (cross-track) elevation data over narrow (~198 m) aircraft transects. Because glacial terrain consists of steep slopes, crevices, glacial lakes, and outflow into the sea, accurate slope information is critical to our understanding of any changes that may be happening in the ice sheets. RADARSAT-2 operates in the C-band, at a wavelength of 5.55 cm, and was chosen partly for its ability to image the Earth under all atmospheric conditions, including clouds. The SAR images not only provide spatial context for the elevation data found using the lidar, but also offer key insights into the consistency of the snow and ice making up the glacier, giving us some idea of mean temperature and surface conditions on the ice sheet. Finally, Landsat 7 images provide us with information on the extent of the glacier, and additional understanding of the state of the glacial surface. To aid in the analysis of the three data sets, proper preparation of each data set must first be performed. For the lidar data, this required the development of a new data reduction technique, based on statistical analysis, to reduce the number of received photons to those representing only the surface return. Accordingly, the raw SAR images require calibration, speckle reduction, and geocorrection, before they can be used. Landsat 7 bands are selected to provide the most contrast between rock, snow, and other surface features, and compiled into a three-band red, green, blue (RGB) image. By qualitatively analyzing images and data taken only a short time apart using multiple imaging modalities, we are able to accurately compare glacial surface features to elevation provided by MABEL, with the goal of increasing our understanding of how the glacier is changing over time. Quantitative analysis performed throughout this thesis has indicated that there is a strong correlation between top-of-the-atmosphere reflectance (Landsat 7), σ,0-calibrated HH and HV polarized backscatter coefficients (RADARSAT-2), elevation (MABEL), and various surface features and glacial zones on the ice sheet. By comparing data from unknown or mixed surfaces to known quantities scientists can effectively estimate the type of glacial zone the area of interest occurs in. Climate scientists can then use this data, along with long-term digital elevations models, as a measure of predicting climate change

Laser Based Altimetry for Unmanned Aerial Vehicle Hovering Over a Snow Surface

A microwave radar for non-invasive snow stratigraphy measurements has been developed. Results were promising, but it failed to detect light powder snow in the air-snowpack interface. The aim of this thesis is to find and verify a system for estimating altitude on centimeter scale over a snow surface, independent of snow conditions. Also, relative pitch and roll angle estimation between the UAV and local surface should be resolved, to help directing the radar beam perpendicularly to the surface. After a variety of technical solutions were examined, we propose a system of three time-of-flight near-infrared altimeters pointing at different directions towards the surface. Experimental results showed RMS error of 1.39 cm for range estimation averaged over the most common snow types, and 2.81 cm for wet snow, which was the least reflective medium. An experiment conducted for an array of two altimeters scanning over a snow surface, showed that the local, relative surface tilt was found to be accurate within ±2° given that it was sufficiently planar. Further, the altitude RMS error was estimated to 1.57 cm. We conclude that the chosen altimeter was within the requirements, and that an array of three altimeters would give acceptable relative tilt estimation in to planes on the snow surface. The system should be subject to flight testing and implemented on UAV platform such that it can aid the microwave radar system during snow scanning

New geophysics study programs at the University of Helsinki

Peer reviewe

Petrophysical properties of the Kylylahti Cu-Au-Zn sulphide mineralization and its host rocks

Non peer reviewe

Drone-Borne Ground-Penetrating Radar for Snow Cover Mapping

Ground-penetrating radar (GPR) is one of the most commonly used instruments to map the Snow Water Equivalent (SWE) in mountainous regions. However, some areas may be difficult or dangerous to access; besides, some surveys can be quite time-consuming. We test a new system to fulfill the need to speed up the acquisition process for the analysis of the SWE and to access remote or dangerous areas. A GPR antenna (900 MHz) is mounted on a drone prototype designed to carry heavy instruments, fly safely at high altitudes, and avoid interference of the GPR signal. A survey of two test sites of the Alpine region during winter 2020-2021 is presented, to check the prototype performance for mapping the snow thickness at the catchment scale. We process the data according to a standard flow-chart of radar processing and we pick both the travel times of the air-snow interface and the snow-ground interface to compute the travel time difference and to estimate the snow depth. The calibration of the radar snow depth is performed by comparing the radar travel times with snow depth measurements at preselected stations. The main results show fairly good reliability and performance in terms of data quality, accuracy, and spatial resolution in snow depth monitoring. We tested the device in the condition of low snow density (<200 kg/m(3)) and this limits the detectability of the air-snow interface. This is mainly caused by low values of the electrical permittivity of the dry soft snow, providing a weak reflectivity of the snow surface. To overcome this critical aspect, we use the data of the rangefinder to properly detect the travel time of the snow-air interface. This sensor is already installed in our prototype and in most commercial drones for flight purposes. Based on our experience with the prototype, various improvement strategies and limitations of drone-borne GPR acquisition are discussed. In conclusion, the drone technology is found to be ready to support GPR-based snow depth mapping applications at high altitudes, provided that the operators acquire adequate knowledge of the devices, in order to effectively build, tune, use and maintain a reliable acquisition system

Fiber-optic interferometric sensor for monitoring automobile and rail traffic

This article describes a fiber-optic interferometric sensor and measuring scheme including input-output components for traffic density monitoring. The proposed measuring system is based on the interference in optical fibers. The sensor, based on the Mach-Zehnder interferometer, is constructed to detect vibration and acoustic responses caused by vehicles moving around the sensor. The presented solution is based on the use of single-mode optical fibers (G.652.D and G.653) with wavelength of 1550 nm and laser source with output power of 1 mW. The benefit of this solution lies in electromagnetic interference immunity and simple implementation because the sensor does not need to be installed destructively into the roadway and railroad tracks. The measuring system was tested in real traffic and is characterized by detection success of 99.27% in the case of automotive traffic and 100% in the case of rail traffic.Web of Science2662995298