Characterization of Cavities Using the GPR, LIDAR and GNSS Techniques

The study of the many types of natural and manmade cavities in different parts of the world is important to the fields of geology, geophysics, engineering, architectures, agriculture, heritages and landscape. Ground-penetrating radar (GPR) is a noninvasive geodetection and geolocation technique suitable for accurately determining buried structures. This technique requires knowing the propagation velocity of electromagnetic waves (EM velocity) in the medium. We propose a method for calibrating the EM velocity using the integration of laser imaging detection and ranging (LIDAR) and GPR techniques using the Global Navigation Satellite System (GNSS) as support for geolocation. Once the EM velocity is known and the GPR profiles have been properly processed and migrated, they will also show the hidden cavities and the old hidden structures from the cellar. In this article, we present a complete study of the joint use of the GPR, LIDAR and GNSS techniques in the characterization of cavities. We apply this methodology to study underground cavities in a group of wine cellars located in Atauta (Soria, Spain). The results serve to identify construction elements that form the cavity and group of cavities or cellars. The described methodology could be applied to other shallow underground structures with surface connection, where LIDAR and GPR profiles could be joined, as, for example, in archaeological cavities, sewerage systems, drainpipes, etc.

Surface velocity and mass balance of Livingston Island ice cap, Antarctica

The mass budget of the ice caps surrounding the Antarctica Peninsula and, in particular, the partitioning of its main components are poorly known. Here we approximate frontal ablation (i.e. the sum of mass losses by calving and submarine melt) and surface mass balance of the ice cap of Livingston Island, the second largest island in the South Shetland Islands archipelago, and analyse variations in surface velocity for the period 2007–2011. Velocities are obtained from feature tracking using 25 PALSAR-1 images, and used in conjunction with estimates of glacier ice thicknesses inferred from principles of glacier dynamics and ground-penetrating radar observations to estimate frontal ablation rates by a flux-gate approach. Glacier-wide surface mass-balance rates are approximated from in situ observations on two glaciers of the ice cap. Within the limitations of the large uncertainties mostly due to unknown ice thicknesses at the flux gates, we find that frontal ablation (−509 ± 263 Mt yr−1, equivalent to −0.73 ± 0.38 m w.e. yr−1 over the ice cap area of 697 km2) and surface ablation (−0.73 ± 0.10 m w.e. yr−1) contribute similar shares to total ablation (−1.46 ± 0.39 m w.e. yr−1). Total mass change (δM = −0.67 ± 0.40 m w.e. yr−1) is negative despite a slightly positive surface mass balance (0.06 ± 0.14 m w.e. yr−1). We find large interannual and, for some basins, pronounced seasonal variations in surface velocities at the flux gates, with higher velocities in summer than in winter. Associated variations in frontal ablation (of ~237 Mt yr−1; −0.34 m w.e. yr−1) highlight the importance of taking into account the seasonality in ice velocities when computing frontal ablation with a flux-gate approach.

Estimate of the total volume of Svalbard glaciers, and their potential contribution to sea-level rise, using new regionally based scaling relationships

We present a set of new volume scaling relationships specific to Svalbard glaciers, derived from a sample of 60 volume–area pairs. Glacier volumes are computed from ground-penetrating radar (GPR)-retrieved ice thickness measurements, which have been compiled from different sources for this study. The most precise scaling models, in terms of lowest cross-validation errors, are obtained using a multivariate approach where, in addition to glacier area, glacier length and elevation range are also used as predictors. Using this multivariate scaling approach, together with the Randolph Glacier Inventory V3.2 for Svalbard and Jan Mayen, we obtain a regional volume estimate of 6700 ± 835 km3, or 17 ± 2 mm of sea-level equivalent (SLE). This result lies in the mid- to low range of recently published estimates, which show values as varied as 13 and 24 mm SLE. We assess the sensitivity of the scaling exponents to glacier characteristics such as size, aspect ratio and average slope, and find that the volume of steep-slope and cirque-type glaciers is not very sensitive to changes in glacier area.

In situ investigation of rapid subsurface flow : Temporal dynamics and catchment-scale implication

Non peer reviewed

A History of Transplants: A Study of Entryway Gardens at Amache

Previous research shows that during the period of Japanese American internment gardening became a popular activity for the interned. Primarily approached historically, little work has been conducted to archaeologically analyze the efforts of landscaping by former internees. Gardening activity can paint a better picture of Japanese American identity during the period of forced confinement. This research investigates internee gardens methodologically through surface survey, ground penetrating radar, excavation, oral history, soil chemistry, archaeobotany, and palynology. The thorough investigation of landscaping efforts of internees builds upon knowledge of expression within Japanese American relocation centers, as well as the understanding of a lineage of gardening as Japanese immigrant tradition. Using available materials, gardeners adapted both tradition and environment for the purpose of improving conditions under internment and maintaining an affiliation to heritage. My examination of internee landscaping better explains how many collectively maintained, adapted, and publicly expressed an ethnic identity.

Steel Pedestrian Bridge to Protect a Unique Tree

This paper describes the “Variation Guggenheim 3: Mirador de la palmera” project, situated in Daya Vieja (Alicante-Spain). This structure is inspired by the Guggenheim museum of New York and is designed to protect a land-mark palm-tree from wind loads. This six – trunk palm tree was declared monument by the Valencian government in 2012. The structure that now protect it appears to fly around de palm tree creating a helicoidally skywalk made of steel, while retrofitting the lateral trunks of the tree to protect them from collapse. An 18 m. long straight beam starts on the top of this helix, and stretches towards a lookout point that offers a view of the whole village and its surroundings. The reduction of the visual impact of the structure on the tree was a major aim for the project design. The structural elements are as slender as possible to avoid the visual obstruction of tree. They are painted white, while the walkway steel corrugated plate is painted green in order to highlight its neat shape among the blur created by the apparent mess of bars of the supporting structure. The two main piles of this pedestrian bridge were designed in steel and geometrically resemble trees. A Ground Penetrating Radar analysis was performed to detect the palm root location and to decide the best foundation system. Slender cast in-situ steel-concrete micropiles along with a concrete pile-cap, raised some centimeters above the ground level, were used to reduce the damage to the roots. The projected pile-cap is a slender, continuous, circular ring; which geometry resembles a concrete bench. This structure has been a finalist in the Architecture Awards for the 2010-2014 best construction projects, held by the Diputación de Alicante.

Characteristics of samples obtained during the expedition to Bolshoy Lyakhovsky Island in July/August 2014

The German-Russian project CARBOPERM - Carbon in Permafrost, origin, quality, quantity, and degradation and microbial turnover - is devoted to studying soil organic matter history, degradation and turnover in coastal lowlands of Northern Siberia. The multidisciplinary project combines research from various German and Russian institutions and runs from 2013 to 2016. The project aims assessing the recent and the ancient trace gas budget over tundra soils in northern Siberia. Studied field sites are placed in the permafrost of the Lena Delta and on Bol'shoy Lyakhovsky, the southernmost island of the New Siberian Archipelago in the eastern Laptev Sea. Field campaigns to Bol'shoy Lyakhovsky in 2014 (chapter 2) were motivated by research on palaeoenvironmental and palaeoclimate reconstruction, sediment dating, near surface geophysics and microbiological research. In particular the field campaigns focussed on: - coring Quaternary strata with a ages back to ~200.000 years ago as found along the southern coast; they allow tracing microbial communities and organic tracers (i.e. lipids and biomarkers, sedimentary DNA) in the deposits across two climatic cycles (chapter 3), - instrumenting a borehole with a thermistor chain for measuring permafrost temperatures (chapter 3), - sampling Quaternary strata for dating permafrost formation periods based on the optical stimulated luminescence (OSL) technique (chapter 4), - sampling soil and geologic formations for carbon content in order to highlight potential release of CO2 and methane based on incubation experiments (chapter 5), - profiling near surface permafrost using ground-penetrating radar and geoelectrics for defining the spatial depositional context, where the cores are located (chapters 6 + 7).

Investigating the Eastern Extent of the Seattle Fault Zone through Depth to Bedrock and Borehole Analysis in Bellevue, Washington

This study identifies lineaments that indicate fault activity and strengthens previous interpretations of structures within the eastern extent of the Seattle Fault zone in Bellevue, WA. My investigation has compiled geotechnical subsurface data, high-resolution LiDAR imagery, and ground-penetrating radar to produce strip log sections transecting identified lineaments and depth-to-bedrock maps exposing fault structure. My work incorporates field investigation, multiple publicly available datasets, and subsurface modeling. My results include a map showing twenty-eight identified surface lineaments, five strip-log sections, and interpolated depth-to-bedrock and minimum-depth-to-bedrock maps. Several lineaments identified in the minimum-depth-to-bedrock raster are parallel to the Seattle Fault zone and suggest the presence of small splay faults beneath east Bellevue. These results strengthen previous interpretations of seismic profile data located in the study area. Another lineament identified in the minimum-depth-to-bedrock raster suggest an unmapped tear fault accommodating differential offset along fault strike between Mercer Island and Bellevue. This work also demonstrates the utility of publicly available datasets such as geotechnical subsurface explorations and LiDAR imagery in supplementing geologic investigations in the eastern extent of the Seattle Fault zone.

Using Image Processing Methods to Improve the Detection of Buried Explosive Threats in GPR Data

Current state of the art techniques for landmine detection in ground penetrating radar (GPR) utilize statistical methods to identify characteristics of a landmine response. This research makes use of 2-D slices of data in which subsurface landmine responses have hyperbolic shapes. Various methods from the field of visual image processing are adapted to the 2-D GPR data, producing superior landmine detection results. This research goes on to develop a physics-based GPR augmentation method motivated by current advances in visual object detection. This GPR specific augmentation is used to mitigate issues caused by insufficient training sets. This work shows that augmentation improves detection performance under training conditions that are normally very difficult. Finally, this work introduces the use of convolutional neural networks as a method to learn feature extraction parameters. These learned convolutional features outperform hand-designed features in GPR detection tasks. This work presents a number of methods, both borrowed from and motivated by the substantial work in visual image processing. The methods developed and presented in this work show an improvement in overall detection performance and introduce a method to improve the robustness of statistical classification.

Diagnostic des systèmes de drainage basé sur l'imagerie du géoradar en culture de canneberges

Une méthode optimale pour identifier les zones problématiques de drainage dans les champs de canneberges est d’un intérêt pratique pour les producteurs. Elle peut aider au développement de stratégies visant à améliorer le rendement des cultures. L’objectif de cette étude était de développer une méthodologie non intrusive de diagnostic d’un système de drainage en utilisant l’imagerie du géoradar ou Ground Penetrating Radar (GPR) ayant pour finalité de localiser les zones restrictives au drainage et de lier les scans du GPR à des propriétés du sol. Un système GPR muni d’une antenne monostatique a été utilisé pour acquérir des données dans deux champs de canneberges : un construit sur sol organique et l’autre sur sol minéral. La visualisation en trois dimensions de la stratification du champ a été possible après l’interpolation et l’analyse des faciès. La variabilité spatiale du rendement des cultures et la conductivité hydraulique saturée du sol ont été comparées aux données GPR par deux méthodes : calcul du pourcentage de différence et estimation de l’entropie. La visualisation des données couplée à leur analyse a permis de mettre en évidence la géométrie souterraine et des discontinuités importantes des champs. Les résultats montrent qu’il y a bonne corrélation entre les zones où la couche restrictive est plus superficielle et celle de faible rendement. Le niveau de similarité entre la conductivité hydraulique saturée et la profondeur de la couche restrictive confirme la présence de cette dernière. L’étape suivante a été la reconstruction de l’onde électromagnétique et son ajustement par modélisation inverse. Des informations quantitatives ont été extraites des scans : la permittivité diélectrique, la conductivité électrique et l’épaisseur des strates souterraines. Les permittivités diélectriques modélisées sont concordantes avec celles mesurées in-situ et celles de la littérature. Enfin, en permettant la caractérisation des discontinuités du sous-sol, les zones les plus pertinentes pour l’amélioration du drainage et d’irrigation ont été localisées, afin de maximiser le rendement.

Soft computing based parameter identification in pavements and geomechanical systems

Accurate estimation of road pavement geometry and layer material properties through the use of proper nondestructive testing and sensor technologies is essential for evaluating pavement’s structural condition and determining options for maintenance and rehabilitation. For these purposes, pavement deflection basins produced by the nondestructive Falling Weight Deflectometer (FWD) test data are commonly used. The nondestructive FWD test drops weights on the pavement to simulate traffic loads and measures the created pavement deflection basins. Backcalculation of pavement geometry and layer properties using FWD deflections is a difficult inverse problem, and the solution with conventional mathematical methods is often challenging due to the ill-posed nature of the problem. In this dissertation, a hybrid algorithm was developed to seek robust and fast solutions to this inverse problem. The algorithm is based on soft computing techniques, mainly Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs) as well as the use of numerical analysis techniques to properly simulate the geomechanical system. A widely used pavement layered analysis program ILLI-PAVE was employed in the analyses of flexible pavements of various pavement types; including full-depth asphalt and conventional flexible pavements, were built on either lime stabilized soils or untreated subgrade. Nonlinear properties of the subgrade soil and the base course aggregate as transportation geomaterials were also considered. A computer program, Soft Computing Based System Identifier or SOFTSYS, was developed. In SOFTSYS, ANNs were used as surrogate models to provide faster solutions of the nonlinear finite element program ILLI-PAVE. The deflections obtained from FWD tests in the field were matched with the predictions obtained from the numerical simulations to develop SOFTSYS models. The solution to the inverse problem for multi-layered pavements is computationally hard to achieve and is often not feasible due to field variability and quality of the collected data. The primary difficulty in the analysis arises from the substantial increase in the degree of non-uniqueness of the mapping from the pavement layer parameters to the FWD deflections. The insensitivity of some layer properties lowered SOFTSYS model performances. Still, SOFTSYS models were shown to work effectively with the synthetic data obtained from ILLI-PAVE finite element solutions. In general, SOFTSYS solutions very closely matched the ILLI-PAVE mechanistic pavement analysis results. For SOFTSYS validation, field collected FWD data were successfully used to predict pavement layer thicknesses and layer moduli of in-service flexible pavements. Some of the very promising SOFTSYS results indicated average absolute errors on the order of 2%, 7%, and 4% for the Hot Mix Asphalt (HMA) thickness estimation of full-depth asphalt pavements, full-depth pavements on lime stabilized soils and conventional flexible pavements, respectively. The field validations of SOFTSYS data also produced meaningful results. The thickness data obtained from Ground Penetrating Radar testing matched reasonably well with predictions from SOFTSYS models. The differences observed in the HMA and lime stabilized soil layer thicknesses observed were attributed to deflection data variability from FWD tests. The backcalculated asphalt concrete layer thickness results matched better in the case of full-depth asphalt flexible pavements built on lime stabilized soils compared to conventional flexible pavements. Overall, SOFTSYS was capable of producing reliable thickness estimates despite the variability of field constructed asphalt layer thicknesses.

Sistema inteligente para estimar a porosidade em sedimentos a partir da análise de sinais GPR

This Thesis presents the elaboration of a methodological propose for the development of an intelligent system, able to automatically achieve the effective porosity, in sedimentary layers, from a data bank built with information from the Ground Penetrating Radar GPR. The intelligent system was built to model the relation between the porosity (response variable) and the electromagnetic attribute from the GPR (explicative variables). Using it, the porosity was estimated using the artificial neural network (Multilayer Perceptron MLP) and the multiple linear regression. The data from the response variable and from the explicative variables were achieved in laboratory and in GPR surveys outlined in controlled sites, on site and in laboratory. The proposed intelligent system has the capacity of estimating the porosity from any available data bank, which has the same variables used in this Thesis. The architecture of the neural network used can be modified according to the existing necessity, adapting to the available data bank. The use of the multiple linear regression model allowed the identification and quantification of the influence (level of effect) from each explicative variable in the estimation of the porosity. The proposed methodology can revolutionize the use of the GPR, not only for the imaging of the sedimentary geometry and faces, but mainly for the automatically achievement of the porosity one of the most important parameters for the characterization of reservoir rocks (from petroleum or water)