Joint inversion of crosshole GPR and seismic traveltime data

Joint inversion of crosshole ground-penetrating radar and seismic data can improve model resolution and fidelity of the resultant individual models. Model coupling obtained by minimizing or penalizing some measure of structural dissimilarity between models appears to be the most versatile approach because only weak assumptions about petrophysical relationships are required. Nevertheless, experimental results and petrophysical arguments suggest that when porosity variations are weak in saturated unconsolidated environments, then radar wave speed is approximately linearly related to seismic wave speed. Under such circumstances, model coupling also can be achieved by incorporating cross-covariances in the model regularization. In two case studies, structural similarity is imposed by penalizing models for which the model cross-gradients are nonzero. A first case study demonstrates improvements in model resolution by comparing the resulting models with borehole information, whereas a second case study uses point-spread functions. Although radar seismic wavespeed crossplots are very similar for the two case studies, the models plot in different portions of the graph, suggesting variances in porosity. Both examples display a close, quasilinear relationship between radar seismic wave speed in unconsolidated environments that is described rather well by the corresponding lower Hashin-Shtrikman (HS) bounds. Combining crossplots of the joint inversion models with HS bounds can constrain porosity and pore structure better than individual inversion results can.

Constraining 3-D electrical resistance tomography with GPR reflection data forimproved aquifer characterization

Surface-based ground penetrating radar (GPR) and electrical resistance tomography (ERT) are common tools for aquifer characterization, because both methods provide data that are sensitive to hydrogeologically relevant quantities. To retrieve bulk subsurface properties at high resolution, we suggest incorporating structural information derived from GPR reflection data when inverting surface ERT data. This reduces resolution limitations, which might hinder quantitative interpretations. Surface-based GPR reflection and ERT data have been recorded on an exposed gravel bar within a restored section of a previously channelized river in northeastern Switzerland to characterize an underlying gravel aquifer. The GPR reflection data acquired over an area of 240×40 m map the aquifer's thickness and two internal sub-horizontal regions with different depositional patterns. The interface between these two regions and the boundary of the aquifer with then underlying clay are incorporated in an unstructured ERT mesh. Subsequent inversions are performed without applying smoothness constraints across these boundaries. Inversion models obtained by using these structural constraints contain subtle resistivity variations within the aquifer that are hardly visible in standard inversion models as a result of strong vertical smearing in the latter. In the upper aquifer region, with high GPR coherency and horizontal layering, the resistivity is moderately high (N300 Ωm). We suggest that this region consists of sediments that were rearranged during more than a century of channelized flow. In the lower low coherency region, the GPR image reveals fluvial features (e.g., foresets) and generally more heterogeneous deposits. In this region, the resistivity is lower (~200 Ωm), which we attribute to increased amounts of fines in some of the well-sorted fluvial deposits. We also find elongated conductive anomalies that correspond to the location of river embankments that were removed in 2002.

Inversion of multiple intersecting high-resolution crosshole GPR profiles for hydrological characterization at the Boise Hydrogeophysical Research Site

The integration of geophysical data into the subsurface characterization problem has been shown in many cases to significantly improve hydrological knowledge by providing information at spatial scales and locations that is unattainable using conventional hydrological measurement techniques. In particular, crosshole ground-penetrating radar (GPR) tomography has shown much promise in hydrology because of its ability to provide highly detailed images of subsurface radar wave velocity, which is strongly linked to soil water content. Here, we develop and demonstrate a procedure for inverting together multiple crosshole GPR data sets in order to characterize the spatial distribution of radar wave velocity below the water table at the Boise Hydrogeophysical Research Site (BHRS) near Boise, Idaho, USA. Specifically, we jointly invert 31 intersecting crosshole GPR profiles to obtain a highly resolved and consistent radar velocity model along the various profile directions. The model is found to be strongly correlated with complementary neutron porosity-log data and is further corroborated by larger-scale structural information at the BHRS. This work is an important prerequisite to using crosshole GPR data together with existing hydrological measurements for improved groundwater flow and contaminant transport modeling.

Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.

Modélisation hydrologique des précipitations et des crues extrêmes dans les bassins versants alpin

Les crues et les risques de débordement des barrages, notamment des digues en terre, en cas de fortes précipitations, préoccupent depuis longtemps les autorités et la population. Les études réalisées dans les dernières années ont montré que le réchauffement global du climat s'est accompagné d'une augmentation de la fréquence des fortes précipitations et des crues en Suisse et dans de nombreuses régions du globe durant le 20ème siècle. Les modèles climatiques globaux et régionaux prévoient que la fréquence des fortes précipitations devrait continuer à croître durant le 21éme siècle en Suisse et dans le monde. Cela rend les recherches actuelles sur la modélisation des pluies et des crues à une échelle fine encore plus importantes. En Suisse, pour assurer une bonne protection sur le plan humain et économique, des cartes de précipitations maximales probables (PMP) ont été réalisées. Les PMP ont été confrontées avec les précipitations extrêmes mesurées dans les différentes régions du pays. Ces PMP sont ensuite utilisées par les modèles hydrologiques pour calculer des crues maximales probables (PMF). Cette la méthode PMP-PMF nécessite toutefois un certain nombre de précautions. Si elle est appliquée d'une manière incorrecte ou sur la base de données insuffisantes, elle peut entraîner une surestimation des débits de crue, notamment pour les grands bassins et pour les régions montagneuses entraînant des surcoûts importants. Ces problèmes résultent notamment du fait que la plupart des modèles hydrologiques répartissent les précipitations extrêmes (PMP) de manière uniforme dans le temps sur l'ensemble du bassin versant. Pour remédier ce problème, cette thèse a comme objectif principal de développer un modèle hydrologique distribué appelé MPF (Modeling Precipitation Flood) capable d'estimer la PMF de manière réaliste à partir de la PMP distribuée de manière spatio-temporelle à l'aide des nuages. Le modèle développé MPF comprend trois parties importantes. Dans la première partie, les précipitations extrêmes calculées par un modèle météorologique à une méso-échelle avec une résolution horizontale de 2 km, sont réparties à une échelle locale (25 ou 50 m) de manière non-uniforme dans l'espace et dans le temps. La deuxième partie concerne la modélisation de l'écoulement de l'eau en surface et en subsurface en incluant l'infiltration et l'exfiltration. Et la troisième partie inclut la modélisation de la fonte des neiges, basée sur un calcul de transfert de chaleur. Le modèle MPF a été calibré sur des bassins versants alpins où les données de précipitations et du débit sont disponibles pour une période considérablement longue, qui inclut plusieurs épisodes de fortes pluies avec des débits élevés. À partir de ces épisodes, les paramètres d'entrée du modèle tel que la rugosité du sol et la largeur moyenne des cours d'eau dans le cas d'écoulement de surface ont pu être estimés. Suivant la même procédure, les paramètres utilisés dans la simulation des écoulements en subsurface sont également estimés indirectement, puisque des mesures directes de l'écoulement en subsurface et de l'exfiltration sont difficiles à obtenir. Le modèle de distribution spatio-temporelle de la pluie a aussi été validé en utilisant les images radar avec la structure de la pluie provoquée par un nuage supercellulaire. Les hyétogrammes obtenus sur plusieurs points du terrain sont très proches de ceux enregistrées avec les images radar. Les résultats de la validation du modèle sur les épisodes de fortes crues présentent une bonne synchronisation entre le débit simulé et le débit observé. Cette corrélation a été mesurée avec trois critères d'efficacité, qui ont tous donné des valeurs satisfaisantes. Cela montre que le modèle développé est valide et il peut être utilisé pour des épisodes extrêmes tels que la PMP. Des simulations ont été faites sur plusieurs bassins ayant comme données d'entrée la pluie de type PMP. Des conditions variées ont été utilisées, comme la situation du sol saturé, ou non-saturé, ou la présence d'une couche de neige sur le terrain au moment de la PMP, ce qui conduit à une estimation de PMF pour des scénarios catastrophiques. Enfin, les résultats obtenus montrent comment mieux estimer la valeur de la crue de sécurité des barrages, à partir d'une pluie extrême dix-millennale avec une période de retour de 10'000 ans.

Stochastic inversion of tracer test and electrical geophysical data to estimate hydraulic conductivities.

Quantifying the spatial configuration of hydraulic conductivity (K) in heterogeneous geological environments is essential for accurate predictions of contaminant transport, but is difficult because of the inherent limitations in resolution and coverage associated with traditional hydrological measurements. To address this issue, we consider crosshole and surface-based electrical resistivity geophysical measurements, collected in time during a saline tracer experiment. We use a Bayesian Markov-chain-Monte-Carlo (McMC) methodology to jointly invert the dynamic resistivity data, together with borehole tracer concentration data, to generate multiple posterior realizations of K that are consistent with all available information. We do this within a coupled inversion framework, whereby the geophysical and hydrological forward models are linked through an uncertain relationship between electrical resistivity and concentration. To minimize computational expense, a facies-based subsurface parameterization is developed. The Bayesian-McMC methodology allows us to explore the potential benefits of including the geophysical data into the inverse problem by examining their effect on our ability to identify fast flowpaths in the subsurface, and their impact on hydrological prediction uncertainty. Using a complex, geostatistically generated, two-dimensional numerical example representative of a fluvial environment, we demonstrate that flow model calibration is improved and prediction error is decreased when the electrical resistivity data are included. The worth of the geophysical data is found to be greatest for long spatial correlation lengths of subsurface heterogeneity with respect to wellbore separation, where flow and transport are largely controlled by highly connected flowpaths.

3D crosshole ERT for aquifer characterization and monitoring of infiltrating river water

The hydrogeological properties and responses of a productive aquifer in northeastern Switzerland are investigated. For this purpose, 3D crosshole electrical resistivity tomography (ERT) is used to define the main lithological structures within the aquifer (through static inversion) and to monitor the water infiltration from an adjacent river. During precipitation events and subsequent river flooding, the river water resistivity increases. As a consequence, the electrical characteristics of the infiltrating water can be used as a natural tracer to delineate preferential flow paths and flow velocities. The focus is primarily on the experiment installation, data collection strategy, and the structural characterization of the site and a brief overview of the ERT monitoring results. The monitoring system comprises 18 boreholes each equipped with 10 electrodes straddling the entire thickness of the gravel aquifer. A multi-channel resistivity system programmed to cycle through various four-point electrode configurations of the 180 electrodes in a rolling sequence allows for the measurement of approximately 15,500 apparent resistivity values every 7 h on a continuous basis. The 3D static ERT inversion of data acquired under stable hydrological conditions provides a base model for future time-lapse inversion studies and the means to investigate the resolving capability of our acquisition scheme. In particular, it enables definition of the main lithological structures within the aquifer. The final ERT static model delineates a relatively high-resistivity, low-porosity, intermediate-depth layer throughout the investigated aquifer volume that is consistent with results from well logging and seismic and radar tomography models. The next step will be to define and implement an appropriate time-lapse ERT inversion scheme using the river water as a natural tracer. The main challenge will be to separate the superposed time-varying effects of water table height, temperature, and salinity variations associated with the infiltrating water.

Evolution and sedimentology of a channel fill in the sandy braided South Saskatchewan River and its comparison to the deposits of an adjacent compound bar

The depositional stratigraphy of within-channel deposits in sandy braided rivers is dominated by a variety of barforms (both singular `unit' bars and complex `compound' bars), as well as the infill of individual channels (herein termed `channel fills'). The deposits of bars and channel fills define the key components of facies models for braided rivers and their within-channel heterogeneity, knowledge of which is important for reservoir characterization. However, few studies have sought to address the question of whether the deposits of bars and channel fills can be readily differentiated from each other. This paper presents the first quantitative study to achieve this aim, using aerial images of an evolving modern sandy braided river and geophysical imaging of its subsurface deposits. Aerial photographs taken between 2000 and 2004 document the abandonment and fill of a 1 3 km long, 80 m wide anabranch channel in the sandy braided South Saskatchewan River, Canada. Upstream river regulation traps the majority of very fine sediment and there is little clay (<1%) in the bed sediments. Channel abandonment was initiated by a series of unit bars that stalled and progressively blocked the anabranch entrance, together with dune deposition and stacking at the anabranch entrance and exit. Complete channel abandonment and subsequent fill of up to 3 m of sediment took approximately two years. Thirteen kilometres of ground-penetrating radar surveys, coupled with 18 cores, were obtained over the channel fill and an adjacent 750 m long, 400 m wide, compound bar, enabling a quantitative analysis of the channel and bar deposits. Results show that, in terms of grain-size trends, facies proportions and scale of deposits, there are only subtle differences between the channel fill and bar deposits which, therefore, renders them indistinguishable. Thus, it may be inappropriate to assign different geometric and sedimentological attributes to channel fill and bar facies in object-based models of sandy braided river alluvial architecture.

Quantification of the relation between surface morphodynamics and subsurface sedimentological product in sandy braided rivers

This study uses digital elevation models and ground-penetrating radar to quantify the relation between the surface morphodynamics and subsurface sedimentology in the sandy braided South Saskatchewan River, Canada. A unique aspect of the methodology is that both digital elevation model and ground-penetrating radar data were collected from the same locations in 2004, 2005, 2006 and 2007, thus enabling the surface morphodynamics to be tied explicitly to the associated evolving depositional product. The occurrence of a large flood in 2005 also allowed the influence of discharge to be assessed with respect to the processproduct relationship. The data demonstrate that the morphology of the study reach evolved even during modest discharges, but more extensive erosion was caused by the large flood. In addition, the study reach was dominated by compound bars before the flood, but switched to being dominated by unit bars during and after the flood. The extent to which the subsurface deposits (the product') were modified by the surface morphodynamics (the process') was quantified using the changes in radar-facies recorded in sequential ground-penetrating radar surveys. These surveys reveal that during the large flood there was an increase in the proportion of facies associated with bar margin accretion and larger dunes. In subsequent years, these facies became truncated and replaced with facies associated with smaller dune sets. This analysis shows that unit bars generally become truncated more laterally than vertically and, thus, they lose the high-angle bar margin deposits and smaller scale bar-top deposits. In general, the only fragments that remain of the unit bars are dune sets, thus making identification of the original unit barform problematic. This novel data set has implications for what may ultimately become preserved in the rock record.

Scales and causes of heterogeneity in bars in a large multi-channel river: Rio Parana, Argentina

To date, published studies of alluvial bar architecture in large rivers have been restricted mostly to case studies of individual bars and single locations. Relatively little is known about how the depositional processes and sedimentary architecture of kilometre-scale bars vary within a multi-kilometre reach or over several hundreds of kilometres downstream. This study presents Ground Penetrating Radar and core data from 11, kilometre-scale bars from the Rio Parana, Argentina. The investigated bars are located between 30km upstream and 540km downstream of the Rio Parana - Rio Paraguay confluence, where a significant volume of fine-grained suspended sediment is introduced into the network. Bar-scale cross-stratified sets, with lengths and widths up to 600m and thicknesses up to 12m, enable the distinction of large river deposits from stacked deposits of smaller rivers, but are only present in half the surface area of the bars. Up to 90% of bar-scale sets are found on top of finer-grained ripple-laminated bar-trough deposits. Bar-scale sets make up as much as 58% of the volume of the deposits in small, incipient mid-channel bars, but this proportion decreases significantly with increasing age and size of the bars. Contrary to what might be expected, a significant proportion of the sedimentary structures found in the Rio Parana is similar in scale to those found in much smaller rivers. In other words, large river deposits are not always characterized by big structures that allow a simple interpretation of river scale. However, the large scale of the depositional units in big rivers causes small-scale structures, such as ripple sets, to be grouped into thicker cosets, which indicate river scale even when no obvious large-scale sets are present. The results also show that the composition of bars differs between the studied reaches upstream and downstream of the confluence with the Rio Paraguay. Relative to other controls on downstream fining, the tributary input of fine-grained suspended material from the Rio Paraguay causes a marked change in the composition of the bar deposits. Compared to the upstream reaches, the sedimentary architecture of the downstream reaches in the top ca 5m of mid-channel bars shows: (i) an increase in the abundance and thickness (up to metre-scale) of laterally extensive (hundreds of metres) fine-grained layers; (ii) an increase in the percentage of deposits comprised of ripple sets (to >40% in the upper bar deposits); and (iii) an increase in bar-trough deposits and a corresponding decrease in bar-scale cross-strata (<10%). The thalweg deposits of the Rio Parana are composed of dune sets, even directly downstream from the Rio Paraguay where the upper channel deposits are dominantly fine-grained. Thus, the change in sedimentary facies due to a tributary point-source of fine-grained sediment is primarily expressed in the composition of the upper bar deposits.

Conditioning of multiple-point statistics facies simulations to tomographic images

Geophysical tomography captures the spatial distribution of the underlying geophysical property at a relatively high resolution, but the tomographic images tend to be blurred representations of reality and generally fail to reproduce sharp interfaces. Such models may cause significant bias when taken as a basis for predictive flow and transport modeling and are unsuitable for uncertainty assessment. We present a methodology in which tomograms are used to condition multiple-point statistics (MPS) simulations. A large set of geologically reasonable facies realizations and their corresponding synthetically calculated cross-hole radar tomograms are used as a training image. The training image is scanned with a direct sampling algorithm for patterns in the conditioning tomogram, while accounting for the spatially varying resolution of the tomograms. In a post-processing step, only those conditional simulations that predicted the radar traveltimes within the expected data error levels are accepted. The methodology is demonstrated on a two-facies example featuring channels and an aquifer analog of alluvial sedimentary structures with five facies. For both cases, MPS simulations exhibit the sharp interfaces and the geological patterns found in the training image. Compared to unconditioned MPS simulations, the uncertainty in transport predictions is markedly decreased for simulations conditioned to tomograms. As an improvement to other approaches relying on classical smoothness-constrained geophysical tomography, the proposed method allows for: (1) reproduction of sharp interfaces, (2) incorporation of realistic geological constraints and (3) generation of multiple realizations that enables uncertainty assessment.

From Regional Landslide Detection to Site-Specific Slope Deformation Monitoring and Modelling Based on Active Remote Sensors

Landslide processes can have direct and indirect consequences affecting human lives and activities. In order to improve landslide risk management procedures, this PhD thesis aims to investigate capabilities of active LiDAR and RaDAR sensors for landslides detection and characterization at regional scales, spatial risk assessment over large areas and slope instabilities monitoring and modelling at site-specific scales. At regional scales, we first demonstrated recent boat-based mobile LiDAR capabilities to model topography of the Normand coastal cliffs. By comparing annual acquisitions, we validated as well our approach to detect surface changes and thus map rock collapses, landslides and toe erosions affecting the shoreline at a county scale. Then, we applied a spaceborne InSAR approach to detect large slope instabilities in Argentina. Based on both phase and amplitude RaDAR signals, we extracted decisive information to detect, characterize and monitor two unknown extremely slow landslides, and to quantify water level variations of an involved close dam reservoir. Finally, advanced investigations on fragmental rockfall risk assessment were conducted along roads of the Val de Bagnes, by improving approaches of the Slope Angle Distribution and the FlowR software. Therefore, both rock-mass-failure susceptibilities and relative frequencies of block propagations were assessed and rockfall hazard and risk maps could be established at the valley scale. At slope-specific scales, in the Swiss Alps, we first integrated ground-based InSAR and terrestrial LiDAR acquisitions to map, monitor and model the Perraire rock slope deformation. By interpreting both methods individually and originally integrated as well, we therefore delimited the rockslide borders, computed volumes and highlighted non-uniform translational displacements along a wedge failure surface. Finally, we studied specific requirements and practical issues experimented on early warning systems of some of the most studied landslides worldwide. As a result, we highlighted valuable key recommendations to design new reliable systems; in addition, we also underlined conceptual issues that must be solved to improve current procedures. To sum up, the diversity of experimented situations brought an extensive experience that revealed the potential and limitations of both methods and highlighted as well the necessity of their complementary and integrated uses.

Large slope deformations detection and monitoring along shores of the Potrerillos dam reservoir, Argentina, based on a small-baseline InSAR approach

The Argentina National Road 7 that crosses the Andes Cordillera within the Mendoza province to connect Santiago de Chile and Buenos Aires is particularly affected by natural hazards requiring risk management. Integrated in a research plan that intends to produce landslide susceptibility maps, we aimed in this study to detect large slope movements by applying a satellite radar interferometric analysis using Envisat data, acquired between 2005 and 2010. We were finally able to identify two large slope deformations in sandstone and clay deposits along gentle shores of the Potrerillos dam reservoir, with cumulated displacements higher than 25mm in 5years and towards the reservoir. There is also a body of evidences that these large slope deformations are actually influenced by the seasonal reservoir level variations. This study shows that very detailed information, such as surface displacements and above all water level variation, can be extracted from spaceborne remote sensing techniques; nevertheless, the limitations of InSAR for the present dataset are discussed here. Such analysis can then lead to further field investigations to understand more precisely the destabilising processes acting on these slope deformations.