Geostatistical inversion of seismic and GPR reflection images: what can we actually resolve?

Estimation of the spatial statistics of subsurface velocity heterogeneity from surface-based geophysical reflection survey data is a problem of significant interest in seismic and ground-penetrating radar (GPR) research. A method to effectively address this problem has been recently presented, but our knowledge regarding the resolution of the estimated parameters is still inadequate. Here we examine this issue using an analytical approach that is based on the realistic assumption that the subsurface velocity structure can be characterized as a band-limited scale-invariant medium. Our work importantly confirms recent numerical findings that the inversion of seismic or GPR reflection data for the geostatistical properties of the probed subsurface region is sensitive to the aspect ratio of the velocity heterogeneity and to the decay of its power spectrum, but not to the individual values of the horizontal and vertical correlation lengths.

Caracterização da diversidade genética em feijão por meio de marcadores RAPD

Marcadores RAPD (Random Amplified Polymorphic DNA) foram usados para avaliar a diversidade genética entre 19 cultivares de feijão (Phaseolus vulgaris L.). Dos cento e oito locos de RAPD obtidos de 15 primers decâmeros, 70 foram polimórficos. Para estimar a distância genética foi usado o coeficiente de similaridade de Jaccard e as análises de agrupamento foram feitas pelos métodos UPGMA e Tocher. As análises de agrupamento confirmaram a ampla diversidade genética existente entre germoplasmas tropicais de feijão, separando as cultivares em dois grupos principais, correspondendo aos centros de domesticação Andino (genótipos de sementes médias e grandes) e Mesoamericano (genótipos de sementes pequenas). No grupo Andino, a diversidade genética relativa foi maior do que no Mesoamericano.

Radarsat backscattering from an agricultural scene

Orbital remote sensing in the microwave electromagnetic region has been presented as an important tool for agriculture monitoring. The satellite systems in operation have almost all-weather capability and high spatial resolution, which are features appropriated for agriculture. However, for full exploration of these data, an understanding of the relationships between the characteristics of each system and agricultural targets is necessary. This paper describes the behavior of backscattering coefficient (sigma°) derived from calibrated data of Radarsat images from an agricultural area. It is shown that in a dispersion diagram of sigma° there are three main regions in which most of the fields can be classified. The first one is characterized by low backscattering values, with pastures and bare soils; the second one has intermediate backscattering coefficients and comprises well grown crops mainly; and a third one, with high backscattering coefficients, in which there are fields with strong structures causing a kind of double bounce effect. The results of this research indicate that the use of Radarsat images is optimized when a multitemporal analysis is done making the best use of the agricultural calendar and of the dynamics of different cultures.

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.

Nodulação em cultivares de feijão dos conjuntos gênicos andino e meso-americano

O objetivo deste trabalho foi determinar a capacidade de nodulação e a especificidade de feijão (Phaseolus vulgaris L.) dos conjuntos gênicos andino e meso-americano submetidos a inóculo de Rhizobium. O experimento foi estabelecido em parcelas subdivididas em blocos ao acaso, com quatorze cultivares de feijão e três estirpes de Rhizobium (R. etli KIM 5, R. etli CIAT 632 e R. tropici CIAT 899). Somente as cultivares andinas WAF 15, WAF 7, Mineiro Precoce, WAF 6 e Antioquia 8 apresentaram especificidade na nodulação. Em relação à massa seca dos nódulos, houve diferenças significativas dos tratamentos de inoculação nas cultivares andinas WAF 15, WAF 7, WAF 6 e Diacol Andino, e na cultivar meso-americana Ouro Negro. Nenhuma das cultivares restringiu a nodulação, embora tenham sido verificadas diferenças de até 53 e 103 vezes no número e massa de nódulos por planta, respectivamente. Considerando todas as cultivares e estirpes de rizóbio, WAF 15 foi a cultivar com melhor desempenho em número e massa nodular. WAF 6 foi a cultivar de pior desempenho, chegando quase ao nível de restrição da nodulação com a estirpe R. etli CIAT 632.

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.

A la recerca del temple d’August a Tarragona: una experiència entre arqueologia i geofísica

Aplicació de diferents tècniques de prospecció geofísica aplicades al subsòl de la Catedral de Tarragona: tomografia de resistivitat elèctrica (ERT), cartografia de conductivitat (EM) i radar de subsòl (GPR).

Dynamic Speed Feedback Signs, 2014

Dynamic speed feedback sign (DSFS) systems are traffic control devices that are programmed to provide a message to drivers exceeding a certain speed thresh¬old. A DSFS system typically consists of a speed-measuring device, which may be loop detectors or radar, and a message sign that displays feedback to drivers who exceed a predetermined speed threshold. The feedback may be the driver’s actual speed, a message like “SLOW DOWN,” or activation of a warning device such as beacons or a curve warning sign. For more on this topic by these authors, see also "Evaluation of Dynamic Speed Feedback Signs on Curves: A National Demonstration Project": http://www.trb.org/main/blurbs/172092.aspx

Development of In Situ Detection Methods for Materials-Related Distress (MRD) in Concrete Pavements: Phase 2, HR-1081, 2005

This project utilized information from ground penetrating radar (GPR) and visual inspection via the pavement profile scanner (PPS) in proof-of-concept trials. GPR tests were carried out on a variety of portland cement concrete pavements and laboratory concrete specimens. Results indicated that the higher frequency GPR antennas were capable of detecting subsurface distress in two of the three pavement sites investigated. However, the GPR systems failed to detect distress in one pavement site that exhibited extensive cracking. Laboratory experiments indicated that moisture conditions in the cracked pavement probably explain the failure. Accurate surveys need to account for moisture in the pavement slab. Importantly, however, once the pavement site exhibits severe surface cracking, there is little need for GPR, which is primarily used to detect distress that is not observed visually. Two visual inspections were also conducted for this study by personnel from Mandli Communications, Inc., and the Iowa Department of Transportation (DOT). The surveys were conducted using an Iowa DOT video log van that Mandli had fitted with additional equipment. The first survey was an extended demonstration of the PPS system. The second survey utilized the PPS with a downward imaging system that provided high-resolution pavement images. Experimental difficulties occurred during both studies; however, enough information was extracted to consider both surveys successful in identifying pavement surface distress. The results obtained from both GPR testing and visual inspections were helpful in identifying sites that exhibited materials-related distress, and both were considered to have passed the proof-of-concept trials. However, neither method can currently diagnose materials-related distress. Both techniques only detected the symptoms of materials-related distress; the actual diagnosis still relied on coring and subsequent petrographic examination. Both technologies are currently in rapid development, and the limitations may be overcome as the technologies advance and mature.

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.

Estudo da dinâmica espaço-temporal do bioma Pantanal por meio de imagens MODIS

O objetivo deste trabalho foi avaliar dados multitemporais, obtidos pelo sensor "moderate resolution imaging spectroradiometer" (MODIS), para o estudo da dinâmica espaço-temporal de duas sub-regiões do bioma Pantanal. Foram utilizadas 139 imagens "enhanced vegetation index" (EVI), do produto MOD13 "vegetation index", dados de altimetria oriundos do "shuttle radar topography mission" (SRTM) e dados de precipitação do "tropical rainfall measuring mission" (TRMM). Para a redução da dimensionalidade dos dados, as imagens MODIS-EVI foram amostradas com base nas curvas de nível espaçadas em 10 m. Foram aplicadas as técnicas de análise de autocorrelação e análise de agrupamentos aos dados das amostras, e a análise de componentes principais na área total da imagem. Houve dependência tanto temporal quanto espacial da resposta espectral com a precipitação. A análise de agrupamentos apontou a presença de dois grupos, o que indicou a necessidade da análise completa da área. A análise de componentes principais permitiu diferenciar quatro comportamentos distintos: as áreas permanentemente alagadas; as áreas não inundáveis, compostas por vegetação; as áreas inundáveis com maior resposta de vegetação; e áreas com vegetação ripária.