Regional-scale integration of multiresolution hydrological and geophysical data using a two-step Bayesian sequential simulation approach

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale for the purpose of improving predictions of groundwater flow and solute transport. However, extending corresponding approaches to the regional scale still represents one of the major challenges in the domain of hydrogeophysics. To address this problem, we have developed a regional-scale data integration methodology based on a two-step Bayesian sequential simulation approach. Our objective is to generate high-resolution stochastic realizations of the regional-scale hydraulic conductivity field in the common case where there exist spatially exhaustive but poorly resolved measurements of a related geophysical parameter, as well as highly resolved but spatially sparse collocated measurements of this geophysical parameter and the hydraulic conductivity. To integrate this multi-scale, multi-parameter database, we first link the low- and high-resolution geophysical data via a stochastic downscaling procedure. This is followed by relating the downscaled geophysical data to the high-resolution hydraulic conductivity distribution. After outlining the general methodology of the approach, we demonstrate its application to a realistic synthetic example where we consider as data high-resolution measurements of the hydraulic and electrical conductivities at a small number of borehole locations, as well as spatially exhaustive, low-resolution estimates of the electrical conductivity obtained from surface-based electrical resistivity tomography. The different stochastic realizations of the hydraulic conductivity field obtained using our procedure are validated by comparing their solute transport behaviour with that of the underlying "true” hydraulic conductivity field. We find that, even in the presence of strong subsurface heterogeneity, our proposed procedure allows for the generation of faithful representations of the regional-scale hydraulic conductivity structure and reliable predictions of solute transport over long, regional-scale distance

Review of soil salinity assessment for agriculture across multiple scales using proximal and/or remote sensors

Mapping and monitoring soil spatial variability is particularly problematic for temporally and spatially dynamic properties such as soil salinity. The tools necessary to address this classic problem only reached maturity within the past 2 decades to enable field- to regional-scale salinity assessment of the root zone, including GPS, GIS, geophysical techniques involving proximal and remote sensors, and a greater understanding of apparent soil electrical conductivity (ECa) and multi- and hyperspectral imagery. The concurrent development and application of these tools have made it possible to map soil salinity across multiple scales, which back in the 1980s was prohibitively expensive and impractical even at field scale. The combination of ECa-directed soil sampling and remote imagery has played a key role in mapping and monitoring soil salinity at large spatial extents with accuracy sufficient for applications ranging from field-scale site-specific management to statewide water allocation management to control salinity within irrigation districts. The objective of this paper is: (i) to present a review of the geophysical and remote imagery techniques used to assess soil salinity variability within the root zone from field to regional scales; (ii) to elucidate gaps in our knowledge and understanding of mapping soil salinity; and (iii) to synthesize existing knowledge to give new insight into the direction soil salinity mapping is heading to benefit policy makers, land resource managers, producers, agriculture consultants, extension specialists, and resource conservation field staff. The review covers the need and justification for mapping and monitoring salinity, basic concepts of soil salinity and its measurement, past geophysical and remote imagery research critical to salinity assessment, current approaches for mapping salinity at different scales, milestones in multi-scale salinity assessment, and future direction of field- to regional-scale salinity assessment

Faktoranalízisen alapuló új statisztikus eljárás a szivárgási tényező meghatározására

A Miskolci Egyetem Geofizikai Tanszékén kifejlesztett faktoranalízisen alapuló statisztikus eljárással korábban az agyagtartalmat határoztuk meg a fúrólyukszelvényekből. Folytatva az alkalmazási lehetőségeket, ebben a tanulmányban a víztárolók agyagtartalmával szorosan összefüggő mennyiséget a szivárgási tényezőt származtatjuk a faktorszelvényekből. Szintetikus modellkísérleten és terepi alkalmazásokon keresztül mutatjuk be a kiértékelési eljárást, mely mind elsődleges, mind másodlagos porozitású kőzetekben jól alkalmazható. A faktoranalízis eredményei megfelelő egyezőséget mutatnak a Kozeny-Carman modell alapján számított és a vízadó formációk hidraulikai tesztjeiből származó szivárgási tényezők értékeivel. A fúrólyukszelvények egyidejű statisztikai feldolgozásával folytonos in-situ információ nyerhető a szivárgási tényezőről a fúrólyuk teljes hossza mentén, mely 2-D faktoranalízis alkalmazása esetén kiterjeszthető a szomszédos fúrások közötti térrészre. A tanulmány célja egy új független szelvényértelmezési eljárás bemutatása, mely hatékonyan felhasználható a hidrogeofizikai kutatások során

Multivariate Analysis of Cross-Hole Georadar Velocity and Attenuation Tomograms for Aquifer Zonation

We have investigated the potential of combining cross-hole georadar velocity and attenuation tomography as a method for characterizing heterogeneous alluvial aquifers. A multivariate statistical technique, known as k-means cluster analysis, is used to correlate and integrate information contained in velocity and attenuation tomograms. Cluster analysis allows us to identify objectively the major common trends in the tomographic data and thus to ‘‘reduce’’ the information to a limited number of characteristic parameter combinations. The application of this procedure to two synthetic data sets indicates that it is a powerful tool for converting the complex relationships between the tomographically derived velocity and attenuation structures into a lithologically and hydrologically meaningful zonation of the probed region. In addition, these synthetic examples allow us to evaluate the reliability of further petrophysical parameter estimates. We find that although absolute values of the tomographically inferred petrophysical parameters often differ significantly from the actual parameters, the clustering approach enables us to reliably identify the major trends in the petrophysical properties. Finally, we have applied the approach to a cross-hole georadar data set collected in a well-studied alluvial aquifer. A comparison of the clustered tomographic section with well-log data demonstrates that our approach delineates the hydrostratigraphic zonation

Delineation of Groundwater Potential Using GIS, Hydrogeological, Geophysical and Analytical Hierarchy Process (AHP) Technique in Olorunda-Abaa, Ibadan, Southwest Nigeria

Integration Geographic Information System (GIS), hydrogeological, geoelectrical method involving Vertical Electrical Sounding (VES) technique, coupled with the Multi-Criteria Decision Analysis (MCDA) data mining technique were utilized with the aim to delineate the groundwater potential zones of Olorunda-Abaa area, Ibadan, southwestern Nigeria. Fifty-three (53) Vertical Electrical Sounding (VES) measurements using Schlumberger electrode array, depth to Water Level (DWL) estimation and determination of depth to the bottom of Hand dug wells were carried out across the study area. Multi-criteria Decision Analysis (MCDA) using Analytical Hierarchy Process (AHP) technique was applied to the factors controlling groundwater accumulation in the area. Weights were assigned and subsequently integrated in the ArcGIS environment using Arc Map 10.1 to develop the groundwater potential map of the investigated area. The geoelectric sections developed delineated four subsurface geological units consisting of the topsoil, weathered layer, partly weathered/fractured basement and the fresh bedrock. The VES gave depths to basement bedrock which generally range from 5.6 - 59.4 m. The groundwater conceptual model developed delineated five groundwater potential zones classified as low, medium, high and very high and validated with the thickness of water column obtained from wells over the entire study area. The groundwater potential map generated for the study area show that the medium to very high groundwater potential zones indicates the favourable area where groundwater development is feasible in the study area. This study concludes that the characterization of the groundwater potential zones in the study area can be adopted for future allocation of social amenities, planning, location, development and management of groundwater resources. Keywords: Hydrogeology, Geophysical, Multi-criteria Decision Analysis, Groundwater Potential, Basement Complex, Ibadan. DOI: 10.7176/JEES/11-5-08 Publication date:May 31st 202

Integration of hydrogeophysics and remote sensing with coupled hydrological models

Dissertation to obtain the degree of doctor at the University of Twente, on the authority of the rector magnificus, prof. dr. H. Brinksma, on account of the decision of the graduation committee, to be publicity defended on Friday, July 17, 2015 at 14:45 hr

Characterizing Petrophysical Properties of Carbonate Rocks Using Nuclear Magnetic Resonance and Complex Conductivity

Carbonate rocks are well known for their highly complex petrophysical behaviors due to their intrinsically heterogeneous pore geometry and wide range of pore sizes. As a result, both effective characterization of carbonate pore systems and the prediction of fluid transport in carbonate reservoirs, remains challenging. This thesis focuses on using nuclear magnetic resonance (NMR) and complex conductivity to quantify carbonate pore structure and gain insights into fluid flow and lithology of carbonate reservoir rocks at the core and log scales. In the laboratory study, integrated NMR and complex conductivity data are used to characterize porosity, pore size distribution, and surface area-to-pore volume ratio, in grainstones, packstones, and mudstones from carbonate reservoirs in Kansas. Carbonate samples with varying pore types and depositional texture are characterized according to NMR porosity, log-mean of transverse relaxation time (T2) value T2ML, real conductivity σ', and imaginary conductivity σ". Widely used petrophysical relationships derived from NMR and complex conductivity data also are assessed, and alternative relationships appropriate for carbonate samples at laboratory scale are proposed. Furthermore, to test the proposed petrophysical relationships at a larger spatial scale, and to exploit the potential of borehole NMR data, this study analyzes NMR well log data from Wellington, KS. This study focus on the uses of NMR longitudinal and transverse relaxation time ratio (T1/T2) in electrofacies characterization. Through multivariate analysis of a suite of logs (e.g., sonic slowness, photoelectric factor, etc.), the results show that T1/T2 ratio is uncorrelated with other logs which makes it a potentially independent indicator for rock typing. The data bear on the accuracy of predicted electrofacies using T1/T2 ratio, and how factors such as lithology and fluid could impact the T1/T2 ratio. Extending beyond experimental observations, this work assesses and proposes new electrical and NMR petrophysical models, analyzes the factors controlling the variation within NMR logging data, and harnesses the complete NMR logging information to improve carbonate reservoir characterization. This work demonstrates the potential of combining NMR and electrical methods to advance understandings of fluid distribution and fluid flow in complex carbonate reservoirs

Enhancing the information content of geophysical data for nuclear site characterisation

Our knowledge and understanding to the heterogeneous structure and processes occurring in the Earth’s subsurface is limited and uncertain. The above is true even for the upper 100m of the subsurface, yet many processes occur within it (e.g. migration of solutes, landslides, crop water uptake, etc.) are important to human activities. Geophysical methods such as electrical resistivity tomography (ERT) greatly improve our ability to observe the subsurface due to their higher sampling frequency (especially with autonomous time-lapse systems), larger spatial coverage and less invasive operation, in addition to being more cost-effective than traditional point-based sampling. However, the process of using geophysical data for inference is prone to uncertainty. There is a need to better understand the uncertainties embedded in geophysical data and how they translate themselves when they are subsequently used, for example, for hydrological or site management interpretations and decisions. This understanding is critical to maximize the extraction of information in geophysical data. To this end, in this thesis, I examine various aspects of uncertainty in ERT and develop new methods to better use geophysical data quantitatively. The core of the thesis is based on two literature reviews and three papers. In the first review, I provide a comprehensive overview of the use of geophysical data for nuclear site characterization, especially in the context of site clean-up and leak detection. In the second review, I survey the various sources of uncertainties in ERT studies and the existing work to better quantify or reduce them. I propose that the various steps in the general workflow of an ERT study can be viewed as a pipeline for information and uncertainty propagation and suggested some areas have been understudied. One of these areas is measurement errors. In paper 1, I compare various methods to estimate and model ERT measurement errors using two long-term ERT monitoring datasets. I also develop a new error model that considers the fact that each electrode is used to make multiple measurements. In paper 2, I discuss the development and implementation of a new method for geoelectrical leak detection. While existing methods rely on obtaining resistivity images through inversion of ERT data first, the approach described here estimates leak parameters directly from raw ERT data. This is achieved by constructing hydrological models from prior site information and couple it with an ERT forward model, and then update the leak (and other hydrological) parameters through data assimilation. The approach shows promising results and is applied to data from a controlled injection experiment in Yorkshire, UK. The approach complements ERT imaging and provides a new way to utilize ERT data to inform site characterisation. In addition to leak detection, ERT is also commonly used for monitoring soil moisture in the vadose zone, and increasingly so in a quantitative manner. Though both the petrophysical relationships (i.e., choices of appropriate model and parameterization) and the derived moisture content are known to be subject to uncertainty, they are commonly treated as exact and error‐free. In paper 3, I examine the impact of uncertain petrophysical relationships on the moisture content estimates derived from electrical geophysics. Data from a collection of core samples show that the variability in such relationships can be large, and they in turn can lead to high uncertainty in moisture content estimates, and they appear to be the dominating source of uncertainty in many cases. In the closing chapters, I discuss and synthesize the findings in the thesis within the larger context of enhancing the information content of geophysical data, and provide an outlook on further research in this topic

Three-Dimensional Stochastic Estimation of Porosity Distribution: Benefits of Using Ground-Penetrating Radar Velocity Tomograms in Simulated-Annealing-Based or Bayesian Sequential Simulation Approaches

Estimation of the three-dimensional (3-D) distribution of hydrologic properties and related uncertainty is a key for improved predictions of hydrologic processes in the subsurface. However it is difficult to gain high-quality and high-density hydrologic information from the subsurface. In this regard a promising strategy is to use high-resolution geophysical data (that are relatively sensitive to variations of a hydrologic parameter of interest) to supplement direct hydrologic information from measurements in wells (e.g., logs, vertical profiles) and then generate stochastic simulations of the distribution of the hydrologic property conditioned on the hydrologic and geophysical data. In this study we develop and apply this strategy for a 3-D field experiment in the heterogeneous aquifer at the Boise Hydrogeophysical Research Site and we evaluate how much benefit the geophysical data provide. We run high-resolution 3-D conditional simulations of porosity with both simulated-annealing-based and Bayesian sequential approaches using information from multiple intersecting crosshole gound-penetrating radar (GPR) velocity tomograms and neutron porosity logs. The benefit of using GPR data is assessed by investigating their ability, when included in conditional simulation, to predict porosity log data withheld from the simulation. Results show that the use of crosshole GPR data can significantly improve the estimation of porosity spatial distribution and reduce associated uncertainty compared to using only well log measurements for the estimation. The amount of benefit depends primarily on the strength of the petrophysical relation between the GPR and porosity data, the variability of this relation throughout the investigated site, and lateral structural continuity at the site