Sensitivity and resolution of tomographic pumping tests in an alluvial aquifer

This is the published version. Copyright American Geophysical Union[1] Various investigators have proposed hydraulic tomography, the simultaneous analysis of responses to multiple well tests, as a means to obtain a high-resolution characterization of aquifer flow properties. This study assesses the information content of drawdown records from a set of tomographic pumping tests in an alluvial aquifer, comparing the parameter sensitivity and resolution associated with transient and steady-shape formulations of the objective function for the parameter estimation problem. The steady-shape approach takes advantage of the rapid establishment of constant gradients within the region surrounding a pumping well, comparing observed drawdown differences within this region with drawdown differences predicted by a steady state model. Both the transient and steady-shape approaches resolve K variations only within a limited distance of the pumping intervals and observation points. Relative to the transient approach, the steady-shape approach reduces the influence of poorly resolved property variations, including K variations outside the region of investigation and storage coefficient variations throughout the model domain

First Development and Demonstration of Fiber Optic Bolometer

The fiber optic bolometer (FOB) was demonstrated observing a fusion plasma for the first time, and 2D fiber optic bolometer was developed and demonstrated to have high spatial resolution. The FOB is a novel type of a bolometer that is theoretically immune to EMI. A bolometer that is a sensor that measure the power of the incoming electromagnetic radiation. The most common bolometer used in fusion research is a resistive bolometer that utilize resistors in an electrical circuit. Due to high electromagnetic interferences (EMI) in fusion environment, noise can be a serious problem in determining accurate plasma radiation. The demonstration at DIII-D tokamak utilized a single-channel system having a measurement FOB and a reference FOB, which was blocked of incoming radiation. The demonstration showed negligible increase in noise in fusion environment and acceptable absolute-value comparisons with the resistive bolometers. Plasma radiations contain information relating to plasma phenomena, and the structures are unique depending on plasma conditions. 2D FOB array was designed to investigate plasma radiations near the divertor with higher resolutions more rigorously for DIII-D. The design parameters were optimized using the machine learning technique called Bayesian global optimization, which was efficient for the multivariate non-linear problem. A physics-based regularization was developed using a magnetic reconstruction profile for the DIII-D implementation with an iterative inversion method. Neural network inversion methods were developed to not depend on an arbitrary regularization strength and to do between-plasma-shot inversions, which could not overcome the problem of biasing on input data A new method of raw spectra data processing that used Fourier transform was developed for real time analysis. The design from the optimization was validated with several analysis methods to characterize the performance. The forward-modelled radiated power divided into different sections compared to the values from the original synthetic radiation profiles. The central location and shape of various radiation profiles were analyzed and compared to the original values using a computer vision library. The regularized iterative methods worked well. The results demonstrated that the optimized 2D FOB array system will be able to answer important questions relating plasma radiation structures

Hydrological Parameter Estimations from a Conservative Tracer Test with Variable-Density Effects at the Boise Hydrogeophysical Research Site

Reliable predictions of groundwater flow and solute transport require an estimation of the detailed distribution of the parameters (e.g., hydraulic conductivity, effective porosity) controlling these processes. However, such parameters are difficult to estimate because of the inaccessibility and complexity of the subsurface. In this regard, developments in parameter estimation techniques and investigations of field experiments are still challenging and necessary to improve our understanding and the prediction of hydrological processes. Here we analyze a conservative tracer test conducted at the Boise Hydrogeophysical Research Site in 2001 in a heterogeneous unconfined fluvial aquifer. Some relevant characteristics of this test include: variable-density (sinking) effects because of the injection concentration of the bromide tracer, the relatively small size of the experiment, and the availability of various sources of geophysical and hydrological information. The information contained in this experiment is evaluated through several parameter estimation approaches, including a grid-search-based strategy, stochastic simulation of hydrological property distributions, and deterministic inversion using regularization and pilot-point techniques. Doing this allows us to investigate hydraulic conductivity and effective porosity distributions and to compare the effects of assumptions from several methods and parameterizations. Our results provide new insights into the understanding of variable-density transport processes and the hydrological relevance of incorporating various sources of information in parameter estimation approaches. Among others, the variable-density effect and the effective porosity distribution, as well as their coupling with the hydraulic conductivity structure, are seen to be significant in the transport process. The results also show that assumed prior information can strongly influence the estimated distributions of hydrological properties

Imaging of a fluid injection process using geophysical data - A didactic example

In many subsurface industrial applications, fluids are injected into or withdrawn from a geologic formation. It is of practical interest to quantify precisely where, when, and by how much the injected fluid alters the state of the subsurface. Routine geophysical monitoring of such processes attempts to image the way that geophysical properties, such as seismic velocities or electrical conductivity, change through time and space and to then make qualitative inferences as to where the injected fluid has migrated. The more rigorous formulation of the time-lapse geophysical inverse problem forecasts how the subsurface evolves during the course of a fluid-injection application. Using time-lapse geophysical signals as the data to be matched, the model unknowns to be estimated are the multiphysics forward-modeling parameters controlling the fluid-injection process. Properly reproducing the geophysical signature of the flow process, subsequent simulations can predict the fluid migration and alteration in the subsurface. The dynamic nature of fluid-injection processes renders imaging problems more complex than conventional geophysical imaging for static targets. This work intents to clarify the related hydrogeophysical parameter estimation concepts

The emergence of hydrogeophysics for improved understanding of subsurface processes over multiple scales

Geophysics provides a multi-dimensional suite of investigative methods that are transforming our ability to see into the very fabric of the subsurface environment, and monitor the dynamics of its fluids and the biogeochemical reactions that occur within it. Here, we document how geophysical methods have emerged as valuable tools for investigating shallow subsurface processes over the past two decades and offer a vision for future developments relevant to hydrology and also ecosystem science. The field of “hydrogeophysics” arose in the late 1990s, prompted, in part, by the wealth of studies on stochastic subsurface hydrology that argued for better field-based investigative techniques. These new hydrogeophysical approaches benefited from the emergence of practical and robust data inversion techniques, in many cases with a view to quantify shallow subsurface heterogeneity and the associated dynamics of subsurface fluids. Furthermore, the need for quantitative characterization stimulated a wealth of new investigations into petrophysical relationships that link hydrologically relevant properties to measurable geophysical parameters. Development of time-lapse approaches provided a new suite of tools for hydrological investigation, enhanced further with the realization that some geophysical properties may be sensitive to biogeochemical transformations in the subsurface environment, thus opening up the new field of “biogeophysics”. Early hydrogeophysical studies often concentrated on relatively small ‘plot-scale’ experiments. More recently, however, the translation to larger-scale characterization has been the focus of a number of studies. Geophysical technologies continue to develop, driven, in part, by the increasing need to understand and quantify key processes controlling sustainable water resources and ecosystem services

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