Search

Quantifying historical research trends in the field of hydrogeology is not only generally informative for hydrogeologists but is essential for fostering interdisciplinary collaboration and assessing the relationship between academic study and societal interests in hydrogeologic issues. To address this, a topic model was applied to over 37,000 academic abstracts published in over 20 journals between 1963–2022 in the field of hydrogeology to study the evolution of topic trends through time. Model results were fed into the popular large-language model ChatGPT to assign topic names, representing an unsupervised method. The results indicate that, historically, popular topics related to methodological development and analytical and numerical models analytical and numerical methods in groundwater flow modeling and well hydraulics have given way to topics related to more increasingly complex models (groundwater monitoring and uncertainty estimation and groundwater modeling calibration and simulation) as data and computational capability becomes increasingly available. An insight into the period of boom-and-bust in contaminant hydrogeology is reflected by a shift in focus from topics related to assessment and characterization of contaminant sources toward topics related to degradation and remediation methods. Topics of emerging prevalence (sustainable groundwater resource management, catchment hydrology and runoff processes) in the current period reflect an increasing focus on treating the surface-water/groundwater system as a single system. In addition, results suggest that topic distribution within the field of hydrogeology has become more varied as time has progressed. [ABSTRACT FROM AUTHOR]

Graph theory is useful for analyzing time-dependent model parameters estimated from interferometric synthetic aperture radar (InSAR) data in the temporal domain. Plotting acquisition dates (epochs) as vertices and pair-wise interferometric combinations as edges defines an incidence graph. The edge-vertex incidence matrix and the normalized edge Laplacian matrix are factors in the covariance matrix for the pair-wise data. Using empirical measures of residual scatter in the pair-wise observations, we estimate the relative variance at each epoch by inverting the covariance of the pair-wise data. We evaluate the rank deficiency of the corresponding least-squares problem via the edge-vertex incidence matrix. We implement our method in a MATLAB software package called GraphTreeTA available on GitHub (). We apply temporal adjustment to the data set described in Lu et al. (Geophys Res Solid Earth 110, 2005) at Okmok volcano, Alaska, which erupted most recently in 1997 and 2008. The data set contains 44 differential volumetric changes and uncertainties estimated from interferograms between 1997 and 2004. Estimates show that approximately half of the magma volume lost during the 1997 eruption was recovered by the summer of 2003. Between June 2002 and September 2003, the estimated rate of volumetric increase is $$(6.2 \, \pm \, 0.6) \times 10^6~\mathrm{m}^3/\mathrm{year} $$ . Our preferred model provides a reasonable fit that is compatible with viscoelastic relaxation in the five years following the 1997 eruption. Although we demonstrate the approach using volumetric rates of change, our formulation in terms of incidence graphs applies to any quantity derived from pair-wise differences, such as range change, range gradient, or atmospheric delay. [ABSTRACT FROM AUTHOR]

While geophysicists recognize that all measurements of media properties are subject to noise, methods for fitting petrophysical relations commonly employ regression-based formulas, which assume no error in one of the properties being related (the 'independent" variable). To derive a more rigorous method for fitting such relations, we take a probabilistic viewpoint on the problem of fitting petrophysical relations to sample correlation data. Unlike prior approaches, we take into account the fact that noise is present in both measured properties. Under basic assumptions, we derive a new objective function for such problems which is proportional to the data likelihood and which can be used for both model parameter optimization and model selection. We present several numerical experiments outlining the utility of our method, and compare results of our method against results of other commonly used methods, such as kriging, regression, and total distance minimization. The results of our applications using the maximum likelihood technique appear visually accurate, and we also provide quantitative comparisons of performance that suggest the method produces more desirable results. [ABSTRACT FROM AUTHOR]

Popular parameter estimation methods, including least squares, maximum likelihood, and maximum a posteriori (MAP), solve an optimization problem to obtain a central value (or best estimate) followed by an approximate evaluation of the spread (or covariance matrix). A different approach is the Monte Carlo (MC) method, and particularly Markov chain Monte Carlo (MCMC) methods, which allow sampling from the posterior distribution of the parameters. Though available for years, MC methods have only recently drawn wide attention as practical ways for solving challenging high-dimensional parameter estimation problems. They have a broader scope of applications than conventional methods and can be used to derive the full posterior pdf but can be computationally very intensive. This paper compares a number of different methods and presents improvements using as case study a nonlinear DNAPL source dissolution and solute transport model. This depth-integrated semi-analytical model approximates dissolution from the DNAPL source zone using nonlinear empirical equations with partially known parameters. It then calculates the DNAPL plume concentration in the aquifer by solving the advection-dispersion equation with a flux boundary. The comparison is among the classical MAP and some versions of computer-intensive Monte Carlo methods, including the Metropolis-Hastings (MH) method and the adaptive direction sampling (ADS) method. [ABSTRACT FROM AUTHOR]

An essay is presented on the research conducted for analyzing the factors behind the depletion of groundwater. It discusses initiatives taken by the U.S. National Science Foundation (NSF) for analyzing the groundwater resources. It further informs about various factors that should be considered while researching on groundwater resources including sum of uncontaminated groundwater, techniques required for model calibration and role of water economics in the development of basin.