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Register a new userTidal response of groundwater in a leaky aquifer: application to Oklahoma
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Quantitative interpretation of the tidal response of water levels measured in wells has long been made either with a model for perfectly confined aquifers or with a model for purely unconfined aquifers. However, many aquifers may be neither totally confined nor purely unconfined at the frequencies of tidal loading but behave somewhere between the two end members. Here we present a more general model for the tidal response of groundwater in aquifers with both horizontal and vertical flow. The model has three independent parameters: the transmissivity and storativity of the aquifer and the specific leakage of the leaking aquitard. If transmissivity and storativity are known independently, this model may be used to estimate aquitard leakage from the phase shift and amplitude ratio of water level in wells obtained from tidal analysis. We apply the model to interpret the tidal response of water level in a USGS deep monitoring well installed in the Arbuckle aquifer in Oklahoma, into which massive amount of wastewater co-produced from hydrocarbon exploration has been injected. The analysis shows that the Arbuckle aquifer is leaking significantly at this site. We suggest that the present method may be effectively and economically applied to monitor leakage in groundwater systems, which bears on the safety of water resources, the security of underground waste repositories, and the outflow of wastewater during deep injection and hydrocarbon extraction.
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The quest for a better understanding of the cancer risk associated with drinking the radium-contaminated groundwater of the Disi Aquifer in Jordan has become more urgent in recent years. To quantitively identify the health consequences attainable from the consumption of this groundwater source, internal dosimetry analysis was performed with emphasis on doses deliverable to bone surfaces. Moreover, the age-dependent dose calculations performed in this study show that the most critical group is those who are below the age of 15, where we predict an increase in the risk of cancer by up to a factor of 5 as compared to adults. It is also demonstrated that radium radioactivity remains relatively constant in the bone even 10 years after ingestion. The whole-body dose analysis concluded that it is a factor of 5 higher than what the WHO recommends as a limit.
Characterizing the properties of groundwater aquifers is essential for predicting aquifer response and managing groundwater resources. In this work, we develop a high-dimensional scalable Bayesian inversion framework governed by a three-dimensional quasi-static linear poroelastic model to characterize lateral permeability variations in groundwater aquifers. We determine the maximum a posteriori (MAP) point of the posterior permeability distribution from centimeter-level surface deformation measurements obtained from Interferometric Synthetic Aperture Radar (InSAR). The scalability of our method to high parameter dimension is achieved through the use of adjoint-based derivatives, inexact Newton methods to determine the MAP point, and a Matern class sparse prior precision operator. Together, these guarantee that the MAP point is found at a cost, measured in number of forward/adjoint poroelasticity solves, that is independent of the parameter dimension. We apply our methodology to a test case for a municipal well in Mesquite, Nevada, in which InSAR and GPS surface deformation data are available. We solve problems with up to 320,824 state variable degrees of freedom (DOFs) and 16,896 parameter DOFs. A consistent treatment of noise level is employed so that the aquifer characterization result does not depend on the pixel spacing of surface deformation data. Our results show that the use of InSAR data significantly improves characterization of lateral aquifer heterogeneity, and the InSAR-based aquifer characterization recovers complex lateral displacement trends observed by independent daily GPS measurements.
The hypocentral depth is a key requirement in seismology and earthquake engineering, but it is very difficult to be determined. The current accepted improvement is taking advantage of the depth phases, such as the pP, to constrain this parameter. However, it is not easy to pick such a phase in a seismogram from the other phases and the backgound noises. Here we propose the use of the minimum entropy deconvolution (MED) to detect it. Synthetic tests show that impulse(s) hidden in the seimic noises, eg. discrete unit impulses or the Gaussian mono impulses, can be detected completely. Further, we assume that the pP phase is an impulse-like signal buried in the Z component of the seismogram and applied this technique to 12 earthquakes in the International Association of Seismology and Physics (IASPEI) Ground Truth (GT) reference events list. Results show that 9 out of 12 earthquakes have absolute errors of less than 2.00 s for the travel-time differences of pP-P, and the maximum absolute error is 3.06 s . This demonstrate that the assumption above is reasonable, and this technique works well and effectively even for a single seismogram. Due to its little cost and effectiveness, this technique may be also useful in the starting points for other methods to detect pP phase.
Uncertainty quantification of groundwater (GW) aquifer parameters is critical for efficient management and sustainable extraction of GW resources. These uncertainties are introduced by the data, model, and prior information on the parameters. Here we develop a Bayesian inversion framework that uses Interferometric Synthetic Aperture Radar (InSAR) surface deformation data to infer the laterally heterogeneous permeability of a transient linear poroelastic model of a confined GW aquifer. The Bayesian solution of this inverse problem takes the form of a posterior probability density of the permeability. Exploring this posterior using classical Markov chain Monte Carlo (MCMC) methods is computationally prohibitive due to the large dimension of the discretized permeability field and the expense of solving the poroelastic forward problem. However, in many partial differential equation (PDE)-based Bayesian inversion problems, the data are only informative in a few directions in parameter space. For the poroelasticity problem, we prove this property theoretically for a one-dimensional problem and demonstrate it numerically for a three-dimensional aquifer model. We design a generalized preconditioned Crank--Nicolson (gpCN) MCMC method that exploits this intrinsic low dimensionality by using a low-rank based Laplace approximation of the posterior as a proposal, which we build scalably. The feasibility of our approach is demonstrated through a real GW aquifer test in Nevada. The inherently two dimensional nature of InSAR surface deformation data informs a sufficient number of modes of the permeability field to allow detection of major structures within the aquifer, significantly reducing the uncertainty in the pressure and the displacement quantities of interest.
We study the response of star clusters to individual tidal perturbations using controlled $N$-body simulations. We consider perturbations by a moving point mass and by a disc, and vary the duration of the perturbation as well as the cluster density profile. For fast perturbations (i.e. `shocks), the cluster gains energy in agreement with theoretical predictions in the impulsive limit. For slow disc perturbations, the energy gain is lower, and this has previously been attributed to adiabatic damping. However, the energy gain due to slow perturbations by a point-mass is similar to that due to fast shocks, which is not expected because adiabatic damping should be almost independent of the nature of the tides. We show that the geometric distortion of the cluster during slow perturbations is of comparable importance for the energy gain as adiabatic damping, and that the combined effect can qualitatively explain the results. The half-mass radius of the bound stars after a shock increases up to $sim$7% for low-concentration clusters, and decreases $sim$3% for the most concentrated ones. The fractional mass loss is a non-linear function of the energy gain, and depends on the nature of the tides and most strongly on the cluster density profile, making semi-analytic model predictions for cluster lifetimes extremely sensitive to the adopted density profile.
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