EBG waveguides for contactless surface impedance measurements

A method for the estimation of sheet impedance of thin sample which does not require a direct contact with the sample under test is proposed. The surface impedance is calculated through an inversion procedure exploiting the scattering parameters obtained through a waveguide measurement setup. An inversion procedure based on the representation of the waveguide-air-waveguide section as a π junction is employed. In order to prevent the field leakage from the air gap created for hosting the thin sheet, an EBG surface is introduced on the flange of the waveguide. It is shown that the introduction of the EBG surface remarkably improves the estimation of the surface impedance of the thin sheet with respect to the case without EBG.

Tailoring Nanocomposite Membranes of Cellulose of Acetate/Silica Nanoparticles for Desalination

Herein, fabrication of cellulose acetate (CA) silica-based nanocomposite membranes via the dry-wet phase inversion procedure for the water desalination was investigated. The modified and unmodified silica nanoparticles (SNPs and MSNPs) were prepared by the sol-gel technique. The addition effect of the SNPs and MSNPs was investigated on the CA membranes properties and their performance for water separation. The CA nanocomposite membranes were characterized to study their structure, hydrophilicity, and morphology. The fabricated nanocomposite membranes showed hydrophilic surface properties. The performance of reverse osmosis (RO) membranes was measured using a crossflow RO unit. At 10 bar, The membrane with 10 mg of SNPs showed enhanced permeate water flux compared to the pristine CA membrane by 1.6 L/m 2 .hr. Increasing the SNPs in the nanocomposite membrane showed enhancement in the permeate water flux all over the operating pressure. The effect of MSNPs on the nanocomposites’ performance was lower than their counterpart in the case of adding SNPs. The membrane with 30 mg of MSNPs showed the highest permeate water flux among other nanocomposite membranes with a value of 35.7 L/m 2 .hr at 24 bar.

Evaluation of induced polarization measurements using a new inversion method

In this paper, a new inversion method is proposed to process laboratory-measured induced polarization (IP) data. In the new procedure, the concept of the series expansion-based inversion is combined with a more general definition of the objective function. The time constant spectrum of the IP effect is assumed a line spectrum approximated by a series of Dirac’s delta function resulting in a square-integrable forward problem formula. This gives the applicability of the generalized objective function. The expansion coefficients as unknowns represent the model parameters of the inversion procedure. We use the new inversion procedure on an apparent polarizability dataset measured on a rock sample originated from the Recsk ore complex, northeast Hungary. The inversion results was compared to those of three additional laboratory datasets, which were measured on samples rich in ore minerals collected from the same area. The results are compared to those given by the traditional series expansion-based least squares method. It is shown that the newly proposed method gives more accurate and stable parameter estimation.

Full-Wave Modeling and Inversion of UWB Radar Data for Wave Propagation in Cylindrical Objects

The nondestructive characterization of cylindrical objects is needed in many fields, such as medical diagnostics, tree trunk inspection, or concrete column testing. In this study, the radar equation of Lambot et al. is combined with cylindrical Green’s functions to fully model and invert ultra-wideband (UWB) ground-penetrating radar (GPR) data and retrieve the properties of cylindrical objects. Inversion is carried out using a lookup table (LUT) approach followed by local optimization to ensure retrieval of the global minimum of the objective function. Numerical experiments were conducted to analyze the capabilities of the developed inversion procedure to estimate the radius, permittivity, and conductivity of the cylinders. The full-wave model was validated in laboratory conditions on metallic and plastic pipes of different sizes. The adopted radar system consists of a lightweight vector network analyzer (VNA) connected to a single transmitting and receiving horn antenna. The numerical experiments highlighted the complexity of the inverse problem, mainly originating from the multiple propagation modes within cylindrical objects. The laboratory measurements demonstrated the accuracy of the forward modeling and reconstructions in far-field conditions.

CHARACTERIZING CEMENT BOND QUALITY USING SLIP-INTERFACE THEORY AND COUPLING STIFFNESS

Cased-hole acoustic-wave modeling using the slip-interface theory is applied to cement bond evaluation, allowing for characterizing various bonding issues caused by poor bonding, lack of a cement, interface roughness and irregularity, micro-annulus, etc. The new theory models the interface between casing and cement (or cement and formation) as a slip boundary governed by normal and tangential coupling stiffness parameters. With the new theory and the stiffness parameters, we can model various wave phenomena for bond quality variation between the free-pipe and well bonded conditions. The modeling shows that wave amplitude variation is primarily controlled by the tangential (or shear) coupling stiffness, providing the theoretical foundation for developing an inversion procedure to estimate this parameter from field acoustic logging data. In the inversion procedure, the maximum stiffness value is first determined by matching the modeled and measured waveform data for the well bonded condition. Using the stiffness value as a reference, the stiffness values for the borehole section of interest are inverted by minimizing the modeled and measured waveform data, resulting in a continuous coupling stiffness curve to characterize the cement bond quality of the borehole section of interest. Because the stiffness parameter is directly related to the cement bond strength, the new stiffness-based method is advantageous over the existing wave-amplitude-based method and can thus better characterize and quantify the cement bond quality.

Conditional stochastic inversion of common-offset GPR reflection data

We present a stochastic inversion procedure for common-offset ground-penetrating radar (GPR) reflection measurements. Stochastic realizations of subsurface properties that offer an acceptable fit to GPR data are generated via simulated annealing optimization. The realizations are conditioned to borehole porosity measurements available along the GPR profile, or equivalent measurements of another petrophysical property that can be related to the dielectric permittivity, as well as to geostatistical parameters derived from the borehole logs and the processed GPR image. Validation of our inversion procedure is performed on a pertinent synthetic data set and indicates that the proposed method is capable of reliably recovering strongly heterogeneous porosity structures associated with surficial alluvial aquifers. This finding is largely corroborated through application of the methodology to field measurements from the Boise Hydrogeophysical Research Site near Boise, Idaho, USA.

Implementation of Machine Learning Algorithms in Spectral Analysis of Surface Waves (SASW) Inversion

One of the complex processes in spectral analysis of surface waves (SASW) data analysis is the inversion procedure. An initial soil profile needs to be assumed at the beginning of the inversion analysis, which involves calculating the theoretical dispersion curve. If the assumption of the starting soil profile model is not reasonably close, the iteration process might lead to nonconvergence or take too long to be converged. Automating the inversion procedure will allow us to evaluate the soil stiffness properties conveniently and rapidly by means of the SASW method. Multilayer perceptron (MLP), random forest (RF), support vector regression (SVR), and linear regression (LR) algorithms were implemented in order to automate the inversion. For this purpose, the dispersion curves obtained from 50 field tests were used as input data for all of the algorithms. The results illustrated that SVR algorithms could potentially be used to estimate the shear wave velocity of soil.

HypoSVI: Hypocenter inversion with Stein Variational Inference and Physics Informed Neural Networks

<p>High resolution earthquake hypocentral locations are of critical importance for understanding the regional context driving seismicity. We introduce a scheme to reliably approximate a hypocenter posterior in a continuous domain that relies on recent advances in deep learning.</p><p>Our method relies on a differentiable forward model in the form of a deep neural network, which is trained to solve the Eikonal equation (EikoNet). EikoNet can rapidly determine the travel-time between any source-receiver pair for a non-gridded solution. We demonstrate the robustness of these travel-time solutions are for a series of complex velocity models.</p><p>For the inverse problem, we utilize Stein Variational Inference, which is a recent approximate inference procedure that iteratively updates a configuration of particles to approximate a target posterior by minimizing the so-called Stein discrepancy. The gradients of this objective function can be rapidly calculated due to the differentiability of the EikoNet. The particle locations are updated until convergence, after which we utilize clustering techniques and kernel density methods to determine the optimal hypocenter and its uncertainty.</p><p>The inversion procedure outlined in this work is validated using a series of synthetic tests to determine the parameter optimisation and the validity for large observational datasets, which can locate earthquakes in 439s per event for 2039 observations. In addition, we apply this technique to a case study of seismicity in the Southern California region for earthquakes from 2019.</p>