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Ground Penetrating Radar (GPR) systems with multi-concurrent sampling receivers can rapidly acquire dense multi-offset GPR data, which is not feasible using typical common offset (CO) GPR systems with a single, fixed offset transmitter-receiver pair. Multi-offset GPR data from these new multi-concurrent receiver systems have the potential to be used to create detailed subsurface velocity models and enhanced reflection sections. These are important features that can improve qualitative and quantitative interpretation of GPR data. In order to realize these benefits and to deal with the large amount of multi-offset data generated by these new systems, we have developed an automated and customized data processing workflow. There are three key algorithms that we have developed as part of our workflow, which are crucial for processing large volume, multi-offset GPR data so as: firstly, to efficiently correct and manage time misalignments from multi-concurrent receivers; secondly, to carry out trace balancing of common mid-point (CMP) data for semblance analysis; and thirdly, to automate the velocity analysis step. We showcase our processing workflow using two field datasets acquired using a multi-concurrent sampling receiver GPR system consisting of one transmitter and seven receivers. The field data were collected at two different locations: a site using a system with a 500 MHz center frequency and another site using a system with a 1000 MHz center frequency. We demonstrated, with both datasets, that our processing workflow could produce automated stacking velocity fields and enhanced zero-offset reflection cross-sections. These benefits increase the information that can be used for interpretation (compared to conventional CO data) and can form the basis of further processing steps such as migration. As the cost of these multi-concurrent sampling receiver systems decreases over time, we anticipate their use, and the acquisition of dense multi-offset GPR data, to become much more commonplace.

Ground-penetrating radar (GPR) is becoming a mainstream tool in planetary exploration, and one of the few in-situ planetary geophysical methods. There are currently three missions (Perseverance, Tianwen-1, Chang'E-4) with GPR-equipped rovers, and two future missions (Chang'E-7, ExoMars) that will include GPR in their scientific payload. The large number of GPR data, combined with the novel setup of the measurements, creates the need for new data processing and interpretation techniques to address the unique challenges of in-situ planetary radar. The current paper proposes an interpretation pipeline that starts with a novel stochastic hyperbola fitting that estimates the probability kernel density of the bulk permittivity at different depths. Subsequently, the bulk permittivity distribution is transformed via a novel probabilistic inversion to a 1-dimensional (1D) permittivity profile. The inverted 1D permittivity profile is then used as an input to a bespoke reverse-time migration (RTM) using the finite-difference time-domain (FDTD) method. RTM using FDTD does not assume a clinical homogeneous half-space; instead, it accounts for the expected layered structure of the investigated medium. Lastly, the migrated radargram is clustered in order to identify subsurface targets and distinguish them from the background medium. Each of the processing steps has never been reported in planetary radar; and together act as a complete processing toolbox tuned for planetary science. The suggested interpretation pipeline is validated numerically in a 1D case study with a complex layered structure and multiple subsurface targets. The proposed processing scheme is then applied to the GPR data from the Chang'E-4 mission at the Von Karman crater, revealing a previously unseen layered structure and a complex distribution of rocks/boulders.

Hyperbola fitting is a mainstream interpretation technique used in ground-penetrating radar (GPR) due to its simplicity and relatively low computational requirements. Conventional hyperbola fitting is based on the assumption that the investigated medium is a homogeneous half-space, and that the target is an ideal reflector with zero radius. However, the zero-radius assumption can be easily removed by formulating the problem in a more generalized way that considers targets with arbitrary size. Such approaches were recently investigated in the literature, suggesting that hyperbola fitting can be used not only for estimating the velocity of the medium, but also for estimating the radius of subsurface cylinders, a very challenging problem with no conclusive solution to this day. In this letter, through a series of synthetic and laboratory experiments, we demonstrate that for practical GPR survey, hyperbola fitting is not suitable for simultaneously estimating both the velocity of the medium and the size of the target, due to its inherent nonuniqueness, making the results unreliable and sensitive to noise.

Determining subsurface electromagnetic (EM) wave velocity is critical for ground-penetrating radar (GPR) data analysis, as velocity is used for the time-to-depth conversion, and hence leads to obtaining the precise location of the objects of interest. Currently, the way to acquire detailed subsurface EM wave velocity models involves employing multi-offset GPR surveys, such as wide-angle reflection-refraction (WARR), in conjunction with normal moveout (NMO) based velocity analysis. Traditionally, these surveys are carried out using two separate transducers and were, therefore, time-consuming and had limited uptake. Recent advances in GPR hardware have allowed the development of novel systems with multi-concurrent sampling receivers, which enable rapid and dense acquisition of WARR data. These additional receivers increase the overall size, weight and cost of the system. Therefore, we investigated the effects of receiver arrangement on NMO-based velocity analysis and considered reducing the overall number of transducers, whilst maintaining satisfactory velocity spectra resolution and, hence, obtaining detailed stacking velocity models as well as improved stacked reflection sections. We used both simulated data from complex three-dimensional models as well as field data and examined different numbers and positions of receivers in different environments. Our results show that velocity spectra resolution can be maintained within acceptable limits whilst reducing the number of receivers from a configuration with seven equally spaced receivers, to a sparse configuration of four receivers. Thus, being able to decrease the number of receivers used by these new GPR systems will reduce both the total system weight and cost and, hopefully, increase their adoption for GPR surveys.

Chang’E-4 was the first mission to land a human object on the far side of the Moon. The landing site was at the Von Kármán (VK) crater at the South-Pole Aitken (SPA) basin, one of biggest craters in the solar system. SPA is believed that was created by a huge impact that penetrated the lunar crust and uplifted mantle materials. Evidence of these materials are expected to be found by the Yutu-2, the rover of the mission that is still active to this day, having covered more than 1km on the lunar’s surface. Yutu-2 is equipped with a stereo camera, visible/near-infrared imaging spectrometer, alpha particle x-ray spectrometer and Ground-Penetrating Radar (GPR). In-situ GPR is a powerful geophysical methodology with a uniquely wide range of applications to civil engineering, archaeology and geophysics. In planetary science, it was first used in 2013 during the Chang’E-3 mission. Since then, GPR has become a very popular instrument in planetary missions, and has been included in the scientific payload of Chang’E-4, E-5, Tianwen-1, and Perseverance. It is also planned to be used in the future missions Chang’E-7 (2024) and ExoMars (September 2022). Yutu-2 rover is equipped with three different GPR systems. One low frequency and two high frequency antennas. Unfortunately, due to interferences between the antenna and the metallic parts of the rover, the low frequency data have very low signal to clutter ratio making the interpretation of these data unreliable. On the other hand, the signal from the high frequency antennas is very clear, probably due the lack of ilmenite in the area, which results to low electromagnetic losses (compared to the Chang’E-3 landing site). This resulted to good quality radagrams that provided new insights into the structure and composition of the top ejecta layers at the VK crater. In the current paper, we introduce a complete processing scheme, tuned for high frequency lunar penetrating radar. The first step of the proposed framework is an advanced hyperbola fitting (AHF) capable of inferring previously unseen layers due to their smooth boundaries. Subsequently, the reconstructed layered structure is used in a Reverse-Time Migration (RTM) coupled with Finite-Differences Time-Domain (FDTD) method. Via this approach, the radagram is focused subject to a 1D model, avoiding homogeneity constrains that often deviate from reality. Lastly, an un-supervised thresholding is applied to cluster the migrated image into two categories i.e. A) the background host medium and B) rocks/boulders. The suggested scheme is applied to the high frequency data collected by the Yutu-2 rover at the first 100 meters of the mission. A layered structure is inferred at the top 12 meters, similar to the results presented in [1]. Moreover, using the proposed RTM, an abundance of rocks/boulders was revealed. The distribution of the rocks/boulders correlates with the permittivity/density profile, indicating the reliability of the proposed scheme. References[1] Giannakis, I., Zhou, F., Warren, C., & Giannopoulos, A. (2021). Inferring the shallow layered structure at the Chang’E-4 landing site: A novel interpretation approach using lunar penetrating radar. Geophysical Research Letters, 48, e2021GL092866

Full-waveform inversion (FWI) is considered one of the most promising interpretation tools for hydrogeological applications using ground-penetrating radar. However, FWI has had limited practical uptake for several reasons: large computational requirements, an inability to reconstruct loss mechanisms of soil, and the need for a good initial starting model. We aim to address these issues via a novel FWI subject to a fractally correlated distribution of water. Initially, the dispersive properties of the soil are expressed as a function of the water fraction using a semiempirical model. This approach means that the permittivity, conductivity, and relaxation mechanisms are all correlated, and therefore, sensitivity problems between the permittivity and loss mechanisms no longer affect the performance of FWI. Subsequently, the distribution of the water fraction is constrained to follow a fractal geometry. Fractal-correlated noise is then compressed using a principal component analysis (PCA) in order to further reduce the number of the system's unknowns and accelerate FWI. PCA reduces the volume and dimensions of the optimization space, and thus, initialization is no longer necessary. Finally, a novel measurement configuration is suggested that uses superposition with all the individual measurements in order to reduce the number of forward models that need to be executed for every iteration of FWI. These enhancements substantially reduce the computational requirements of FWI and therefore eliminate the need for high-performance computers and time-consuming algorithms. The proposed scheme has been successfully tested with several numerical case-studies, which indicates the potential of this approach to become a commercially appealing interpretation tool for hydrogeology.

Quantitative structure-property relationship (QSPR) models for predicting primary biodegradation of petroleum hydrocarbons have been previously developed. These models use experimental data generated under widely varied conditions, the effects of which are not captured adequately within model formalisms. As a result, they exhibit variable predictive performance and are unable to incorporate the role of study design and test conditions on the assessment of environmental persistence. To address these limitations, a novel machine-learning System-Integrated Model (HC-BioSIM) is presented, which integrates chemical structure and test system variability, leading to improved prediction of primary disappearance time (DT50) values for petroleum hydrocarbons in fresh and marine water. An expanded, highly curated database of 728 experimental DT50 values (181 unique hydrocarbon structures compiled from 13 primary sources) was used to develop and validate a supervised model tree machine-learning model. Using relatively few parameters (6 system and 25 structural parameters), the model demonstrated significant improvement in predictive performance (root mean square error = 0.26, R

The simulation, or forward modeling, of Ground Penetrating Radar (GPR) is becoming a more frequently used approach to facilitate interpretation of complex real GPR data, and as an essential component of full-waveform inversion (FWI). However, general full-wave 3D electromagnetic (EM) solvers, such as ones based on the Finite-Difference Time-Domain (FDTD) method, are still computationally demanding for simulating realistic GPR problems. We have developed a novel near real-time, forward modeling approach for GPR that is based on a machine learning (ML) architecture. The ML framework uses an innovative training method which combines a predictive principal component analysis technique, a detailed model of the GPR transducer, and a large dataset of modeled GPR responses from our FDTD simulation software. The ML-based forward solver is parameterized for a specific GPR application, but the framework can be applied to many different classes of GPR problems.To demonstrate the novelty and computational efficiency of our ML-based GPR forward solver, we used it to carry out FWI for a common infrastructure assessment application – determining the location and diameter of reinforcement bars in concrete. We tested our FWI with synthetic and real data, and found a good level of accuracy in determining the rebar location, size, and surrounding material properties from both datasets. The combination of the near real-time computation, which is orders of magnitude less than what is achievable by traditional full-wave 3D EM solvers, and the accuracy of our ML-based forward model is a significant step towards commercially-viable applications of FWI of GPR.

Fish bioconcentration factors (BCFs) are often used to assess substance-specific bioaccumulation. However, reliable BCF data are limited given the practical challenges of conducting such tests. The objectives of this paper are to describe nine rainbow trout studies performed in our lab using tailored dosing and test designs for obtaining empirical BCFs for 21 test substances; gain insights into the structural features and processes determining the magnitude and uncertainty in observed BCFs; and assess performance of six quantitative structure property relationships (QSPRs) for correctly categorizing bioaccumulation given current regulatory triggers. Resulting mean steady-state BCFs, adjusted to a 5% lipid content, ranged from 12 Lkg-1 for isodecanol to 15,448 Lkg-1 for hexachlorobenzene which served as a positive control. BCFs for hydrocarbons depended on aromatic and saturated ring configurations and position. Uptake clearances appeared to be modulated by gill metabolism and substance bioavailability, while elimination rates were likely influenced by somatic biotransformation. Current approaches for quantifying uncertainty in experimental BCFs, which take into account only variability in measured fish concentrations, were found to underestimate the true uncertainty in this endpoint with important implications for decision-making. The Vega (KNN/Read-Across) QSPR and Arnot-Gobas model yielded the best model performance when compared to measured BCFs generated in this study.

The Finite-Difference Time-Domain (FDTD) method is a popular numerical modelling technique in computational electromagnetics. The volumetric nature of the FDTD technique means simulations often require extensive computational resources (both processing time and memory). The simulation of Ground Penetrating Radar (GPR) is one such challenge, where the GPR transducer, subsurface/structure, and targets must all be included in the model, and must all be adequately discretised. Additionally, forward simulations of GPR can necessitate hundreds of models with different geometries (A-scans) to be executed. This is exacerbated by an order of magnitude when solving the inverse GPR problem or when using forward models to train machine learning algorithms. We have developed one of the first open source GPU-accelerated FDTD solvers specifically focused on modelling GPR. We designed optimal kernels for GPU execution using NVIDIA’s CUDA framework. Our GPU solver achieved performance throughputs of up to 1194 Mcells/s and 3405 Mcells/s on NVIDIA Kepler and Pascal architectures, respectively. This is up to 30 times faster than the parallelised (OpenMP) CPU solver can achieve on a commonly-used desktop CPU (Intel Core i7-4790K). We found the cost–performance benefit of the NVIDIA GeForce-series Pascal-based GPUs – targeted towards the gaming market – to be especially notable, potentially allowing many individuals to benefit from this work using commodity workstations. We also note that the equivalent Tesla-series P100 GPU – targeted towards data-centre usage – demonstrates significant overall performance advantages due to its use of high-bandwidth memory. The performance benefits of our GPU-accelerated solver were demonstrated in a GPR environment by running a large-scale, realistic (including dispersive media, rough surface topography, and detailed antenna model) simulation of a buried anti-personnel landmine scenario. New version program summary Program Title: gprMax Program Files doi: http://dx.doi.org/10.17632/kjjm4z87nj.1 Licensing provisions: GPLv3 Programming language: Python, Cython, CUDA Journal reference of previous version: Comput. Phys. Comm., 209 (2016), 163–170 Does the new version supersede the previous version?: Yes Reasons for the new version: Performance improvements due to implementation of CUDA-based GPU engine Summary of revisions: A FDTD solver has been written in CUDA for execution on NVIDIA GPUs. This is in addition to the existing FDTD solver which has been parallelised using Cython/OpenMP for running on CPUs. Nature of problem: Classical electrodynamics Solution method: Finite-Difference Time-Domain (FDTD)

The COVID-19 [SARS-COV-2] pandemic has had a devastating global impact, with both the human and socio economic costs being severe. One result of the COVID-19 pandemic is the emergence of an urgent requirement for effective techniques and technologies for screening individuals showing symptoms of infection in a non-invasive and non-contact way. Systems that exploit thermal imaging technology to screen individuals show promise to satisfy the desired criteria, including offering a non-contact, non-invasive method of temperature measurement. Furthermore, the potential for mass and passive screening makes thermal imaging systems an attractive technology where current ‘standard of care’ methods are not practical.Critically, any fever screening solution must be capable of accurate temperature measurement and subsequent prediction of core temperature. This is essential to ensure a high sensitivity in identifying fever while maintaining a low rate of false positives. This paper discusses the results and analysis of a clinical trial undertaken by Thales UK Ltd and the Queen Elizabeth University Teaching Hospital in Glasgow to assess the accuracy and operation of the High Temperature Detection (HTD) system developed by Thales UK Ltd when used in a clinical setting.Results of this single centre prospective observational cohort study show that the measured laboratory accuracy of the Thales HTD system (RMSE=0.1°C) is comparable to the accuracy when used in a clinical setting (RMSE = 0.15°C) when measuring a calibrated blackbody source at typical skin temperature. For measurement of forehead skin temperature, the system produced results commensurate with close contact measurement methods (R = 0.86, Mean error=0.05°C).. Compared to measured tympanic temperatures, measurement of the forehead skin temperature by the HTD system showed a moderate correlation (R = 0.43),), which is stronger than close contact IR forehead thermometers (R = 0.20,0.35) An improved correlation was observed between the maximum facial temperature measured by the HTD system and measured tympanic temperatures (R = 0.53), which is significantly stronger than the close contact methods. A linear predictive model for tympanic temperature based on the measured maximum facial temperatures resulted in a root mean square error (RMSE = 0.50°C) that is marginally larger than what is expected as a compound of errors in the measuring devices (RMSE=0.45°C).The study demonstrates that the HTD could be applied in the clinical and non-clinical setting as a screening mechanism to detect citizens with raised temperature. This approach would enable high volume surveillance and identification of individuals that contribute to further spread of COVID-19. Deployment of the HTD system could be implemented as part of a screening tool to support measures to enhance public safety and confidence in areas of high throughput, such as airports, shopping centres or places of work.

Full-waveform inversion (FWI) using ground-penetrating radar (GPR) is gaining momentum as a powerful hydrogeological tool for inferring the hydraulic properties of soils between boreholes [1]. Nonetheless, the large computational requirements of FWI make it often unattainable with limited practical uptake [2]. In addition, the inability to accurate reconstruct the loss mechanisms and the need for a good initial model, further reduce the applicability of FWI [1], [2].In order to overcome the aforementioned limitations, we suggest a novel framework that substantially reduces the optimization space of FWI which consequently reduces the overall computational requirements [2]. This methodology assumes that the water fraction of the investigated medium follows a fractal distribution [3]. Based on that, using a principal components analysis on 3000 randomly generated fractals, we build an orthonormal basis that is fine-tuned for fractal correlated noise. Furthermore, it is proven [2], that fractal correlated noise is highly compressible and can be sufficiently represented with just 30-40 principal components. This reduces the optimization space since now FWI needs to fine-tune just these 30-40 parameters instead of every cell of the investigated medium [2].The involved fractals describe the distribution of the water fraction that is subsequently transformed to dielectric properties via a semi-empirical formula that relates readily available soil properties to the frequency depended complex electric permittivity [4], [5]. Via this approach, we overcome the need for a simultaneous FWI for both permittivity and conductivity [6]. This further reduces the optimization space and overcomes pitfalls associated with reconstructing loss mechanisms [2].References[1] Klotzsche, A., Vereecken, H., & Kruk van der J., (2019), Review of Crosshole Ground-Penetrating Radar Full-Waveform Inversion of Experimental Data: Recent Developments, challenges, and Pitfalls, Geophysics, vol. 84, pp. H13-H28.[2] Giannakis, I, Giannopoulos, A., Warren, C. & Sofroniou, A., (2021), Fractal-Constrained Crosshole/Borehole-to-Surface Full Waveform Inversion for Hydrogeological Applications Using Ground-Penetrating Radar, IEEE Transactions on Geoscience and Remote Sensing, Early Access.[3] Turcotte, L. (1992), Fractal and Chaos in Geology and Geophysics, Cambrige, UK: The Press Syndicate of the University of Cambridge.[4] Peplinski, N. R., Ulaby, F. T., & Dobson, M. C., (1995), Dielectric Properties of Soils in the 0.3-1.3 GHz Range, IEEE Transactions on Geoscience and Remote Sensing, vol. 33, no. 3, pp. 803-807.[5] Giannakis, I., Realistic Numerical Modelling of Ground Penetrating Radar for Landmine Detection, (2016), PhD Thesis Submitted at The University of Edinburgh.[6] Meles, G. A., Kruk, van der J., Grennhalgh, S. A., Ernst, J. R., Maurer, H & Green, A. G., (2010), A New Vector Waveform Inversion Algorithm for Simultaneous Updating of Conductivity and Permittivity Parameters from Combination Cross/Borehole-to-Surface GPR Data, IEEE Transactions on Geoscience and Remote Sensing, vol. 48, no. 9, pp. 3391-3407.

Lack of well-labelled and coherent training data is the main reason why machine learning (ML) and data-driven interpretations are not established in the field of Ground-Penetrating Radar (GPR). Non-representative and limited datasets lead to non-reliable ML-schemes that overfit, and are unable to compete with traditional deterministic approaches. To that extent, numerical data can potentially complement insufficient measured datasets and overcome this lack of data, even in the presence of large feature spaces.Using synthetic data in ML is not new and it has been extensively applied to computer vision. Applying numerical data in ML requires a numerical framework capable of generating synthetic but nonetheless realistic datasets. Regarding GPR, such a framework is possible using gprMax, an open source electromagnetic solver, fine-tuned for GPR applications [1], [2], [3]. gprMax is fully parallelised and can be run using multiple CPU’s and GPU’s. In addition, it has a flexible scriptable format that makes it easy to generate big data in a trivial manner. Stochastic geometries, realistic soils, vegetation, targets [3] and models of commercial antennas [4], [5] are some of the features that can be easily incorporated in the training data.The capability of gprMax to generate realistic numerical datasets is demonstrated in [6], [7]. The investigated problem is assessing the depth and the diameter of rebars in reinforced concrete. Estimating the diameter of rebars using GPR is particularly challenging with no conclusive solution. Using a synthetic training set, generated using gprMax, we managed to effectively train ML-schemes capable of estimating the diameter of rebar in an accurate and efficient manner [6], [7]. The aforementioned case studies support the premise that gprMax has the potential to provide realistic training data to applications where well-labelled data are not available, such as landmine detection, non-destructive testing and planetary sciences.References[1] Warren, C., Giannopoulos, A. & Giannakis, I., (2016). gprMax: Open Source software to simulate electromagnetic wave propagation for Ground Penetrating Radar, Computer Physics Communications, 209, 163-170.[2] Warren, C., Giannopoulos, A., Gray, A., Giannakis, I., Patterson, A., Wetter, L. & Hamrah, A., (2018). A CUDA-based GPU engine for gprMax: Open source FDTD, electromagnetic simulation software. Computer Physics Communications, 237, 208-218.[3] Giannakis, I., Giannopoulos, A. & Warren, C. (2016). A realistic FDTD numerical modeling framework of Ground Penetrating Radar for landmine detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 9(1), 37-51.[4] Giannakis, I., Giannopoulos, A. & Warren, C., (2018). Realistic FDTD GPR antenna models optimized using a novel linear/non-linear full waveform inversion. IEEE Transactions on Geoscience and Remote Sensing, 207(3), 1768-1778.[5] Warren, C., Giannopoulos, A. (2011). Creating finite-difference time-domain models of commercial ground-penetrating radar antennas using Taguchi’s optimization method. Geophysics, 76(2), G37-G47[6] Giannakis, I., Giannopoulos, A. & Warren, C. (2021). A Machine Learning Scheme for Estimating the Diameter of Reinforcing Bars Using Ground Penetrating Radar. IEEE Geoscience and Remote Sensing Letters.[7] Giannakis, I., Giannopoulos, A., & Warren, C. (2019). A machine learning-based fast-forward solver for ground penetrating radar with application to full-waveform inversion. IEEE Transactions on Geoscience and Remote Sensing. 57(7), 4417-4426.

The most frequently used survey mode for acquiring Ground Penetrating Radar (GPR) data is common offset (CO) – where a single transmitter and receiver pair move along a survey line at a constant (offset) separation distance. This allows rapid and dense data acquisition, and therefore high-resolution large-scale investigations, to be carried out with relative ease, and at relatively low cost. However, it has long been known that multi-offset survey methods, such as common mid-point (CMP) and wide-angle reflection-refraction (WARR), can offer many benefits over CO: detailed subsurface EM wave velocity models; enhanced reflection sections with higher signal-to-noise ratio (SNR); the potential to adapt well-established advanced seismic processing schemes for GPR data [1-2].Despite the advantages of multi-offset GPR data, these methods have seen limited adoption as, in the past, they required significantly more time, effort, and hence cost, to collect. However, recent advances in GPR hardware, particularly in timing and control technology, have enabled the development of multi-concurrent sampling receiver GPR systems such as the “WARR Machine” manufactured by Sensors & Software Inc. [3-4]. These newly developed GPR systems have the potential to provide all the aforementioned benefits with considerably less effort and therefore reduced survey cost, as they allow for the fast acquisition of multi-offset WARR soundings.In this work, we look at the challenges and opportunities from collecting and processing multi-offset GPR data. We demonstrate a processing workflow that combines standard GPR processing approaches, with methods adapted from seismic processing, as well as our own algorithms. This processing framework has been implemented into a GUI-based software written in MATLAB [5], and has been tested using both synthetic [6] and real multi-offset GPR data. Some of the specific challenges with multi-offset GPR that we investigate are time zero misalignments, CMP balancing, velocity analysis, and automated velocity picking. We show how addressing these issues can result in improved velocity analysis, and ultimately in improved subsurface velocity models, and stacked sections.References[1] Ursin, B., 1983. Review of elastic and electromagnetic wave propagation in horizontally layered media. Geophysics, 48(8), pp.1063-1081.[2] Carcione, J. and Cavallini, F., 1995. On the acoustic-electromagnetic analogy. Wave Motion, 21(2), 149-162.[3] Annan, A. P., and Jackson, S., 2017. The WARR machine. 2017 9th International Workshop on Advanced Ground Penetrating Radar (IWAGPR).[4] Diamanti, N., Elliott, J., Jackson, R. and Annan, A. P., 2018, The WARR Machine: System Design, Implementation and Data: Journal of Environmental & Engineering Geophysics, 23, pp.469-487.[5] Angelis, D., Warren, C. and Diamanti, N., 2020. A software toolset for processing and visualization of single and multi-offset GPR data. 18th International Conference on Ground Penetrating Radar.[6] Warren, C., Giannopoulos, A. and Giannakis, I., 2016. gprMax: Open source software to simulate electromagnetic wave propagation for Ground Penetrating Radar. Computer Physics Communications, 209, pp.163-170.

Ground penetrating radar (GPR) is a well-established tool for detecting and locating reinforcing bars (rebars) in concrete structures. However, using GPR to quantify the diameter of rebars is a challenging problem that current processing approaches fail to tackle. To that extent, we have developed a novel machine learning framework that can estimate the diameter of the investigated rebar within the resolution range of the employed antenna. The suggested approach combines neural networks and a random forest regression and has been trained entirely using synthetic data. Although the training process relied only on numerical training sets, nonetheless, the suggested scheme is successfully evaluated with real data indicating the generalization capabilities of the resulting regression. The only required input of the proposed technique is a single A-scan, avoiding laborious measurement configurations and multisensor approaches. In addition, the results are provided in real time and making this method practical and commercially appealing.

Processing Ground Penetrating Radar (GPR) data is essential to assist with interpretation, the reduction of unwanted noise, and to highlight information relating to the purpose of a survey. Currently, the majority of GPR data processing software is coupled together with GPR hardware systems from the manufacturers, and often at an additional cost. These software packages often incorporate simplified or limited processing methods, and they do not allow the processing of data generated by GPR systems of different manufacturers.\ud \ud We present a MATLAB-based software with a user-friendly graphical user interface (GUI) for the visualization and processing of GPR data. The software supports file formats from a wide variety of GPR manufacturers, and can handle both single-fold (SF) and multi-fold (MF) GPR data. The software is capable of applying standard workflows, but one of its key features is novel processing algorithms for MF GPR data, especially from the new multi-concurrent sampling receiver GPR systems. These algorithms are more commonly used in seismic data processing and have not previously been applied to GPR data from these systems.

Full-waveform inversion (FWI) of cross-borehole ground-penetrating radar (GPR) data is a technique with the potential to investigate subsurface structures. Typical FWI applications transform 3D measurements into a 2D domain via an asymptotic 3D to 2D data transformation, widely known as a Bleistein filter. Despite the broad use of such a transformation, it requires some assumptions that make it prone to errors. Although the existence of the errors is known, previous studies have failed to quantify the inaccuracies introduced on permittivity and electrical conductivity estimation. Based on a comparison of 3D and 2D modeling, errors could reach up to 30% of the original amplitudes in layered structures with high-contrast zones. These inaccuracies can significantly affect the performance of crosshole GPR FWI in estimating permittivity and especially electrical conductivity. We have addressed these potential inaccuracies by introducing a novel 2.5D crosshole GPR FWI that uses a 3D finite-difference time-domain forward solver (gprMax3D). This allows us to model GPR data in 3D, whereas carrying out FWI in the 2D plane. Synthetic results showed that 2.5D crosshole GPR FWI outperformed 2D FWI by achieving higher resolution and lower average errors for permittivity and conductivity models. The average model errors in the whole domain were reduced by approximately 2% for permittivity and conductivity, whereas zone-specific errors in high-contrast layers were reduced by approximately 20%. We verified our approach using crosshole 2.5D FWI measured data, and the results showed good agreement with previous 2D FWI results and geologic studies. Moreover, we analyzed various approaches and found an adequate trade-off between computational complexity and accuracy of the results, i.e., reducing the computational effort while maintaining the superior performance of our 2.5D FWI scheme.

In addition to methane gas, higher-value resources such as hydrogen gas are produced during anaerobic wastewater treatment. They are, however, immediately consumed by other organisms. To recover these high-value resources, not only do the desired phenotypes need to be retained in the anaerobic reactor, but the undesired ones need to be washed out. In this study, a well-established alginate-based polymer gel, with and without a coating layer, was used to selectively encapsulate hydrogen-producing biomass in beads to achieve high-rate recovery of hydrogen during anaerobic wastewater treatment. The effect of cross-linking agents, Ca2+, Sr2+, and Ba2+, as well as a composite coating on the beads, consisting of alternating layers of polyethylenimine and silica hydrogel, were investigated with respect to their performance, specifically, their mass transfer characteristics and their differential ability to retain the encapsulated biomass. Although the coating reduced the escape rate of encapsulated biomass from the beads, all alginate polymer matrices without coating effectively retained biomass. Fast diffusion of dissolved organic carbon (DOC) through the polymer gel was observed in both Ca-alginate and Sr-alginate without coating. The coating, however, decreased either the diffusivity or the permeability of the DOC depending on whether the DOC was from synthetic wastewater (more lipids and proteins) or real brewery wastewater (more sugars). Consequently, the encapsulation system with coating became diffusion limited when brewery wastewater with high chemical oxygen demand was fed, resulting in a lower hydrogen production rate than the uncoated encapsulation systems. In all cases the encapsulated biomass was able to produce hydrogen, even at a hydraulic residence time of 45 min. Although there are limitations to this system, the used of encapsulated biomass for resource recovery from wastewater shows promise, particularly for high-rate systems in which the retention of specific phenotypes is desired.

Methods for obtaining accurate predictions of solvent-water partitioning for neutral organic chemicals (e.g., Kow) are well-established. However, methods that provide comparable accuracy are not available for predicting the solvent-water partitioning of ionic species. Previous methods for addressing charge contributions to solvent-water partitioning rely on charged solute descriptors which are obtained from regressions to neutral species descriptors as well as charged descriptors which are specific to unique charge-functionalities and structural moieties. This paper presents a method for obtaining Abraham poly-parameter linear free energy relationship (pp-LFER) descriptors using quantum chemical calculations and molecular structure, only. The method utilizes a large number of solvent-water systems to overcome large errors in individual quantum chemical computations of ionic solvent-water partition coefficients. The result is a single set of quantum-chemically estimated Abraham solute parameters (QCAP) which are solvent-independent, and can be used to predict the solvent-water partitioning of ionic species. Predictions of solvent-water partition coefficients for ionic species using quantum-chemically estimated Abraham parameters (QCAPs) are shown to provide improved accuracy compared over both existing Absolv-estimated Abraham solute parameters (AAP) as well as direct a priori quantum chemical (QC) calculations for partitioning of anionic solutes in 4 organic solvent-water systems (RMS = 0.740, 2.48 and 0.426 for the Absolv, QC and QCAP methods, respectively). For quaternary amine cations in the octanol-water system the RMS errors of the solvent-water partition coefficients were larger and similar between the two Abraham models (RMSE = 0.997 and 1.16, for the AAP and QCAP methods, respectively). Both methods showed significant improvement over direct QC calculations (RMSE = 2.82).

Bitumen recovery via mining in Alberta’s Athabasca region generates large quantities of oil sands process water (OSPW). Aquatic toxicity of OSPW has been well-studied and the class of organic compounds referred to as naphthenic acids (NAs) are consistently implicated as the primary driver. Proposed lease closure options include treated produced waters in reclaimed landscapes such as pit lakes and wetlands. Consequently, it is crucial to understand the bioaccumulation potential of NAs and other OSPW dissolved organics in these environments. Early studies were focussed only on NAs due to analytical limitations, however, later studies investigated additional classes of dissolved organics in OSPW. Reported bioconcentration factors (BCFs) for NAs in fish and amphibians range from 0.24 to 53 L/kg wet-weight. Most quantitative assessments of NAs bioaccumulation potential evaluated commercial NAs mixtures as a surrogate for OSPW and used using single-ion monitoring for measuring NAs concentrations. The resulting BCF values are based on the NA isomers that conform to the formula, C13H22O2. More recently, an advanced analytical technique capable of determining the profile of different isomer classes in OSPW showed that NAs and other OSPW ionizable dissolved organics (OSPW-IDO) have low partitioning to simulated biological storage lipids, suggesting low bioaccumulation potential. Using the same analytical technique to assess in vivo fish exposures, a subsequent study reported a range of BCFs for OSPW NAs between 0.7 and 53 L/kg wet-weight and heteroatomic isomer classes containing S or N heteroatoms had BCFs between 0.6 and 28 L/kg wet-weight. Reported BCFs for all isomer classes of the OSPW-IDO fraction were less than the Canadian standard for bioaccumulative designation (i.e., BCF ≥ 5000).

Finite-Difference Time-Domain (FDTD) modelling of Ground Penetrating Radar (GPR) is becoming regularly used in model-based interpretation methods like full waveform inversion (FWI), and machine learning schemes using synthetic training data. Oversimplifications in such forward models can compromise the accuracy and realism with which real GPR responses can be simulated, and this degrades the overall performance of the aforementioned interpretation techniques. Therefore, a forward model must be able to accurately simulate every part of the GPR problem that can affect the resulting scattered field. A key element is the antenna system and excitation waveform, so the model must contain a complete description of the antenna including the excitation source and waveform, the geometry, and the dielectric properties of materials in the antenna. The challenge is that some of these parameters are not known or easily measured, especially for commercial GPR antennas that are used in practice. We present a novel hybrid linear/non-linear FWI approach which can be used, with only knowledge of the basic antenna geometry, to simultaneously optimise the dielectric properties and excitation waveform of the antenna, and minimise the error between real and synthetic data. The accuracy and stability of our proposed methodology is demonstrated by successfully modelling a Geophysical Survey Systems (GSSI) Inc. 1.5~GHz commercial antenna. Our framework allows accurate models of GPR antennas to be developed without requiring detailed knowledge of every component in the antenna. This is significant because it allows commercial GPR antennas, regularly used in GPR surveys, to be more readily simulated.

Summary Recent advances in timing and control technology have allowed the development of multi-concurrent receiver Ground Penetrating Radar (GPR) systems. These types of system enable rapid acquisition of Wide-Angle Reflection and Refraction (WARR) data and have the potential to provide a wide range of benefits, including improved velocity models. However, the high volume of data that can be generated, as well as the fact that these systems are new and continually being developed, demands research and automation of new processing workflows. Numerical modeling is gradually becoming a standard approach in GPR for testing new processing schemes/algorithms and guiding interpretation of real data. Nevertheless, simulations can require detailed models of antennas, targets, and structures, which can be time-consuming to create programmatically. In this work, we demonstrate a graphical user interface (GUI) tool for rapidly designing and building complex geometries that can be used in numerical simulations for GPR. We then use this toolset to generate realistic WARR data to test automatic first break/time-offset correction algorithms we have developed. These algorithms form the initial steps of our workflow for processing WARR data.

A three-dimensional (3-D) finite-difference time-domain (FDTD) algorithm is used in order to simulate ground penetrating radar (GPR) for landmine detection. Two bowtie GPR transducers are chosen for the simulations and two widely employed antipersonnel (AP) landmines, namely PMA-1 and PMN are used. The validity of the modeled antennas and landmines is tested through a comparison between numerical and laboratory measurements. The modeled AP landmines are buried in a realistically simulated soil. The geometrical characteristics of soil’s inhomogeneity are modeled using fractal correlated noise, which gives rise to Gaussian semivariograms often encountered in the field. Fractals are also employed in order to simulate the roughness of the soil’s surface. A frequency-dependent complex electrical permittivity model is used for the dielectric properties of the soil, which relates both the velocity and the attenuation of the electromagnetic waves with the soil’s bulk density, sand particles density, clay fraction, sand fraction, and volumetric water fraction. Debye functions are employed to simulate this complex electrical permittivity. Background features like vegetation and water puddles are also included in the models and it is shown that they can affect the performance of GPR at frequencies used for landmine detection (0.5–3 GHz). It is envisaged that this modeling framework would be useful as a testbed for developing novel GPR signal processing and interpretations procedures and some preliminary results from using it in such a way are presented.

Encapsulating microbial cells in an alginate matrix enables high-rate anaerobic biological treatment and resource recovery by increasing cell concentration and retention time. To determine whether encapsulation protected microbial cells from inhibitors, copper(II), chromium(VI) (as dichromate), ammonium, and chloroform were amended to hydrogen-producing anaerobic microbial cells that were either suspended or encapsulated in a composite-coated alginate matrix. Encapsulation mitigated dichromate inhibition by 36% based on the dose-response curve slope but increased ammonium inhibition by 210% and had no effect on inhibition by chloroform. This was explained by effective concentrations in the beads after measuring partition coefficients. Encapsulation rendered copper(II) unavailable through apparent chelation because it protected both the hydrogen-producing and a methane-producing community from copper(II) despite its high effective concentration in encapsulation. Understanding the effects that the encapsulation of microbial cells has on chemical inhibition can help expand the application conditions of wastewater treatment or resource recovery and avoid unanticipated toxicity.

Ray-based, or approximate, forward modeling techniques have often been used to reduce computational demands for inversion purposes. Due to increasing computational power and possible parallelization of inversion algorithms, accurate forward modeling can be included in advanced inversion approaches such that the full-waveform can be exploited. Here, recent developments of full-waveform ground penetrating radar (GPR) inversions are discussed that yield higher resolution of quantitative medium properties compared to conventional approaches, because of the use of accurate modeling tools that are based on Maxwell's equations. For a limited number of parameters, a combined global and local search using the shuffled complex evolution (SCE) can be used for inversion. For a large number of unknowns, gradient-based optimization methods are commonly used that need a good starting model to prevent them from being trapped in local minima. An overview of the methodological developments for surface and crosshole GPR full-waveform inversion will be given and several applications will be presented. Finally, recent developments and future opportunities will be discussed.

The ability to produce, store and analyse large amounts of well-labeled data as well as recent advancements on supervised training, led machine learning to gain a renewed popularity. In the present paper, the applicability of machine learning to simulate ground penetrating radar (GPR) for high frequency applications is examined. A well-labelled and equally distributed training set is generated synthetically using the finite-difference time-domain (FDTD) method. Special care was taken in order to model the antennas and the soils with sufficient accuracy. Through a stochastic parameterisation, each model is expressed using only seven parameters (i.e. the fractal dimension of water fraction, the height of the antenna and so on). Based on these parameters and the synthetically generated training set, a machine learning framework is trained to predict the resulting A-Scan in real-time. Thus, overcoming the time-consuming calculations required for an equivalent FDTD simulation.

Numerical modelling of Ground Penetrating Radar (GPR) using the Finite-Difference Time-Domain (FDTD) method can be computationally intensive when large volumes must be discretised at fine spatial resolutions. This requirement exists because of the conditionally explicit nature of the FDTD algorithm, and the need to simulate fine geometrical details as commonly found in GPR antennas or in regions of high permittivity dielectric materials. We have developed and implemented a Huygens subgridding (HSG) approach in the open source software gprMax. The HSG can be applied to multiple localised regions of the FDTD grid where it is required, whilst the rest of the grid can be discretised at a more appropriate spatial resolution. An example of applying the HSG to a GPR model that includes a detailed antenna model, demonstrates the accuracy and efficiency of the approach. The relative error compared with a uniform fine grid is generally less than 1.3%. The computational time is reduced by a factor of 16. This level of computational saving is especially important for optimisation and inversion algorithms where many GPR forward models are computed in a single iteration.

Numerical artifacts affect the reliability of computational dosimetry of human exposure to low- frequency electromagnetic fields. In the guidelines of the International Commission of Non-Ionizing Radiation Protection (ICNIRP), a reduction factor of 3 was considered to take into account numerical uncertainties when determining the limit values for human exposure. However, the rationale for this value is unsure. The IEEE International Committee on Electromagnetic Safety has published a research agenda to resolve numerical uncertainties in low- frequency dosimetry. For this purpose, intercomparison of results computed using different methods by different research groups is important. In previous intercomparison studies for low- frequency exposures, only a few computational methods were used, and the computational scenario was limited to a uniform magnetic field exposure. This study presents an application of various numerical techniques used: different Finite Element Method (FEM) schemes, Method of Moments (MoM) and Boundary Element Method (BEM) variants and finally by using a hybrid FEM/BEM approach. As a computational example, the induced electric field in the brain by the coil used in transcranial magnetic stimulation is investigated. Intercomparison of the computational results are presented qualitatively. Some remarks are given for the effectiveness and limitations of application of the various computational methods.

Forward modelling of Ground Penetrating Radar (GPR) is often used to facilitate interpretation of complex GPR data and as a key ingredient of full waveform inversion (FWI) processes. As general 3D full-wave electromagnetic solvers are computationally very demanding routine application of advanced GPR modelling is not popular. A novel concept for creating a fast GPR forward model based on machine learning (ML) concepts is presented. This ML-based model is trained using a dataset obtained from a realistic 3D Finite-Difference Time-Domain (FDTD) gprMax model. The fast model is trained for a specific GPR application that can be easily parametrised and have a somewhat constrained variability. However, the training uses GPR A-Scans obtained from very realistic forward models that include all complex scattering effects and antenna coupling mechanisms. To demonstrate the efficiency of the approach an application, using real GPR data, of the fast forward solver within a FWI process, using a global optimiser requiring a great number of forward model calculations, is presented producing very promising results.

Procedures for accurately predicting linear partition coefficients onto various sorbents (e.g., organic carbon, soils, clay) are reliable and well established. However, similar procedures for the prediction of sorption parameters of nonlinear isotherm models are not. The purpose of this paper is to present a procedure for predicting nonlinear isotherm parameters, specifically the median Langmuir binding constants, K̃L, obtained utilizing the single-chemical parameter log-normal Langmuir isotherm developed in the accompanying work. A reduced poly parameter linear free energy relationship (pp-LFER) is able to predict median Langmuir binding constants for graphite, charcoal, and Darco granular activated carbon (GAC) adsorption data. For the larger F400 GAC data set, a single pp-LFER model was insufficient, as a plateau is observed for the median Langmuir binding constants of larger molecular volume sorbates. This volumetric cutoff occurs in proximity to the median pore diameter for F400 GAC. A log-linear relationship exists between the aqueous solubility of these large compounds and their median Langmuir binding constants. Using this relationship for the chemicals above the volumetric cutoff and the pp-LFER below the cutoff, the median Langmuir binding constants can be predicted with a root-mean square error for graphite (n = 13), charcoal (n = 11), Darco GAC (n = 14), and F400 GAC (n = 44) of 0.129, 0.307, 0.407, and 0.424, respectively.