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In classical radar imaging, such as in Earth remote sensing, electromagnetic waves are usually assumed to propagate in free space. However, in numerous applications, such as ground penetrating radar or non-destructive testing, this assumption no longer holds. When there is a multi-material background, the subsurface image reconstruction becomes considerably more complex. Imaging can be performed in the spatial domain or, equivalently, in the wavenumber domain (k-space). In subsurface imaging, to date, objects with a non-planar surface are commonly reconstructed in the spatial domain, by the Backprojection algorithm combined with ray tracing, which is computationally demanding. On the other hand, objects with a planar surface can be reconstructed more efficiently in k-space. However, many non-planar surfaces are partly planar. Therefore, in this paper, a novel concept is introduced that makes use of the efficient k-space-based reconstruction algorithms for partly planar scenarios, too. The proposed algorithm forms an image from superposing sub-images where as many image parts as possible are reconstructed in the wavenumber domain, and only as many as necessary are reconstructed in the spatial domain. For this, a segmentation scheme is developed to determine which parts of the image volume can be reconstructed in the wavenumber domain. The novel concept is verified by measurements, both from monostatic synthetic aperture radar data and multiple-input–multiple-output radar data. It is shown that the computational efficiency for imaging irregularly shaped geometries can be significantly augmented when applying the proposed concept. [ABSTRACT FROM AUTHOR]

Accurate predictions of fluid flow, mass transport, and reaction rates critically impact the efficiency and reliability of subsurface exploration and sustainable use of subsurface resources. Quantitative dynamical sensing and imaging can play a pivotal role in the ability to make such predictions. Geophysical thermoacoustic technology has the potential to provide the aforementioned capabilities, since it builds upon the principle that electromagnetic and mechanical wave fields can be coupled through a thermodynamic process. In this letter, we present laboratory experiments featuring the efficacy of thermoacoustic imaging in the monitoring of preferential flow of water in porous media. Our laboratory experimental equipment can be readily packaged in a form factor that fits in a borehole, and the use of multiple acoustic transducers—which can be combined with volumetric coding techniques—has the potential to provide quasi‐real‐time imaging (0.5 Hz video rate) of regions in close proximity (a few meters) of an open field well. Plain Language Summary: Multiphysics subsurface sensing and imaging technology has the potential to provide unique insights to better understand multiphase flow and transport in porous media in 4D (time and space). Conventional high‐resolution, laboratory‐based imaging technology—such as X‐ray or MRI—require power‐hungry and often bulky equipment; the latter limits their use in open field experiments and challenges their ability to perform real‐time image reconstruction. Acoustics Doppler imaging has been used for real‐time flow velocity monitoring in biomedical applications; however, the relationship between fluid saturation in porous media and measured acoustic pressure still requires further investigation. In this letter we show how microwave‐induced thermoacoustic (TA) imaging technology can be applied to monitor water distribution in sand. In contrast to traditional acoustic imaging, the proposed TA method exhibits a dominant monotonic relationship between the degree of water saturation and the measured amplitude of the TA pressure. Our experimental results show the efficacy of TA technology for imaging 2D water distribution profiles in sand. The reconstructed TA images are in good agreement with the optical ground truth water distribution map, thus illustrating the feasibility of the proposed method for real‐world field applications in agricultural and hydrological sciences. Key Points: The increased water saturation level in sand, silt, and clay leads to a higher thermoacoustics wave amplitudeThe reconstructed thermoacoustics images match well with the optical ground truth for water‐saturated sandThermoacoustics imaging enables real‐time monitoring of water distribution in subsurface sand [ABSTRACT FROM AUTHOR]

Abstract Accurate predictions of fluid flow, mass transport, and reaction rates critically impact the efficiency and reliability of subsurface exploration and sustainable use of subsurface resources. Quantitative dynamical sensing and imaging can play a pivotal role in the ability to make such predictions. Geophysical thermoacoustic technology has the potential to provide the aforementioned capabilities, since it builds upon the principle that electromagnetic and mechanical wave fields can be coupled through a thermodynamic process. In this letter, we present laboratory experiments featuring the efficacy of thermoacoustic imaging in the monitoring of preferential flow of water in porous media. Our laboratory experimental equipment can be readily packaged in a form factor that fits in a borehole, and the use of multiple acoustic transducers—which can be combined with volumetric coding techniques—has the potential to provide quasi‐real‐time imaging (0.5 Hz video rate) of regions in close proximity (a few meters) of an open field well.

Subsurface microwave imaging is the image reconstruction of objects buried below the surface of a medium, such as soil. Modeling the interactions between the background medium and objects, as well as artifacts due to the limited view of objects, are the major concerns in the image reconstruction process. In this work, we have presented an efficient subsurface microwave imaging technique for the reconstruction of targets in a two-layered medium with an arbitrary rough interface. The use of a computationally expensive two-layered background Green’s function in the classical integral equation model is avoided by exploiting the boundary data model where only a simple homogeneous Green’s function is required. The contrast source imaging technique is reformulated in terms of boundary data and total variation (TV) regularization is included in the cost function multiplicatively to handle limited view and noisy data in a robust way. Numerical simulations demonstrated that the proposed method dramatically reduces the computational time while keeping the same accuracy in comparison to the classical approach. [ABSTRACT FROM AUTHOR]

Different from the ground penetration radar providing only one-dimensional image, synthetic aperture radar (SAR) can produce two-dimensional and even three-dimensional image, which is meaningful in subsurface imaging, but possible layover effects may be a problem. Moreover, multipath reflections from the ground known as hot clutter or terrain scattered interferences will add nonstationary interference components to the subsurface imaging. In this paper, we present the use of fast-time space-time adaptive processing (STAP) to suppress undesired interferences for SAR subsurface imaging. Moreover, the frequency diverse array (FDA) range- and time-dependent beampattern is explored to enhance the SAR azimuth resolution and reduce fluctuations in the received signals, which helps in mitigating multipath effects and potentially addressing layover issues. The FDA-SAR platform moves at a constant speed along with the azimuth direction above the ground with a side-looking FDA antenna to formulate a strip-map side-looking SAR for subsurface imaging, where the FDA antenna elements also are placed along with the moving track of FDA-SAR platform. The research progress is reviewed and the most recent advances are introduced. The technical challenges and future works also are discussed. We aim to introduce the promising FDA-SAR with fast-time STAP for subsurface imaging and encourage further exploration in this domain.

A microwave characterization technique to inspect subsurface scenarios is proposed and numerically assessed in this paper. The approach is based on a combination of finite element electromagnetic modeling and an inversion procedure in Lebesgue spaces with variable exponents. The former allows for description of the measurement system and subsurface scenario with high accuracy, while the latter exploits the adaptive definition of exponent function to achieve improved results in the regularized solution of the inverse scattering problem. The method has been assessed with numerical simulations regarding two-layered environments with both planar and non-planar air–soil interfaces. The results show the capabilities of the method of detecting buried objects in different operative conditions. [ABSTRACT FROM AUTHOR]

Genetic programming (GP) is an evolutionary algorithm used to produce high-quality solutions to various problems. It has seen few claims in circuitry and the development of antenna designs. The application of GP in the model of embedded digital systems on multi-channel antenna arrays of subsurface imaging equipment has not yet been investigated. This study focuses on designing and developing a digital multimeter embedded with a multigene genetic programming (MGGP) model for multi-array transmitter antenna used for subsurface imaging operating at a low frequency of 3.5 kHz to 18.5 kHz using Arduino microcontroller for prototyping. The electrical outputs of a transmitter antenna system employed in a subsurface imaging device require live measurement and monitoring during operation for data logging purposes. The amount of transmitted voltage, produced current, and operating frequency are significant parameters for mapping the underground resistivity, thus the produced GP models are functions of the three parameters. GP fitness function was determined through MATLAB software. The output current signal from the transmitter were imitated in Proteus simulation software using a current source in the designed current measuring circuit. This produced linear and nonlinear relationships of the electrical outputs where GP modeling was beneficially applied. The application of GP in with the microcontroller provided an accurate reading of frequency, current and voltage produced by the multi-array transmitter antenna. These measurements were displayed using LM016L LCD display. Moreover, this embedded digital multimeter on transmitter antenna avoids utilizing costly high voltage measuring devices. [ABSTRACT FROM AUTHOR]

In classical radar imaging, such as in Earth remote sensing, electromagnetic waves are usually assumed to propagate in free space. However, in numerous applications, such as ground penetrating radar or non-destructive testing, this assumption no longer holds. When there is a multi-material background, the subsurface image reconstruction becomes considerably more complex. Imaging can be performed in the spatial domain or, equivalently, in the wavenumber domain (k-space). In subsurface imaging, to date, objects with a non-planar surface are commonly reconstructed in the spatial domain, by the Backprojection algorithm combined with ray tracing, which is computationally demanding. On the other hand, objects with a planar surface can be reconstructed more efficiently in k-space. However, many non-planar surfaces are partly planar. Therefore, in this paper, a novel concept is introduced that makes use of the efficient k-space-based reconstruction algorithms for partly planar scenarios, too. The proposed algorithm forms an image from superposing sub-images where as many image parts as possible are reconstructed in the wavenumber domain, and only as many as necessary are reconstructed in the spatial domain. For this, a segmentation scheme is developed to determine which parts of the image volume can be reconstructed in the wavenumber domain. The novel concept is verified by measurements, both from monostatic synthetic aperture radar data and multiple-input–multiple-output radar data. It is shown that the computational efficiency for imaging irregularly shaped geometries can be significantly augmented when applying the proposed concept.

In this study, the use of electrical resistivity tomography (ERT) as a tool to guide ornamental stone extraction is investigated. ERT is not conventionally used in highly resistive environments, such as on rock faces, due to the high contact resistances that can impede current injection. Here, the challenges of conducting ERT in such environments are discussed and possible solutions suggested. For this, an example of the application of ERT in a deep and narrow marble quarry is used. The marble deposit is affected by fracturing and karstification. Due to the nature of these features, they present a significant resistivity contrast to the background resistivity of the marble and thus excellent targets to test the application of ERT. Their location was mapped using field observations and complementary ground penetrating radar data. By using an appropriate sensor deployment, a suitable resistivity meter, and advanced data processing routines, the derived 3D resistivity model is in good agreement with the independent observations. This shows that despite the challenges, ERT can be used as a non-invasive tool to obtain information on the stone properties prior to extraction. This will help in guiding quarry operations and will allow for a targeted, safe and efficient extraction of high quality stone, thereby increasing sustainability and economical competitiveness.

The control of the subsurface areas of metal products is necessary in many technological processes. So, for example, in aerospace technology it is essential to determine the presence of defects in aircraft engines. The same problems arise in chemical, power and other industries, which are letting out the highly technological equipment one of the widely known methods for inspecting metal products in the aviation industry is the eddy current method. This method is widely used to control small microscopic defects inside conductive materials. This method allows to ensure the safety of the operation of various products and devices in many areas of modern industry. An eddy current detector (probe) is a device, which induces eddy currents into metal objects and then detects the magnetic fields produced by these eddy currents. A magnetic field is created by a coil, or set of coils, through which a time-varying electrical current is driven. The frequency regime is sufficiently low, a few hertz to a few hundred kilohertz, so the targets of interest are within the near field of the transmitter. Considering the high conductivity of study samples, we can define that used waves in metals are located in the millimeter wave band. An eddy current imaging can be considered near-field imaging and a device allowing obtaining the eddy current images as a scanning near-field microscope. The obtained experimental results showed that the proposed tomographic method is effective for studying various complex inhomogeneities under the metal surface

Due to the strong capability of building complex nonlinear mapping without involving linearization theory and high prediction efficiency; the deep learning (DL) technique applied to solve geophysical inverse problems has been a subject of growing interest. Currently, most DL-based inversion approaches are fully data-driven (namely standard deep learning), the performance of which largely depends on the training sample sets. However, due to the heavy burden of time and computational resources, it can be challenging to supply such a massive and exhaustive training dataset for generic realistic exploration scenarios and to perform network training. In this work, based on the recent advances in physics-based networks, the physical laws of magnetotelluric (MT) wave propagation is incorporated into a purely data-driven DL approach (PlainDNN) and thus builds a physics-driven DL MT inversion scheme (PhyDNN). In this scheme, the forward operator modeling MT wave propagation is integrated into the network training loop, in the form of minimizing a hybrid loss objective function composed of the data-driven model misfit and physics-based data misfit, to guide the network training. Consequently, the proposed PhyDNN method will take the advantage of the fully data-driven DL and conventional physics-based deterministic methods, allowing it to deal with complex realistic exploration scenarios. Quantitative and qualitative analysis results demonstrate that the PhyDNN can honor the physical laws of the MT inverse problem, and with other conditions unchanged, the PhyDNN outperforms the PlainDNN and the classical deterministic Occam inversion method. When processing field data, the PhyDNN method yields considerably impressive inversion results compared to the Occam method, and the corresponding simulated MT responses agree well with the real measurements, which confirms the effectiveness and applicability of the PhyDNN method. [ABSTRACT FROM AUTHOR]

Featured Application: The workflow and the results presented in this article can be applied to post-processed and pre-processed seismic data to increase accuracy in the quality and interpretability of the data, as well as save cost and time for hydrocarbon exploration. For decades, how to improve the resolution limit of seismic data has been a concern for seismologists. For this reason, several geoscientists have proposed various methods of improving the bandwidth of the data. Discovering an easy and scientifically reliable means of improving seismic data resolution would undoubtedly help geophysicists interpret more complex details of the subsurface geology. In this study, we transformed the bandlimited time-domain seismic data to the frequency domain using the Fourier analysis method, and a basis pursuit atomic algorithm was applied to decompose the real and imaginary parts of the spectrum into summations of cosines and sines. The resultant reflectivity spectrum (in the frequency domain) was deconvolved by a pre-estimated wavelet spectrum to obtain the true earth's reflectivity data spectrum and was subsequently extrapolated to beyond the original band limit. The result shows an extended bandwidth from 68 Hz to 161 Hz and 80 Hz to 170 Hz for both synthetic trace model and the main seismic data, respectively. Consequently, this improved vertical resolution of sub-seismic geologic features, such as crevasse splay, levee, barrier bar complex, lagoon inlet channels, alluvial fans, and fluvial channels, and shows subtle facies variations in Inas field. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar has emerged as a prominent non-destructive evaluation tool for the study of inaccessible subsurface elements of cultural heritage structures. Often of central interest is the desire to image the remains of a pre-existing historic structure that is located directly beneath a more recently built one. The interpretation of GPR images in such cases is usually difficult due to ambiguities caused by the presence of pervasive clutter, environmental noise, and overlapping target signatures. Sites with abundant ground truth allow for more confident interpretations and serve as a useful testbed to assist similar studies at other places, where little or no ground truth is available. This study reports GPR interpretations of structures belonging to the 19th century Citadel beneath the main prison cellhouse at Alcatraz. At this site, lidar scans, direct observations, and historical documents are available to facilitate identification of radar target signatures. A general interpretation of the acquired radargrams is made in this paper, while the companion paper presents more advanced analysis of target signatures based on curvelet image processing. This study points to the development of a radar facies classification scheme that is specific to cultural heritage investigations.

A microwave characterization technique to inspect subsurface scenarios is proposed and numerically assessed in this paper. The approach is based on a combination of finite element electromagnetic modeling and an inversion procedure in Lebesgue spaces with variable exponents. The former allows for description of the measurement system and subsurface scenario with high accuracy, while the latter exploits the adaptive definition of exponent function to achieve improved results in the regularized solution of the inverse scattering problem. The method has been assessed with numerical simulations regarding two-layered environments with both planar and non-planar air–soil interfaces. The results show the capabilities of the method of detecting buried objects in different operative conditions.

Ground-penetrating radar has emerged as a prominent non-destructive evaluation tool for the study of inaccessible subsurface elements of cultural heritage structures. Often of central interest is the desire to image the remains of a pre-existing historic structure that is located directly beneath a more recently built one. The interpretation of GPR images in such cases is usually difficult due to ambiguities caused by the presence of pervasive clutter, environmental noise, and overlapping target signatures. Sites with abundant ground truth allow for more confident interpretations and serve as a useful testbed to assist similar studies at other places, where little or no ground truth is available. This study reports GPR interpretations of structures belonging to the 19th century Citadel beneath the main prison cellhouse at Alcatraz. At this site, lidar scans, direct observations, and historical documents are available to facilitate identification of radar target signatures. A general interpretation of the acquired radargrams is made in this paper, while the companion paper presents more advanced analysis of target signatures based on curvelet image processing. This study points to the development of a radar facies classification scheme that is specific to cultural heritage investigations. [ABSTRACT FROM AUTHOR]

Traditional contaminated site characterisation approaches are time-consuming, labour-intensive, and demand a high level of expertise. This case study provides a rapid field-based solution to investigating a VOC contaminated site and its vapour incursion by combining soil vapour and groundwater survey. To fully assess the volatile organic compound (VOC) distribution in a contaminated site, a number of self-developed soil vapour sampling probes (SVSPs) were placed vertically at different locations in a grid with different depths. Hence, 3D subsurface contour maps for VOC concentrations in soil vapour can be obtained and used to help identify hot spots and the migration patterns of VOCs. This SVSP is “easy-to-install” in the field and a cost-effective solution for rapid assessment of soil vapour samples. The SVSPs can be installed both vertically and horizontally. If there is a requirement to take soil vapour samples beneath an existing building from a potential contamination source zone, SVSPs can be horizontally installed beneath the building without compromising its structural integrity. In addition, to ascertain the correct groundwater channels that are likely to carry contaminants from a potential source zone, an electrical resistivity tomography technique was employed to provide the preliminary information for groundwater delineation in a complex groundwater channel network.

In this letter, a hybrid analytical and numerical simulation method is presented for the time-efficient calculation of bistatic scattering from the buried targets. This computation is used for a fixed transmitter and target location but for a moving receiver to generate the data set for a 3-D synthetic aperture radar (SAR) simulation. In this method, initially, the current distribution on the target when illuminated by the transmitter (in the absence of the receiver) is calculated. Then, the receiver is used as a transmitter and its field is calculated everywhere in the lower half-space. The reciprocity is then used to find the open-circuit voltage across the receiver terminals due to the equivalent current distribution on the target. This approach substitutes N full-wave simulations for N sampling points required in SAR imaging with two simulations leading only to a factor of N/2 reduction in the simulation time. The proposed method is validated by comparing it with the brute-force full-wave simulation method as well as by image reconstruction from the data set generated by the method. [ABSTRACT FROM AUTHOR]

This letter presents an auto-focusing imaging algorithm for terahertz (THz) band imaging by extending the range point migration (RPM) method using a dielectric lens model. The traditional THz time-domain spectroscopy (THz-TDS) imaging system has an essential problem for depth dependence of the azimuth resolution, which becomes more crucial in subsurface imaging, because the depth of target is unknown in most cases. This letter thus introduces an RPM-based auto-focusing imaging method using an equivalent virtual source model to accurately represent both near- and far-range areas from the focal point. Experimental validation using THz-TDS demonstrated that the proposed algorithm accurately compensates image distortions by out-of-focus effect with much lower complexity than traditional radar-imaging approaches. [ABSTRACT FROM AUTHOR]

Subsurface imaging of Au circuit structures embedded in poly(methyl methacrylate) (PMMA) thin films with a cover thickness ranging from 52 to 653 nm was carried out by using contact resonance atomic force microscopy (CR-AFM). The mechanical difference of the embedded metal layer leads to an obvious CR-AFM frequency shift and therefore its unambiguous differentiation from the polymer matrix. The contact stiffness contrast, determined from the tracked frequency images, was employed for quantitative evaluation. The influence of various parameter settings and sample properties was systematically investigated by combining experimental results with theoretical analysis from finite element simulations. The results show that imaging with a softer cantilever and a lower eigenmode will improve the subsurface contrast. The experimental results and theoretical calculations provide a guide to optimizing parameter settings for the nondestructive diagnosis of flexible circuits. Defect detection of the embedded circuit pattern was also carried out, which indicates the capability of imaging tiny subsurface structures smaller than 100 nm by using CR-AFM.

We present a new method for the subsurface imaging of a conductive object embedded in a dielectric medium based on microwave imaging by thermo-elastic optical indicator microscopy. The present method is based on the imaging of the microwave near field distribution generated by a conductive object embedded in a dielectric medium. When the thickness of the dielectric medium is small so that the microwave near field generated by the conductive object can emerge from the dielectric medium, one can find the presence of the object by visualizing the microwave distribution appearing on the surface of the medium. By using the present method, we visualized the microwave field distribution appearing on a surface of a dielectric medium containing a conductive object with an irradiating microwave. We observed that strong microwave near fields that were present around the conductive object appeared on the surface of the dielectric medium, and their intensities were dependent on the frequency and polarization direction of the irradiated microwave. By visualizing various conductive lines having different lengths of 4~10 mm with a width of 1 mm, and buried depths of 1~5 mm, we showed that the present method can be a practical nondestructive and noncontact way for the detection of conductive lines and structures embedded in an optically opaque dielectric medium.

Due to the strong capability of building complex nonlinear mapping without involving linearization theory and high prediction efficiency; the deep learning (DL) technique applied to solve geophysical inverse problems has been a subject of growing interest. Currently, most DL-based inversion approaches are fully data-driven (namely standard deep learning), the performance of which largely depends on the training sample sets. However, due to the heavy burden of time and computational resources, it can be challenging to supply such a massive and exhaustive training dataset for generic realistic exploration scenarios and to perform network training. In this work, based on the recent advances in physics-based networks, the physical laws of magnetotelluric (MT) wave propagation is incorporated into a purely data-driven DL approach (PlainDNN) and thus builds a physics-driven DL MT inversion scheme (PhyDNN). In this scheme, the forward operator modeling MT wave propagation is integrated into the network training loop, in the form of minimizing a hybrid loss objective function composed of the data-driven model misfit and physics-based data misfit, to guide the network training. Consequently, the proposed PhyDNN method will take the advantage of the fully data-driven DL and conventional physics-based deterministic methods, allowing it to deal with complex realistic exploration scenarios. Quantitative and qualitative analysis results demonstrate that the PhyDNN can honor the physical laws of the MT inverse problem, and with other conditions unchanged, the PhyDNN outperforms the PlainDNN and the classical deterministic Occam inversion method. When processing field data, the PhyDNN method yields considerably impressive inversion results compared to the Occam method, and the corresponding simulated MT responses agree well with the real measurements, which confirms the effectiveness and applicability of the PhyDNN method.

Featured Application: The proposed device can be used to find underground utilities that have ferromagnetic properties and subsurface power-line cable carrying electrical current. We have constructed an imaging device that is capable of showing the spatio-temporal distribution of magnetic flux density in real-time. The device employs a set of AMR (anisotropic magneto-resistance) three-axis magnetometers, which are arranged into a two-dimensional sensor array. All of the magnetic field values measured by the array are collected by a microcontroller, which pre-processes and sends the data to a PDU (processing and display unit) implemented on a smartphone/tablet or a computer. The interpolation algorithm and display software in the PDU present the field as a high-resolution video; thus, the device works as a magnetic field camera. In the experiments, we employ the camera to map the field distribution of the distorted ambient magnetic field caused by a hidden object. The obtained image of field shows both the position and shape of the object. We also demonstrate the capability of the device to image a loaded power-line cable carrying a 50 Hz alternating current. [ABSTRACT FROM AUTHOR]

For decades, how to improve the resolution limit of seismic data has been a concern for seismologists. For this reason, several geoscientists have proposed various methods of improving the bandwidth of the data. Discovering an easy and scientifically reliable means of improving seismic data resolution would undoubtedly help geophysicists interpret more complex details of the subsurface geology. In this study, we transformed the bandlimited time-domain seismic data to the frequency domain using the Fourier analysis method, and a basis pursuit atomic algorithm was applied to decompose the real and imaginary parts of the spectrum into summations of cosines and sines. The resultant reflectivity spectrum (in the frequency domain) was deconvolved by a pre-estimated wavelet spectrum to obtain the true earth’s reflectivity data spectrum and was subsequently extrapolated to beyond the original band limit. The result shows an extended bandwidth from 68 Hz to 161 Hz and 80 Hz to 170 Hz for both synthetic trace model and the main seismic data, respectively. Consequently, this improved vertical resolution of sub-seismic geologic features, such as crevasse splay, levee, barrier bar complex, lagoon inlet channels, alluvial fans, and fluvial channels, and shows subtle facies variations in Inas field.

A hybrid method is proposed to reconstruct the subsurface 3-D objects with electromagnetic fields. The frequency-domain reverse time migration (FRTM) is first used to determine the approximate locations and sizes of the objects. Then, based on these results, the full-wave inversion, the variational Born iteration method (VBIM) is used to reconstruct both the shapes and dielectric parameters of the objects. The Monte Carlo method (MCM) is adopted to further refine the reconstructed shapes. Numerical simulations show that the proposed hybrid method can be effectively used for the subsurface imaging and detection. [ABSTRACT FROM AUTHOR]

Kirchhoff Time Migration method was applied in pre-and post-Stack Time Migration for post-processing of images collected from Balad-Samarra (BS-92) survey line that is sited across Ajeel anticline oilfield. The results showed that Ajeel anticline structure was relocated at the correct position in the migrated stacked section. The two methods (Pre and Post) of migration processing showed enhanced subsurface images and increased horizontal resolution, which was clear after the broadening the syncline and narrowing or compressing the anticline. However, each of these methods was associated with migration noise. Thus, a Post-Stack process was applied using Dip-Removal (DDMED) and Band-Pass filters to eliminate the artifact noise. The time-frequency and signal to noise spectrum analyses as well as the ISO-velocity distribution analysis confirmed that the Pre-Stack Time Migration method outperformed the Post-Stack method as a result of structural complexity. [ABSTRACT FROM AUTHOR]

The Natural Hazards Engineering Research Infrastructure (NHERI) experimental facility at the University of Texas (NHERI@UTexas) is funded by the National Science Foundation (NSF). NHERI@UTexas contributes unique, large-scale, hydraulically controllable mobile shakers and associated instrumentation to study and develop novel, in-situ testing methods that can be used to evaluate the needs of existing infrastructure as well as optimize the design of future infrastructure. The ability to test existing infrastructure under actual field conditions bridges the gap in the transformative tools needed for the next frontier of resilient and sustainable natural-hazards research. Further, these unique facilities are available to any NSF-funded research. The field shakers and support equipment are described. Examples of on-going and future projects in three key areas of investigation that NHERI@UTexas is targeting are presented. These examples includes: (1) performing more accurate 2D/3D subsurface geotechnical imaging up larger depths, (2) characterizing liquefaction resistance and non-linear dynamic behavior in situ soils, and (3) developing in-situ methods non-destructive soil-foundation-structure interaction (SFSI) studies.

In this paper, a microwave microscope for nondestructive evaluation of biological tissues using a near-field probe is described. For this purpose, a near-field probe to obtain microwave image with high resolution at 18.6 GHz is designed and the proposed method is experimentally evaluated in reflectometry imaging scenarios involving different tissues of raw chicken. We employ the technique to detect hidden sebaceous glands below the skin. In this regard, a 2D image of hidden sebaceous glands is provided using changes in the amplitude of reflection coefficient at resonant frequency. Measurement results show that the probe could accurately detect the hidden sebaceous glands with at least 20 dB magnitude and more than 600 MHz frequency shifts contrast. Also, the results show strong implications in a wide range of subsurface detection challenges including detection of biological anomalies in human tissues such as detection of small tumors below of human skin in the early-stage detection of skin cancers. [ABSTRACT FROM AUTHOR]

Understanding the cell–substrate interactions has great significance in tissue regeneration therapies. However, the cell–substrate interactions are not well understood because the interface of cell–substrate is typically buried beneath the cells. This research investigated the subsurfaces of fibroblasts cultured on hybrid nanoarrays using atomic force acoustic microscopy (AFAM). We fabricated hybrid silicon nanowires (SiNWs) and silicon nanoholes (SiNHs) on Si substrates to serve as biomimetic nanoarrays by employing laser interference lithography and the metal-assisted chemical etching (MacEtch) method. After the L929 cells were cultured on the nanoarrays, scanning electron microscopy (SEM) and AFAM were employed to investigate the surface and subsurface of L929 cells. It was suggested that fibroblasts could sense the morphology of the hybrid nanoarrays and membrane damage of fibroblasts on the hybrid nanoarrays were related to the nanostructures. This study can help guide the design of biointerfaces and provide a useful tool for the study of cell subsurfaces in diverse biological fields. [ABSTRACT FROM AUTHOR]

A novel ultrawideband directive antenna for ground-penetrating radar (GPR) applications is presented in this letter. The antenna is designed for operation at frequencies below 500 MHz to allow for high-depth-resolution imaging of the deeply buried targets such as buried pipes and other deep underground man-made structures such as tunnels. The antenna is formed by a pair of coupled antennas inspired by an electrically narrow very low profile (ENVELOP) antenna with differential feeding to provide a directive pattern normal to the antenna ground over a wide bandwidth. An antenna operating in the range of 140–510 MHz is designed and fabricated using the printed circuit board (PCB) substrates. The antenna is designed and optimized for operation in the proximity of a half-space soil medium to achieve efficient coupling of power from the air to the ground. The measured reflection coefficient of the antenna near concrete and wet soil covered by sand and grass is shown to be better than −9.5 dB within the specified band. The simulated realized gain of the antenna in the presence of dry soil is better than 4.9 dBi within the range of 140–510 MHz. The antenna is low-profile with a total height of 153 mm and a lateral dimension of 600 $\text {mm} \times 600$ mm. The performance of the antenna for GPR applications is evaluated through a field measurement of a buried object in wet soil with high losses. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a method is presented to detect the different phases of epoxy cross‐linking process and the subsurface structures of SU‐8 thin films by atomic force acoustic microscopy (AFAM). The AFAM imaging of SU‐8 thin films was investigated under different exposure and bake conditions. Optimized conditions were obtained for the cross‐linking of SU‐8 thin film at the exposure does of eight laser pulses with the laser fluence 10 mJ cm–2 per pulse and the post exposure bake (PEB) time at 90 s. The subsurface structures of undeveloped SU‐8 thin films were visible in the AFAM images. This method provides an effective and low‐cost way for the determination of different phases of epoxy cross‐linking process in nanostructured compounds, for the non‐destructive testing of subsurface defects, and for the evaluation of the quality of patterned structures. [ABSTRACT FROM AUTHOR]

We report recent improvements of the tip-on-gate of field-effect-transistor (ToGoFET) probe used for capacitive measurement. Probe structure, fabrication, and signal processing were modified. The inbuilt metal-oxide-semiconductor field-effect-transistor (MOSFET) was redesigned to ensure reliable probe operation. Fabrication was based on the standard complementary metal-oxide-semiconductor (CMOS) process, and trench formation and the channel definition were modified. Demodulation of the amplitude-modulated drain current was varied, enhancing the signal-to-noise ratio. The I-V characteristics of the inbuilt MOSFET reflect the design and fabrication modifications, and measurement of a buried electrode revealed improved ToGoFET imaging performance. The minimum measurable value was enhanced 20-fold.

We utilized scanning probe microscopy (SPM) based on a metal-oxide-silicon field-effect transistor (MOSFET) to image interdigitated electrodes covered with oxide films that were several hundred nanometers in thickness. The signal varied depending on the thickness of the silicon dioxide film covering the electrodes. We deposited a 400- or 500-nm-thick silicon dioxide film on each sample electrode. Thick oxide films are difficult to analyze using conventional probes because of their low capacitance. In addition, we evaluated linearity and performed frequency response measurements; the measured frequency response reflected the electrical characteristics of the system, including the MOSFET, conductive tip, and local sample area. Our technique facilitated analysis of the passivation layers of integrated circuits, especially those of the back-end-of-line (BEOL) process, and can be used for subsurface imaging of various dielectric layers.

We have constructed an imaging device that is capable of showing the spatio-temporal distribution of magnetic flux density in real-time. The device employs a set of AMR (anisotropic magneto-resistance) three-axis magnetometers, which are arranged into a two-dimensional sensor array. All of the magnetic field values measured by the array are collected by a microcontroller, which pre-processes and sends the data to a PDU (processing and display unit) implemented on a smartphone/tablet or a computer. The interpolation algorithm and display software in the PDU present the field as a high-resolution video; thus, the device works as a magnetic field camera. In the experiments, we employ the camera to map the field distribution of the distorted ambient magnetic field caused by a hidden object. The obtained image of field shows both the position and shape of the object. We also demonstrate the capability of the device to image a loaded power-line cable carrying a 50 Hz alternating current.

A subsurface interferometric synthetic aperture radar imaging technique is proposed for the detection and localization of clandestine underground facilities in the presence of a rough ground surface. The new approach consists of a two-step procedure: first, exploiting the distinctive scattering behaviors of the ground surface and the target, clutter suppression is performed with a polarimetric difference operation that does not alter the propagation phase of the target scattered signal, and then, a subsurface interferometric algorithm is applied to infer target depth by correlating the clutter-suppressed images obtained at two observation angles. The performance of the overall sensing algorithm for scene depth profile mapping from an airborne system is demonstrated with full-wave simulation data. [ABSTRACT FROM AUTHOR]

In most of the existing ground penetrating radar (GPR) imaging algorithms, using either full or sparse data collection, the ground is modeled as a single half-space layer. In this paper, a generalized sparse imaging approach with total variation minimization (TVM) for multiple-input multiple-output (MIMO) GPR imaging through multilayered subsurface is proposed. The multilayered media Green’s function is incorporated in the imaging algorithm to take into account the complex wave propagation effects under multilayered subsurface. An analytical expression of the layered subsurface Green’s function is derived using the stationary-phase method, which significantly reduces the computation time and complexity. On the other hand, as TVM minimizes the gradient of the image, its incorporation in the imaging algorithm results in a reconstruction that preserves the geometry and edges of the targets better than the standard L1-minimization-based sparsity-driven imaging. The number of antenna elements and frequency measurements in MIMO GPR system can be significantly reduced using the proposed technique without degradation of the image quality. Although MIMO configuration is investigated in this paper, the presented approach can be simply applied to monostatic synthetic aperture radar. [ABSTRACT FROM AUTHOR]

Background and aims: Root zone soil moisture is an important component in water cycling through the soil-plant-atmosphere continuum. However, its measurement in the field remains a challenge, especially non-invasively and repeatedly. Here, we developed a new method that uses ground-penetrating radar (GPR) to quantify root zone soil moisture.Methods: Coarse roots were chosen as reflectors to collect GPR radargrams. An automatic hyperbola detection algorithm identified coarse root reflections in GPR radargrams and determined the velocity of GPR wave, which then was used to calculate the average soil water content of a soil profile (ASWC) and soil water content in a depth interval (ISWC). In total, GPR reflection data of 55 root samples from three computer simulation scenarios and two field experiments in sandy shrubland, one burying roots at known depths and the other under the undisturbed condition, were used to evaluate the proposed method.Results: Both the simulated and the field collected data demonstrated the effectiveness of the proposed method for measuring root zone soil moisture with high accuracy. Even in the two field experiments, the root-mean-square errors of the estimated ASWC and ISWC relative to measurements from soil cores were as low as 0.003 and 0.012 m3·m−3, respectively.Conclusion: The proposed method offers a new way of quantifying root zone soil moisture non-invasively that allows repeated measurements. This study expands the application of GPR in root and soil study and enhances our ability to monitor plant-soil-water interactions. [ABSTRACT FROM AUTHOR]

Sumatra subduction zone is one of the most seismically active zones on Earth. After having produced three Mw > 8.4 earthquakes and several Mw > 7.5 earthquakes, including the Mw = 7.8 2010 tsunami earthquake, the northern Mentawai segment is still locked and is capable of generating a great earthquake, possibly a disastrous tsunami. We analyzed ultralong offset seismic reflection data from this locked zone to characterize the nature of the accretionary prism and the plate interface using a combination of traveltime tomography, full waveform inversion, and prestack depth migration. In order to enhance the refractions, we downward extrapolate the streamer data to the seafloor, allowing the refraction arrivals to be observed from near‐zero offset up to far offset, and use a traveltime tomography to determine the background velocity in the upper sediments. Starting from these velocities, we perform a multiscale elastic full waveform inversion to determine the detailed P wave velocity structure of the subsurface. Based on this velocity, we perform a prestack depth migration to obtain seismic image in the depth domain and compute the porosity of the sediments to determine fluid content along faults. Our results show a low‐velocity subduction channel with high porosity at the plate interface that connects the likely active frontal thrusts at the toe of accretionary wedge, suggesting that the frontal section of the prism is seismogenic. We have also observed a low‐velocity layer in the middle of wedge separating old sediments below from new sediments above, defining the roots of bivergent thrust faults up to the seafloor, which can be interpreted as a psudo‐décollement. Key Points: Elastic full waveform inversion is applied to long streamer data at the subduction front in the Mentawai locked zoneCombination of downward extrapolation data and surface streamer data following the frequency continuation strategy is efficient during inversionActive frontal thrusts and low‐velocity décollement at the plate interface relate to tsunami earthquake [ABSTRACT FROM AUTHOR]

Subsurface imaging in arid regions is a well-known application of satellite Synthetic Aperture Radar (SAR). Archaeological prospection has often focused on L-band SAR sensors, given the ability of longer wavelengths to penetrate more deeply into sand. In contrast, this study demonstrates capabilities of shorter-wavelength, but higher spatial resolution, C-band and X-band SAR sensors in archaeological subsurface imaging at the site of 'Uqdat al-Bakrah (Safah), Oman. Despite having varying parameters and acquisitions, both the X-band and C-band images analyzed were able to identify a subsurface paleo-channel that is not visible on the ground surface. This feature was first identified through Ground Penetrating Radar (GPR) survey, then recognized in the SAR imagery and further verified by test excavations. Both the GPR and the excavations reveal the base of the paleo-channel at a depth of 0.6 m-0.7 m. Hence, both X-band and C-band wavelengths are appropriate for subsurface archaeological prospection in suitable (dry silt and sand) conditions with specific acquisition parameters. Moreover, these results offer important new insights into the paleo-environmental context of ancient metal-working at 'Uqdat al-Bakrah and demonstrate surface water flow roughly contemporary with the site's occupation. [ABSTRACT FROM AUTHOR]

We present a novel method of using explainability techniques to design physics-aware convolutional neural networks (CNNs). We demonstrate our approach by developing a CNN for solving an inverse problem for shallow subsurface imaging. Although CNNs have gained popularity, the development of CNNs remains an art, as there are no clear guidelines regarding the selection of hyperparameters that provide the best network. While optimization algorithms can select hyperparameters automatically, these methods develop networks with high predictive accuracy while disregarding model explainability (descriptive accuracy). The field of Explainable Artificial Intelligence (XAI) addresses the absence of model explainability by providing tools to evaluate the internal logic of networks. In this study, we use the explainability methods Score-CAM and Deep SHAP to select hyperparameters (e.g., kernel size and network depth) to develop a physics-aware CNN for shallow subsurface imaging. We begin with an Encoder-Decoder network, which uses surface wave dispersion images to generate 2D shear wave velocity images. Through model explanations, we find that a shallow CNN using two convolutional layers with an atypical kernel size of 3 × 1 yields comparable predictive accuracy but increased descriptive accuracy. We believe this method can be used to develop networks with high predictive accuracy while providing inherent explainability. [ABSTRACT FROM AUTHOR]