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Machine learning‐based approaches demonstrate a significant potential in radar quantitative precipitation estimation (QPE) applications. In contrast to conventional methods that depend on local raindrop size distributions, deep learning (DL) can establish an effective mapping from three‐dimensional radar observations to ground rain rates. However, the lack of transparency in DL models poses challenges toward understanding the underlying physical mechanisms that drive their outcomes. This study aims to develop a DL‐based QPE system and provide a physical explanation of radar precipitation estimation process. This research is designed by employing a deep neural network consisting of two modules. The first module is a quantitative precipitation estimation network that has the capability to learn precipitation patterns and spatial distribution from multidimensional polarimetric radar observations. The second module introduces a quantitative precipitation estimation shapley additive explanations method to quantify the influence of each radar observable on the model estimate across various precipitation intensities. Plain Language Summary: Ground radars can provide continuous spatial observations over large areas with high spatiotemporal resolutions, so they form the infrastructure for precipitation monitoring and observation in many countries. Recently, deep learning (DL) techniques have shown great potential for use in polarimetric radar‐based precipitation estimates. Nevertheless, the black‐box and turn‐key characteristics of DL models make it difficult for researchers to understand the model decision‐making process and cast doubt on the reliability of the model results. This study introduces a physically explainable polarization radar‐based quantitative precipitation estimation (QPE) system built on DL technology that can explain the causes of the precipitation estimates provided by deep learning models under different rainfall amounts. An experiment indicates that our model achieves better estimates than the conventional methods. Furthermore, the explainability methodology allows for visualization of the microphysical precipitation information. Being the initial attempt to apply explainability learning in the QPE domain, the explainability results may offer valuable guidance for rainfall estimation. Key Points: A polarimetric radar‐based rainfall estimation system is developed using deep neural networksThe deep learning‐based rainfall estimates generally outperform products derived from traditional parametric relationsThe proposed deep learning interpretation method can provide physical and statistical explanations of the model decision‐making process [ABSTRACT FROM AUTHOR]

The article discusses the western scarp of the Tausret temple site in Egypt, which was likely carved out during Amenhotep III's clearing for his memorial temple and later modified during the building of Tausret's temple. The scarp was reused during the Third Intermediate Period and the Late Period for tomb cutting, and excavations have uncovered rock-cut tombs, mud-brick walls, faience beads, and an offering table. The study focuses on the ceramic assemblage in the western scarp region, which contains a mix of ceramics from different time periods due to it being used as a dump for items removed from surrounding tombs. The article also discusses the findings from the inner and outer courtyards of the tomb site, including human remains, animal remains, linen fragments, cartonnage and coffin fragments, faience beads, and ceramics. Two tombs, Feature 6 and the Swiss Tomb, are described, with limited cultural material found in both. The western scarp contains more Late Period material, while the northern scarp contains more Third Intermediate Period material. The document is a list of references and sources related to the excavation and study of Pharaoh-Queen Tausret's Temple of Millions of Years, providing a comprehensive list of sources for further research. [Extracted from the article]

Ground Penetrating Radar (GPR) systems mounted on Unmanned Aerial Vehicles (UAVs) are becoming a promising solution to inspect beneath the soil surface. In particular, resorting to Synthetic Aperture Radar (SAR) techniques, these systems are able to retrieve subsurface radar images, in which buried targets can be detected. However, one of the major bottlenecks of this technology is the significant amount of time required for computing the radar images (due to the usage of computationally expensive algorithms to handle non-regular acquisition domains). To face this challenge, this work proposes an efficient interpolation approach, combining a height shift operation with a 2D interpolation, that enables the adoption of a migration algorithm called Phase Shift Migration (PSM). As a result, the processing time is drastically improved, offering close to real-time operation. The proposed methodology has been validated in a realistic scenario for detecting buried explosive targets, comparing it with conventional backprojection techniques in terms of image quality and processing time. [Display omitted] [ABSTRACT FROM AUTHOR]

The occurrence of subsurface karst caves can cause the development of superficial depressions which, in turn, may pose a construction hazard. Identifying such a substratum requires integrated non-invasive measurement methods. The main objective of the study was to demonstrate the effectiveness of the non-invasive ERT, TLS, and GPR survey techniques in identifying the karst floor and determining the direction of discontinuities around the cave. The paper analyzes the limitations of the methods used in the study of heterogeneous media. These limitations are related to the methodology and measurement conditions, data processing, and interpretation in the context of the resolution and depth range. The study was conducted using the example of the Jaskinia pod Świecami cave, formed in the Sarmatianal calcarenites in Poland. The research confirmed its complex karst-anthropogenic genesis. The cave was formed as a result of the infiltration of rainwater and the dissolution of limestone by groundwater, while the paleokarst forms that are characteristic of it and of the surrounding caves and occur in their vicinity, i.e., narrow ridges called "karst candles", were formed as a result of water circulation during the local permafrost degradation in the middle Pleistocene. However, these forms were modified in the Upper Pleistocene and Holocene, as indicated by ERT images. [ABSTRACT FROM AUTHOR]

Micro‐Doppler effect is a vital feature of a target that reflects its oscillatory motions apart from bulk motion and provides an important evidence for target recognition with radars. However, establishing the micro‐Doppler database poses a great challenge, since plenty of experiments are required to get the micro‐Doppler signatures of different targets for the purpose of analyses and interpretations with radars, which are dramatically limited by high cost and time‐consuming. Aiming to overcome these limits, a low‐cost and powerful simulation platform of the micro‐Doppler effects is proposed based on time‐domain digital coding metasurface (TDCM). Owing to the outstanding capabilities of TDCM in generating and manipulating nonlinear harmonics during wave‐matter interactions, it enables to supply rich and high‐precision electromagnetic signals with multiple micro‐Doppler frequencies to describe the micro‐motions of different objects, which are especially favored for the training of artificial intelligence algorithms in automatic target recognition and benefit a host of applications like imaging and biosensing. [ABSTRACT FROM AUTHOR]

This paper presents the current state of conservation of historic roof churches located in the Żuławy of Gdańsk (Poland). It also describes the architecture of these temples, the region itself and old carpentry techniques for constructing roof trusses. Interdisciplinary tests were carried out in six churches. The geometry of the load bearing structures, the moisture content and the carpentry technique were specified. The field survey also included visual inspections and non-destructive testing of timber structural elements of the roof constructions. The ground penetrating radar and ultrasonic testing methods were used to assess the structure and extent of the damage to the timber elements. The interdisciplinary research presented in this article is important in the planning of historic buildings conservation works and it might be applied to other timber structures. [ABSTRACT FROM AUTHOR]

The paper deals with a combined time–depth conversion strategy able to improve the reconstruction of voids embedded in an opaque medium, such as cavities, caves, empty hypogeal rooms, and similar targets. The combined time–depth conversion accounts for the propagation velocity of the electromagnetic waves both in free space and in the embedding medium, and it allows better imaging and interpretation of the underground scenario. To assess the strategy's effectiveness, ground penetrating radar (GPR) data referred to as an experimental test in controlled conditions are accounted for and processed by two different approaches to achieve focused images of the scenario under test. The first approach is based on a classical migration algorithm, while the second one faces the imaging as a linear inverse scattering approach. The results corroborate that the combined time–depth conversion improves the imaging in both cases. [ABSTRACT FROM AUTHOR]

The rapid advancement toward smart cities has accelerated the adoption of various Internet of Things (IoT) devices for underground applications, including agriculture, which aims to enhance sustainability by reducing the use of vital resources such as water and maximizing production. On-farm IoT devices with above-ground wireless nodes are vulnerable to damage and data loss due to heavy machinery movement, animal grazing, and pests. To mitigate these risks, wireless Underground Sensor Networks (WUSNs) are proposed, where devices are buried underground. However, implementing WUSNs faces challenges due to soil heterogeneity and the need for low-power, small-size, and long-range communication technology. While existing radio frequency (RF)-based solutions are impeded by substantial signal attenuation and low coverage, acoustic wave-based WUSNs have the potential to overcome these impediments. This paper is the first attempt to review acoustic propagation models to discern a suitable model for the advancement of acoustic WUSNs tailored to the agricultural context. Our findings indicate the Kelvin–Voigt model as a suitable framework for estimating signal attenuation, which has been verified through alignment with documented outcomes from experimental studies conducted in agricultural settings. By leveraging data from various soil types, this research underscores the feasibility of acoustic signal-based WUSNs. [ABSTRACT FROM AUTHOR]

The S-transform is a fundamental time–frequency (T-F) domain analysis method in ground penetrating radar (GPR) data processing and can be used for identifying targets, denoising, extracting thin layers, and high-resolution imaging. However, the S-transform spectrum experiences energy leakage near the instantaneous frequency. This phenomenon causes frequency components to erroneously spread over a wider range, impacting the accuracy and precision of GPR data processing. Synchrosqueezing is an effective method to prevent spectrum leakage. In this work, we introduce the synchrosqueezing generalized phase-shifting S-transform (SS-GPST). Initially, it resolves the compatibility issue between the S-transform and the synchrosqueezing strategy through phase-shifting. Subsequently, the SS-GPST accomplishes spectral energy focusing and resolution enhancement via a generalized parameter and synchrosqueezing. A synthetic signal test shows that the SS-GPST excels over other methods at focusing degree, spectral resolution, and signal reconstruction accuracy and speed. In actual GPR tunnel detection data processing, we assess the adaptability of the SS-GPST from three aspects: spectral energy distribution, thin layer identification, and data denoising. The results indicate: (1) compared to other methods, the SS-GPST accurately expresses spectral components with a strong focusing degree and fewer interference components; (2) high-frequency slices of the SS-GPST accurately detect the top and bottom interfaces of a 3.0–3.5 cm reinforcement protection layer; and (3) due to fewer interference components in the SS-GPST spectrum, reconstructing GPR profiles through the SS-GPST inverse transform is an efficient denoising technique. The SS-GPST demonstrates adaptability to different data processing purposes, offers high-resolution T-F spectra, and shows potential to supersede the S-transform. [ABSTRACT FROM AUTHOR]

Timely and accurate extraction of pavement crack information is crucial to maintain service conditions and structural safety for infrastructures and reduce further road maintenance costs. Currently, deep learning techniques for automated pavement crack detection are far superior to traditional manual approaches in both speed and accuracy. However, existing deep learning models may easily lose crack details when processing images containing complex background textures or other noises. Although many studies have alleviated this challenge by introducing attention mechanisms, especially the non‐local (NL) block, which has the ability to efficiently capture long‐range dependencies to facilitate crack pixel capture, the huge computational cost of NL makes the inference time of the model too long, which is not conducive to practical implementation. In this study, a new module, namely, the pyramid region attention module (PRAM), was developed by combining the pyramid pooling module in the pyramid scene parsing network and optimized NL, which can achieve global multi‐scale context integration and long‐range dependencies capture at a relatively lower computational cost. By applying PRAM to deep skip connections in the modified U‐Net, an effective crack segmentation model called CrackResU‐Net was developed. The test results on the existing CrackForest dataset showed that CrackResU‐Net not only achieved an F1 score of 0.9580 but also took only 25.89 ms to process an image with a resolution of 480 × 320, which had advantages in accuracy and speed, compared with several other state‐of‐the‐art crack segmentation approaches. It was fully demonstrated that this approach could realize automatic fast and high‐precision recognition of pavement cracks for engineering purposes. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) systems are effective sensors for discovering various types of objects buried underground, such as military mines, metal objects, and pieces of underground infrastructures. A GPR system can be manually operated by a human or can be an integral part of a host platform. The host platform may be semi- or fully autonomous and may operate in different environments such as land vehicles or more recently air-borne drones. One challenge for the fully or semi-autonomous host platforms in particular is to find an efficient search procedure that would reduce the operation time and optimize resource utilization. Most of the current approaches are based on pre-defined search patterns which, for large and sparse areas, could mean unnecessary waste of time and resources. In this paper, we introduce a method that combines a coarse and therefore relatively low cost initial search pattern with a Reinforcement Learning (RL) driven efficient navigation path for eventual target detection, by exploiting the signal processing pipeline of the onboard GPR. We illustrate the applicability of the method using a well-known, high fidelity GPR simulation environment and a novel RL framework. Our results suggest that combination of a coarse navigation scheme and an RL-based training procedure based on GPR scan returns can lead to a more efficient target discovery procedure for host platforms. [ABSTRACT FROM AUTHOR]

Since 2021, a new surge in discrete aurora detections at Mars has been observed by the Emirates Mars Ultraviolet Spectrometer (EMUS) onboard the Emirates Mars Mission (EMM) Hope Orbiter as EMUS started to regularly obtain synoptic auroral images with a high sensitivity. Here we report on a fortuitous conjunction between EMM and Mars Express (MEX) using far ultraviolet (FUV) imaging of discrete aurora by EMM EMUS, in situ measurements of suprathermal electrons by the MEX Analyzer of Space Plasma and Energetic Atoms Electron Spectrometer (ELS), and topside radar sounding of the nightside ionosphere by the MEX Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS). In this event, EMM EMUS imaged a clear discrete aurora signature around moderately strong crustal magnetic fields on the nightside near the dusk terminator, 11 min before which MEX MARSIS measured a prominent local enhancement of the peak electron density in the nightside ionosphere and MEX ELS observed an in situ enhancement of suprathermal electrons at the corresponding location. A remarkable geographic agreement is found between the enhancements of the aurora, ionosphere, and suprathermal electrons, suggesting that the enhanced ionization and auroral emission are caused concurrently by precipitating suprathermal electrons. Subsequent images indicate that the discrete aurora slightly changed its shape in 15 min and mostly disappeared in a few hours. The MEX MARSIS measurements of the auroral ionosphere display overlapping ionospheric and surface echoes indicative of horizontal gradients of the peak electron density. Analysis of the overlapping echoes implies that the auroral ionosphere and electron precipitation could be highly structured with horizontal spatial scales on the order of several tens of km. MEX MARSIS also observed a non-auroral ionospheric enhancement with a wider spatial extent than the local auroral enhancement, suggesting alternative sources of the enhanced nightside ionosphere such as plasma transport. The comparison between the ionospheric structures measured by MEX MARSIS, suprathermal electron flux measured by MEX ELS, and discrete auroral emission imaged by EMM EMUS underscores the complexity of the auroral and non-auroral nightside ionospheres. This motivates further investigations of their sources, transport, and connections to the magnetotail dynamics of Mars. [ABSTRACT FROM AUTHOR]

Background: Stewarding newly planted urban trees to maturity involves consideration of above- and belowground factors. While landscape architects and urban planners often focus on aboveground tree aspects, understanding root structure and function is crucial, especially in urban areas with limited underground space and diverse soil conditions. To address the importance and challenges of belowground planning for urban tree roots, we propose a protocol for site assessment and demonstrate root growth forecasting as a complement to existing urban planning approaches. Methods: This paper describes adaptations to a plant root architecture simulator, RootBox, and its subsequent application in 4 scenarios created to assess its effi cacy as a complement to the phase of urban planning that prescribes vegetation type and planting location in the context of a myriad of other site considerations. RootBox was parameterized based on observed conformity of root growth simulations to generalized tree root architecture reported in the literature. Results: Root growth forecasts for each scenario illustrate that plausible urban tree root system architectures--specifi cally, commonly observed root growth habits--can be produced by RootBox. In-situ root detection (e.g., with an air spade or hydro-vac) at predetermined time intervals can provide model validation and the opportunity to backward reconfi gure RootBox parameters for forecast accuracy. Conclusion: The adapted RootBox is a fl exible, proof-of-concept solution for predicting urban tree root architecture, considering the site and soil conditions pre-established in the planning process. When used with other relevant tools, RootBox can offer valuable insights during the design or rejuvenation of urban spaces. [ABSTRACT FROM AUTHOR]

With the rapidly growing interest in using structural timber, a need exists to inspect and assess these structures using non-destructive testing (NDT). This review article summarizes NDT methods for wood inspection. After an overview of the most important NDT methods currently used, a detailed review of Ground Penetrating Radar (GPR) and Ultrasonic Testing (UST) is presented. These two techniques can be applied in situ and produce useful visual representations for quantitative assessments and damage detection. With its commercial availability and portability, GPR can help rapidly identify critical features such as moisture, voids, and metal connectors in wood structures. UST, which effectively detects deep cracks, delaminations, and variations in ultrasonic wave velocity related to moisture content, complements GPR's capabilities. The non-destructive nature of both techniques preserves the structural integrity of timber, enabling thorough assessments without compromising integrity and durability. Techniques such as the Synthetic Aperture Focusing Technique (SAFT) and Total Focusing Method (TFM) allow for reconstructing images that an inspector can readily interpret for quantitative assessment. The development of new sensors, instruments, and analysis techniques has continued to improve the application of GPR and UST on wood. However, due to the hon-homogeneous anisotropic properties of this complex material, challenges remain to quantify defects and characterize inclusions reliably and accurately. By integrating advanced imaging algorithms that consider the material's complex properties, combining measurements with simulations, and employing machine learning techniques, the implementation and application of GPR and UST imaging and damage detection for wood structures can be further advanced. [ABSTRACT FROM AUTHOR]

This contribution focuses on the detection of tracer particles within non-homogeneous bulk media, aiming to enhance insights into particulate systems. Polarimetric radar measurements are employed, utilizing cross-polarizing channels in order to mitigate interference from bulk media reflections. To distinguish the tracer particle in the measurements, a resonant cross-polarizing structure is constructed, facilitating the isolation of frequency signatures from the surrounding bulk clutter. In addition to characterizing the bulk and tracer components, this study provides a detailed presentation and discussion of the measurement setup, along with the employed signal processing methods. The effectiveness of the proposed methods is demonstrated through comprehensive measurements, where a tracer particle is systematically positioned at various locations. The results affirm the feasibility and efficacy of the approach, highlighting its applicability for enhanced dynamic monitoring in particulate systems within industrial processes. [ABSTRACT FROM AUTHOR]

Fusion-based imaging using ground-penetrating radar (GPR) and ultrasonic echo array (UEA) was employed to track damage progression in the columns of two full-scale reinforced concrete (RC) bridge column-footing subassembly laboratory specimens. The specimens had different lap-splice detailing and were subjected to reverse-cyclic lateral loading simulating a subduction zone earthquake. GPR and UEA scans were performed on the east and west faces of the columns at select ductility levels. Reconstructed images were obtained using the extended total focusing method (XTFM) and fused using a wavelet-based technique. Composite images of each column's interior were created by merging the images from both sides. A quantitative analysis based on the structural similarity (SSIM) index accurately captured damage progression. A backwall analysis using the amplitude of the backwall reflector was also performed. Changes as early as in the first measurement (μ = 0.5 displacement ductility level) could be detected. Damage variation along the column height was observed, consistent with greater damage at the base. The proposed analyses distinguished the structural behavior differences between the two specimens. In summary, the SSIM metric provides a valuable tool for detecting changes, while the backwall analysis offers simple yet informative insights into damage progression and distribution in full-scale RC members. [ABSTRACT FROM AUTHOR]

Three-Dimensional Ground Penetrating Radar (3D GPR) detects subsurface targets non-destructively, rapidly, and continuously. The complex environment around urban roads affects the positioning accuracy of 3D GPR. The positioning accuracy directly affects the data quality, as inaccurate positioning can lead to distortion and misalignment of 3D GPR data. This paper proposed a multi-level robust positioning method to improve the positioning accuracy of 3D GPR in dense urban areas in order to obtain more accurate underground data. In environments with good GNSS signals, fast and high-precision positioning can be achieved based on GNSS data using differential GNSS technology; in scenes with weak GNSS signals, high-precision positioning of subsurface data can be achieved by using GNSS and IMU as well as using GNSS/INS tightly coupled solution technology; in scenes with no GNSS signals, SLAM technology is used for positioning based on INS data and 3D point cloud data. In summary, this method ensures a positioning accuracy of 3D GPR better than 10 cm and high-quality 3D images of underground urban roads in any environment. This provides data support for urban road underground structure surveys and has broad application prospects in underground disease detection and prevention. [ABSTRACT FROM AUTHOR]

The rapid and accurate detection of forest disturbances in temperate forests has become increasingly crucial as policy demands and climate pressure on these forests rise. The cloud-penetrating Sentinel-1 radar constellation provides frequent and high-resolution observations with global coverage, but few studies have assessed its potential for mapping disturbances in temperate forests. This study investigated the sensitivity of temporally dense C-band backscatter data from Sentinel-1 to varying management-related disturbance intensities in temperate forests, and the influence of confounding factors such as radar backscatter signal seasonality, shadow, and layover on the radar backscatter signal at a pixel level. A unique network of 14 experimental sites in the Netherlands was used in which trees were removed to simulate different levels of management-related forest disturbances across a range of representative temperate forest species. Results from six years (2016–2022) of Sentinel-1 observations indicated that backscatter seasonality is dependent on species phenology and degree of canopy cover. The backscatter change magnitude was sensitive to medium- and high-severity disturbances, with radar layover having a stronger impact on the backscatter disturbance signal than radar shadow. Combining ascending and descending orbits and complementing polarizations compared to a single orbit or polarization was found to result in a 34% mean increase in disturbance detection sensitivity across all disturbance severities. This study underlines the importance of linking high-quality experimental ground-based data to dense satellite time series to improve future forest disturbance mapping. It suggests a key role for C-band backscatter time series in the rapid and accurate large-area monitoring of temperate forests and, in particular, the disturbances imposed by logging practices or tree mortality driven by climate change factors. [ABSTRACT FROM AUTHOR]

We applied an interdisciplinary approach to analyze the late Quaternary activity of the Sava Fault in the Slovenian Southern Alps. The Sava Fault is an active strike-slip fault, and part of the Periadriatic Fault System that accommodated the convergence of Adria and Europe. It is one of the longest faults in the Southern Alps. Using high-resolution digital elevation models from lidar and photogrammetric surveys, we were able to overcome the challenges of assessing fault activity in a region with intense surface processes, dense vegetation, and relatively low fault slip rates. By integrating remote sensing analysis, geomorphological mapping, structural geological investigations, and near-surface geophysics (electrical resistivity tomography and ground penetrating radar), we were able to find subtle geomorphological indicators, detect near-surface deformation, and show distributed surface deformation and a complex fault pattern. Using optically stimulated luminescence dating, we tentatively estimated a slip rate of 1.8 ± 0.4 mm/a for the last 27 ka, which exceeds previous estimates and suggests temporal variability in fault behavior. Our study highlights the importance of modern high-resolution remote sensing techniques and interdisciplinary approaches in detecting tectonic deformation in relatively low-strain rate environments with intense surface processes. We show that slip rates can vary significantly depending on the studied time window. This is a critical piece of information since slip rates are a key input parameter for seismic hazard studies. [ABSTRACT FROM AUTHOR]

In near-surface surveys, shallow-seismic and ground-penetrating radar (GPR) full-waveform inversions (FWIs) have received increasing attention because of their ability to reconstruct high-resolution subsurface models. However, they have different sensitivities to the same targets and thus may yield conflicting geophysical parameter models. To solve this issue, we have developed an indirect joint petrophysical inversion (JPI) integrating shallow-seismic and multi-offset GPR data. These data are used to reconstruct porosity and saturation whereby we use only strong sensitivities between petrophysical and geophysical parameters. To promote its field application, we proposed an input strategy to avoid measuring rock matrix parameters and make indirect JPI more robust. We apply indirect JPI to the field data acquired in Rheinstetten, Germany and find that it reveals the mechanical, electrical and petrophysical properties more reliably than individual inversions. The reconstructed models are assessed by direct-push technology, borehole sample measurements and migrated GPR image. Indirect JPI can fit seismic and GPR observed data simultaneously and provide consistent multiparameter models, which are hard to achieve by FWIs and individual petrophysical inversions. We also find that the method is robust when there are uncertainties in petrophysical a priori information. Overall, the field example proves the great potential of using indirect JPI to solve real-world problems. [ABSTRACT FROM AUTHOR]

In the context of Bayesian inversion, we consider sequential Monte Carlo (SMC) methods that provide an approximation of the posterior probability density function and the evidence (marginal likelihood). These particle approaches build a sequence of importance sampling steps between gradually tempered distributions evolving from the prior to the posterior PDF. To automate the definition of the tempering schedule, adaptive SMC (ASMC) allows tuning the temperature increments on-the-go. One general challenge in Bayesian inversions is the computational burden associated with expensive, high-fidelity forward solvers. Lower-fidelity surrogate models are interesting in this context as they can emulate the response of expensive forward solvers at a fraction of their cost. We consider surrogate modelling within ASMC and introduce first an approach involving surrogate modelling only, in which either prior samples are used to train the surrogate, or the surrogate model is retrained by updating the training set during the inversion. In our implementation, we rely on polynomial chaos expansions for surrogate modelling, principal component analysis for model parametrization and a ground-penetrating radar cross-hole tomography problem with either an eikonal or finite-difference time-domain solver as high-fidelity solver. We find that the method based on retraining the surrogate during the inversion outperforms the results obtained when only considering prior samples. We then introduce a computationally more expensive multifidelity approach including a transition to the high-fidelity forward solver at the end of the surrogate-based ASMC run leading to even more accurate results. Both methods result in speed-ups that are larger than one order of magnitude compared to standard high-fidelity ASMC inversion. [ABSTRACT FROM AUTHOR]

The structure and origin of the Pleistocene (Marine Isotope Stage [MIS] 5) coastal Barrier III in southern Brazil were investigated through analysis of lithofacies, numerical ages, and ground-penetrating radar (GPR) data obtained in outcrops and subsurface deposits. The stratigraphic succession is characteristic of transgressive barriers, with muddy lagoon bottom facies unconformably overlying an older unit (Barrier II) and overlain by landward-dipping lagoon margin and aeolian facies. The back-barrier lagoon was filled with sediments and shells transferred from the foreshore through overwash and/or inlets during the MIS 5e transgressive-high-stand phase, with a higher sea level that reached about +6 to +7 m relative to the present. Marine sediments and shells on the seaward side of the barrier dated to ~100–106 ka indicate another high stand at +4 to +5.1 m during MIS 5c. One shell dated to ~87 ka and aeolian deposits dated to ~82 and ~85 ka suggest a third high stand during MIS 5a that reached at least −2 m relative to the present. The two (possibly three) juxtaposed marine deposits show that Barrier III is a more complex unit than previously recognized, built by successive orbitally forced eustatic sea-level oscillations also recorded in other deposits along the Brazilian coast and worldwide. [ABSTRACT FROM AUTHOR]

Many fields of human activity benefit from the ability to create images of obscured objects placed behind walls and to map their displacement in a noninvasive way. Usually, imaging devices like Synthetic Aperture Radars (SARs) and Ground-Penetrating Radars (GPRs) use expensive dedicated electronics which results in prohibitive prices. This paper presents the experimental implementation and the results obtained from an imaging system capable of performing SAR imaging and interferometric displacement mapping of targets located behind walls, as well as 3D GPR imaging using a low-cost general-purpose radar sensor. The proposed solution uses for the RF section of the system a K-band microwave radar sensor module implementing Frequency-Modulated Continuous Wave (FMCW) operation. The low-cost sensor was originally intended for simple presence detection and ranging for domestic applications. The proposed system was tested in several scenarios and proved to operate as intended for a fraction of the cost of a commercial imaging device. In one scenario, it was able to detect and locate a 15 cm-diameter fire-extinguisher located at a distance of 3.5 m from the scanning system and 1.6 m behind a 3 cm-thick MDF (medium-density fiberboard) wall with cm-level accuracy. In a second test, the proposed system was used to perform interferometric displacement measurements, and it was capable of determining the displacement of a metal case with sub-millimeter accuracy. In a third experiment, the system was used to construct a 3D image of the inside of a wood table with cm-level resolution. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR) faces complex challenges in identifying underground rock formations and lithological structures. The diversity, intricate shapes, and electromagnetic properties of subsurface rock formations make their accurate detection difficult. Additionally, the heterogeneity of subsurface media, signal scattering, and non-linear propagation effects contribute to the complexity of signal interpretation. To address these challenges, this study fully considers the unique advantages of convolutional neural networks (CNNs) in accurately identifying underground rock formations and lithological structures, particularly their powerful feature extraction capabilities. Deep learning models possess the ability to automatically extract complex signal features from radar data, while also demonstrating excellent generalization performance, enabling them to handle data from various geological conditions. Moreover, deep learning can efficiently process large-scale data, thereby improving the accuracy and efficiency of identification. In our research, we utilized deep neural networks to process GPR signals, using radar images as inputs and generating structure-related information associated with rock formations and lithological structures as outputs. Through training and learning, we successfully established an effective mapping relationship between radar images and lithological label signals. The results from synthetic data indicate a rock block identification success rate exceeding 88%, with a satisfactory continuity identification of lithological structures. Transferring the network to measured data, the trained model exhibits excellent performance in predicting data collected from the field, further enhancing the geological interpretation and analysis. Therefore, through the results obtained from synthetic and measured data, we can demonstrate the effectiveness and feasibility of this research method. [ABSTRACT FROM AUTHOR]

A geophysical exploration employing ground‐penetrating radar (GPR) and electrical resistivity tomography (ERT) was conducted at the Western Cemetery, Giza, Egypt, in 2021–23 by a joint research team of Higashi Nippon International University, Tohoku University and the National Research Institute of Astronomy and Geophysics (NRIAG), Helwan, Egypt. We believe we found an anomaly: a combination of a shallow structure connected to a deeper structure. The shallow structure, which is L‐shaped in the horizontal plane, 10 m by 10 m, was clearly imaged by GPR. It seems to have been filled with sand, which means it was backfilled after it was constructed. It may have been an entrance to the deeper structure. Its depth is of up to 2 m, measured from the ground surface. Below this structure, ERT reveals a highly electrically resistive anomaly, which extends over an area of about 10 m by 10 m. The depth of the structure is about 5–10 m below the surface. Electrically resistive material in a sand dune can be a mixture of sand and gravel, including sparse spacing or air voids within it. Neither GPR nor ERT could identify the properties of the anomaly. [ABSTRACT FROM AUTHOR]

This paper demonstrates the utility of ground‐penetrating radar (GPR) to inform forensic archaeology recovery efforts of missing service members from historic conflict‐related aircraft crash sites. This approach is becoming more common and improving recovery strategies by pinpointing potential subsurface anomalies prior to excavation. Two examples of recovery efforts at WWII aircraft crash sites are presented, revealing the diversity of landscape upheaval signatures that result from aircraft impacts. In both situations, the GPR successfully located feature boundaries and identified aviation impacturbation. The landscape signature varied in both cases due to factors including the trajectory and velocity of the aircraft crash and the topography of the impacted landscape. Notably, a 'halo' effect was identified in association with one crash site, revealing the force of the impact on sandy soils. Recognition of these anthropogenic signals is important to promote effective recovery strategies, thus saving time, labour and funds, particularly in historic sites where postincident taphonomic conditions have severely altered the morphology of the landscape. [ABSTRACT FROM AUTHOR]

In the last decades, the majority of the existing infrastructure heritage is approaching the end of its nominal design life mainly due to aging, deterioration, and degradation phenomena, threatening the safety levels of these strategic routes of communications. For civil engineers and researchers devoted to assessing and monitoring the structural health (SHM) of existing structures, the demand for innovative indirect non-destructive testing (NDT) methods aided with artificial intelligence (AI) is progressively spreading. In the present study, the authors analyzed the exertion of various deep learning models in order to increase the productivity of classifying ground penetrating radar (GPR) images for SHM purposes, especially focusing on road tunnel linings evaluations. Specifically, the authors presented a comparative study employing two convolutional models, i.e. the ResNet-50 and the EfficientNet-B0, and a recent transformer model, i.e. the Vision Transformer (ViT). Precisely, the authors evaluated the effects of training the models with or without pre-processed data through the bi-dimensional Fourier transform. Despite the theoretical advantages envisaged by adopting this kind of pre-processing technique on GPR images, the best classification performances have been still manifested by the classifiers trained without the Fourier pre-processing. [ABSTRACT FROM AUTHOR]

Shrinking of intermontane basins and expansion of their flanking ranges by reverse faulting and backthrusting in two counter-dipping systems is a typical mechanism of crustal shortening and mountain building in Central Asia. This mechanism is realized along the Kurai Fault Zone (southeastern Gorny Altai). Motions on two reverse fault systems maintained thrusting of the Kurai Range and the Kubadru Uplift on the Kurai Basin sediments and caused the growth of a foreberg before the mountain front. Forberg separates narrow Aktash Basin from the Kurai Basin. The paleoearthquakes were generated by reverse faults that delineate the foreberg. Analysis of the QuickBird satellite images, drone imagery, trenching, archaeoseismological research, radiocarbon dating, dendrochronology, and previous results show that eleven large (МW = 6.5–7.6) paleoearthquakes left traces as surface ruptures along the Kurai Fault Zone: twice before 7.5 ka BP, three events between 7.5 and 5.9 ka BP (7.0, 6.3, and 5.9–5.8 ka BP), one from 5.8 to 4.6 ka BP, four more at 4.6, 3.2, 1.5, and 1.3–1.2 ka BP, and the ultimate earthquake no older than 1450–1650 AD. The time difference between large earthquakes was from 200 to 1700 years. Surface faulting occurred mainly along the northern border of the foreberg where fault scarps are progressively younger northward and the Cenozoic sediments of the Aktash Basin thus become involved into uplift. GPR data to a depth of 12 m confirm the complex structure and slip geometry of the observed surface ruptures. The fault scarps are located < 1 km from the planned route of the gas pipeline from Russia to China, and the potential seismic hazard has to be taken into account in its design and construction. [ABSTRACT FROM AUTHOR]

Implementations of Markov chain Monte Carlo (MCMC) methods need to confront two fundamental challenges: accurate representation of prior information and efficient evaluation of likelihood functions. The definition and sampling of the prior distribution can often be facilitated by standard dimensionality-reduction techniques such as Principal Component Analysis (PCA). Additionally, PCA-based decompositions can enable the implementation of accurate surrogate models, for instance, based on polynomial chaos expansion (PCE). However, intricate geological priors with sharp contrasts may demand advanced dimensionality-reduction techniques, such as deep generative models (DGMs). Although suitable for prior sampling, these DGMs pose challenges for surrogate modelling. In this contribution, we present a MCMC strategy that combines the high reconstruction performance of a DGM in the form of a variational autoencoder with the accuracy of PCA–PCE surrogate modelling. Additionally, we introduce a physics-informed PCA decomposition to improve accuracy and reduce the computational burden associated with surrogate modelling. Our methodology is exemplified in the context of Bayesian ground-penetrating radar traveltime tomography using channelized subsurface structures, providing accurate reconstructions and significant speed-ups, particularly when the computation of the full-physics forward model is costly. [ABSTRACT FROM AUTHOR]

The detection and evaluation of internal corrosion of concrete bridge decks is of critical importance for bridge structure safety. Ground penetrating radar (GPR) is a nondestructive testing technique that has been used widely for corrosion assessment of RC bridge decks. However, the recognition accuracy of traditional automatic data processing methods for blurred hyperbolic features is insufficient, which leads to a decrease in the accuracy of bridge deck corrosion assessment. To address this problem, this paper proposes an automatic rebar picking algorithm based on gradient information and a migration method for bridge deck corrosion assessment with GPR data. This method can be divided into three steps: thresholding processing based on B-scan gradient information, a limited hyperbolic summation-based recognition (LHSR) algorithm, and rebar localization. First, the GPR B-scan is transformed into its gradient image and thresholded to enhance hyperbolic features. The LHSR algorithm then is applied to the binarized gradient B-scan to identify the hyperbolas, locate the rebar, and extract the rebar reflection amplitude. Finally, the corrosion map of the bridge deck is generated based on the rebar position and the rebar reflection amplitude after depth-correction. A case study with GPR data from two tested bridges was employed to validate the feasibility of the proposed method. The results show that the precision and recall of automatic rebar picking by this method for poor-quality GPR data were 91.27% and 93.56%, which are significantly higher than those of the traditional methods. Moreover, the accuracy of the bridge deck corrosion map obtained by the proposed method also is significantly better than that of the traditional methods. It can be concluded that the proposed method can be used for rebar picking and corrosion assessment of RC bridge decks with GPR data. Ground penetrating radar is a nondestructive testing technology that has been used widely for corrosion assessment of concrete bridge decks. However, the traditional automatic processing method of GPR data has insufficient recognition accuracy for blurred features of rebars, resulting in a decrease in the accuracy of bridge deck corrosion assessment. To address this problem, this paper proposes an automatic method that can be used for feature identification and location of rebars. The corrosion map generated by the position and reflection amplitude of rebars could help engineers to conduct corrosion assessment and repair of concrete bridge decks. The feasibility of the proposed method was validated through a case study with GPR data form two actual bridges. Especially for poor-quality GPR data, the accuracy of rebar picking and corrosion maps of this method is significantly higher than that of traditional methods. It can be concluded that the proposed method can be used for rebar positioning and corrosion assessment of concrete bridge decks based on GPR inspection data. [ABSTRACT FROM AUTHOR]

In view of the disadvantages of time consuming and low efficiency caused by single frequency band of Ground Penetrating Radar (GPR) products, a dual-frequency integrated composite array antenna with center frequency of 400 MHz and 1 000 MHz and bandwidth of 200~1 500 MHz is designed and implemented, and is applied in Ultra-Wide Band (UWB)GPR system. The array antenna consists of three butterfly antennas with a center frequency of 400 MHz and six butterfly antennas with a center frequency of 1 000 MHz, and has the capability of simultaneous detection of dual frequency bands, which can realize the detection results of two different frequency bands in one survey line, overcome the problem of repeated detection of different frequencies of traditional GPR and enhance the practicability of GPR system. The proposed dual-frequency integrated GPR antenna has the characteristics of ultra-wideband, high gain and narrow beam. Its relative bandwidth is 153%, the maximum peak gain of 17.6 dBi is achieved in the whole bandwidth, and the narrowest half-power beam width is 7.6°. It provides a new antenna scheme for high resolution and high efficiency GPR applications. [ABSTRACT FROM AUTHOR]

The underground basement of a building is highly affected by anomalous seepage problems. Detection of seepage-prone regions through and around buildings becomes a need in other to sustain their stability. It is of great importance as well to have targeted remedies. However, traditional methods for examining these areas are often destructive and expensive, compelling the use of non-destructive geophysical techniques. This work aims to utilize and demonstrate the hands-on application of ground penetrating radar (GPR) and self-potential (SP) methods as an innovative combination to study seepage-prone zones in the basement of a building. Simulated responses of various seepage zones have been generated based on several significant factors. For better visualization, all 2D radargrams of a block were combined to produce a 3D model using GPR Slice software to distinguish various features at different depths. Higher positive SP values show the presence of high moisture or seepage-prone regions. Conclusively, the combined geophysical method of GPR and SP is trustworthy for conducting a quick and effective inspection of seepage-prone regions and probable source points for detailed characterization of the basement of a building. [ABSTRACT FROM AUTHOR]

The structure and distribution of sub-glacial water directly influences Antarctic ice mass loss by reducing or enhancing basal shear stress and accelerating grounding line retreat. A common technique for detecting sub-glacial water involves analyzing the spatial variation in reflectivity from an airborne radar echo sounding (RES) survey. Basic RES analysis exploits the high dielectric contrast between water and most other substrate materials, where a reflectivity increase ≥ 15 dB is frequently correlated with the presence of sub-glacial water. There are surprisingly few additional tools to further characterize the size, shape, or extent of hydrological systems beneath large ice masses. We adapted an existing radar backscattering simulator to model RES reflections from sub-glacial water structures using the University of Texas Institute for Geophysics (UTIG) Multifrequency Airborne Radar Sounder with Full-phase Assessment (MARFA) instrument. Our series of hypothetical simulation cases modeled water structures from 5 to 50 m wide, surrounded by bed materials of varying roughness. We compared the relative reflectivity from rounded Röthlisberger channels and specular flat canals, showing both types of channels exhibit a positive correlation between size and reflectivity. Large (> 20 m), flat canals can increase reflectivity by more than 20 dB , while equivalent Röthlisberger channels show only modest reflectivity gains of 8–13 dB. Changes in substrate roughness may also alter observed reflectivity by 3–6 dB. All of these results indicate that a sophisticated approach to RES interpretation can be useful in constraining the size and shape of sub-glacial water features. However, a highly nuanced treatment of the geometric context is necessary. Finally, we compared simulated outputs to actual reflectivity from a single RES flight line collected over Thwaites Glacier in 2022. The flight line crosses a previously proposed Röthlisberger channel route, with an obvious bright bed reflection in the radargram. Through multiple simulations comparing various water system geometries, such as canals and sub-glacial lakes, we demonstrated the important role that topography and water geometry can play in observed RES reflectivity. From the scenarios that we tested, we concluded the bright reflector from our RES flight line cannot be a Röthlisberger channel but could be consistent with a series of flat canals or a sub-glacial lake. However, we note our simulations were not exhaustive of all possible sub-glacial water configurations. The approach outlined here has broad applicability for studying the basal environment of large glaciers. We expect to apply this technique when constraining the geometry and extent of many sub-glacial hydrologic structures in the future. Further research may also include comprehensive investigations of the impact of sub-glacial roughness, substrate heterogeneity, and computational efficiencies enabling more complex and complete simulations. [ABSTRACT FROM AUTHOR]

The rapid identification of structural damage in pavements remains a challenging issue. This paper proposes an automatic identification method that combines nondestructive testing, the ground penetrating radar (GPR), and object detection (OD) models for pavement structural assessment. Radar wave image data were collected from a 40 km single-lane track road and processed to create the training data set of 2,000 labeled images with two types of structural distress. Four OD models, including Faster R-CNN, YOLOv5, YOLOv8, and DETR, were adapted and trained on the data set to recognize structural damage in the images, and their prediction performances were compared. The results demonstrated that YOLOv8 was the state-of-the-art (SOTA) model, which achieved precise identification of the location and type of pavement structural damage with a mean average precision (mAP) of 98.5%. This study contributes to the improvement of latest OD models in the non-destructive inspection, facilitating the development of real-time automated pavement assessment and maintenance decision systems. Practical Applications: This research addresses a common problem in civil engineering: how to quickly detect all hidden damage in pavement structures over long stretches of highway projects. Although there has been previous research in this area, finding a more reliable solution has proven to be a challenging goal. This study presents a method that utilizes the geological radar, a nondestructive testing technology, meaning it will not harm the road, along with artificial intelligence (AI) computer models to identify damage under the pavement surface. Think of it as teaching a computer to recognize cracks or looseness in the radar images of the road. Four different computer models were trained using 2,000 road images with known damage to teach them how to identify different damage features. The results were impressive—the computer could accurately identify the location and type of damage with a high level of confidence. In fact, the best model achieved a 98.5% score measured by mean average precision, which indicates its effectiveness. This research has significant practical applications as it streamlines the process of inspecting roads for structural problems, making it faster and more efficient. It can help with real-time evaluations and decisions about road maintenance. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR) is a rapid and non-destructive geophysical technique widely employed to detect and quantify subsurface structures and characteristics. Its capability for time lapse (TL) detection provides essential insights into subsurface hydrological dynamics, including lateral flow and soil water distribution. However, during TL-GPR surveys, field conditions often create discrepancies in surface geometry, which introduces mismatches across sequential TL-GPR images. These discrepancies may generate spurious signal variations that impede the accurate interpretation of TL-GPR data when assessing subsurface hydrological processes. In responding to this issue, this study introduces a TL-GPR image alignment method by employing the dynamic time warping (DTW) algorithm. The purpose of the proposed method, namely TLIAM–DTW, is to correct for geometric mismatch in TL-GPR images collected from the identical survey line in the field. We validated the efficacy of the TLIAM–DTW method using both synthetic data from gprMax V3.0 simulations and actual field data collected from a hilly, forested area post-infiltration experiment. Analyses of the aligned TL-GPR images revealed that the TLIAM–DTW method effectively eliminates the influence of geometric mismatch while preserving the integrity of signal variations due to actual subsurface hydrological processes. Quantitative assessments of the proposed methods, measured by mean absolute error (MAE) and root mean square error (RMSE), showed significant improvements. After performing the TLIAM–DTW method, the MAE and RMSE between processed TL-GPR images and background images were reduced by 96% and 78%, respectively, in simple simulation scenarios; in more complex simulations, MAE declined by 27–31% and RMSE by 17–43%. Field data yielded reductions in MAE and RMSE of >82% and 69%, respectively. With these substantial improvements, the processed TL-GPR images successfully depict the spatial and temporal transitions associated with subsurface lateral flows, thereby enhancing the accuracy of monitoring subsurface hydrological processes under field conditions. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR) is a widely used technology for pipeline detection due to its fast detection speed and high resolution. However, the presence of complex underground media often results in strong ground clutter interference in the collected B-scan echoes, significantly impacting detection performance. To address this issue, this paper proposes an improved clutter suppression network based on a cycle-consistency generative adversarial network (CycleGAN). By employing the concept of style transfer, the network aims to convert clutter images into clutter-free images. This paper introduces multiple residual blocks into the generator and discriminator, respectively, to improve the feature expression ability of the deep learning model. Additionally, the discriminator incorporates the squeeze and excitation (SE) module, a channel attention mechanism, to further enhance the model's ability to extract features from clutter-free images. To evaluate the effectiveness of the proposed network in clutter suppression, both simulation and measurement data are utilized to compare and analyze its performance against traditional clutter suppression methods and deep learning-based methods, respectively. From the result of the measured data, it can be found that the improvement factor ( I m ) of the proposed method has reached 40.68 dB, which is a significant improvement compared to the previous network. [ABSTRACT FROM AUTHOR]

A tremendous number of landmines has been buried during the last decade. In recent years, various autonomous platforms equipped with ground-penetrating radars (GPRs) have been proposed for the detection of landmines. These systems have already demonstrated their performance in controlled environments with known ground truth. However, it has been observed that the influence of surface conditions in the form of vegetation and roughness as well as soil moisture content significantly reduce the detection probability. The influence of these individual factors on a ground-offset GPR is presented and discussed in this work. Each of these factors significantly degrades the backscattered signal. With increasing soil moisture, the signal gets attenuated more strongly; however, the signature is maintained in the phase of the C-Scans. An increase in surface roughness deteriorates the target pattern making it difficult to detect buried objects unambiguously. Vegetation, especially with irregular leaf structures, can appear as a ghost target and scatter the electromagnetic waves. In most cases, the target is easier to detect in the phase of the B- or C-Scan. [ABSTRACT FROM AUTHOR]

Ice thickness has a significant effect on the physical and biogeochemical processes of a lake, and it is an integral focus of research in the field of ice engineering. The Qinghai–Tibetan Plateau, known as the Third Pole of the world, contains numerous lakes. Compared with some information, such as the area, water level, and ice phenology of its lakes, the ice thickness of these lakes remains poorly understood. In this study, we used an unmanned aerial vehicle (UAV) with a 400/900 MHz ice-penetrating radar to detect the ice thickness of Qinghai Lake and Gahai Lake. Two observation fields were established on the western side of Qinghai Lake and Gahai Lake in January 2019 and January 2021, respectively. Based on the in situ ice thickness and the propagation time of the radar, the accuracy of the ice thickness measurements of these two non-freshwater lakes was comprehensively assessed. The results indicate that pre-processed echo images from the UAV-borne ice-penetrating radar identified non-freshwater lake ice, and we were thus able to accurately calculate the propagation time of radar waves through the ice. The average dielectric constants of Qinghai Lake and Gahai Lake were 4.3 and 4.6, respectively. This means that the speed of the radar waves that propagated through the ice of the non-freshwater lake was lower than that of the radio waves that propagated through the freshwater lake. The antenna frequency of the radar also had an impact on the accuracy of ice thickness modeling. The RMSEs were 0.034 m using the 400 MHz radar and 0.010 m using the 900 MHz radar. The radar with a higher antenna frequency was shown to provide greater accuracy in ice thickness monitoring, but the control of the UAV's altitude and speed should be addressed. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR) is a classic geophysical exploration method that utilizes the emission and reception of electromagnetic waves to non-destructively detect target objects in the target medium. It has been widely applied in various fields such as pipeline detection, cavity detection, and rebar detection. However, GPR systems are susceptible to environmental clutter interference, which poses challenges for data interpretation and subsequent processing. In this paper, the separability of clutter and target signal in the frequency-wavenumber (F-K) domain is validated through modeling, leading to the proposal of a comprehensive clutter removal method based on the F-K domain for complex scenarios. The direct coupling wave is initially eliminated by applying a peak matching mean subtraction filter, which avoids the artifacts. Subsequently, the F-K domain transformation is performed and surface clutter undulations are effectively removed using a method based on singular value decomposition and k-means clustering. Finally, an angle filter with Gaussian tapering at the edges is designed based on physical models to efficiently eliminate linear interference without undesired ringing interference. The commonly used clutter removal algorithms, including mean subtraction (MS), singular value decomposition (SVD), robust principal component analysis (RPCA), and traditional F-K filtering methods, are compared with the proposed algorithm on both the numerical simulated data and actual GPR data. The results from visual and quantitative analysis confirm that our proposed method is more effective than current commonly used clutter suppression algorithms. We have successfully enhanced the Signal-to-Clutter Ratio (SCR) of the GPR data, resulting in an Improvement Factor (IF) of 30.63 dB, 23.59 dB, and 30.60 dB for simulated data, experimental data, and TU1208 public data, respectively. The detection capability of buried targets is enhanced, thereby establishing a solid foundation for subsequent data interpretation and target identification. [ABSTRACT FROM AUTHOR]

Large rivers are characterized by large water discharges, high suspended sediment fluxes and low slope, and typically display multiple channels that are separated by large complex bars. The most common channel style found in these rivers is characterized by the alternating presence of single and multiple channel threads. Mid‐channel compound bars separate a main deeper channel from shallower secondary channels, leading to low‐order braiding and low to moderate sinuosity. Despite the importance of this type of river for global terrestrial sediment transport and the interpretation of ancient fluvial successions, integrated depositional models for large multi‐channel rivers are still not fully developed. This paper interprets the channel morphodynamics and depositional architecture of such large rivers by investigating the distinctive features of their laterally‐accreting mid‐channel bars in the Solimões–Amazonas River. This is achieved by examination of temporal series of satellite images, quantification of bedforms using multibeam echosounding surveys, analysis of shallow seismic and ground penetrating radar surveys of selected areas, and on‐site field observations and sample collections. Such mid‐channel bars produce directional variability in planform scroll bar accretion of up to 180°. As these bars – and their associated channels – evolve, they become progressively shallower, due to the bifurcation of the two channels and the resultant partitioning of discharge that leads to a gradual reduction in transport capacity within the outer channel. The preserved successions of this process in the alluvial plain are characterized by fining‐upward trends, with larger cross‐strata sets at mid‐depths and internal erosional surfaces marking distinct cycles of bar development with potentially differing palaeocurrent trends. The new depositional models proposed for these mid‐channel bars and channels constitute a basis for the recognition of large multi‐channel river deposits in the ancient rock record. [ABSTRACT FROM AUTHOR]

Star dunes are the tallest dunes on Earth and are amongst the larger and more spectacular aeolian landforms. Although they are widespread in modern sandy deserts, star dunes are rarely recognised in the rock record probably due to a lack of suitable sedimentary models. This paper presents a new sedimentary model for the structure of a star dune at Erg Chebbi in Morocco (Sahara Desert) on the basis of ground-penetrating radar (GPR) surveys. Individual sedimentary structures in star dunes are similar to those in linear or barchanoid dunes, likely leading to misidentification in the rock record. However, the suite of features described in this paper will permit identification of star dunes in future studies of the rock record. Optically stimulated luminescence (OSL) dating shows that accumulation of the Erg Chebbi star dune post-dates the end of the African Humid Period (AHP). At the base of the dune, there is an ~ 8000-year hiatus in the record. Since then, the dune has grown rapidly to create a 100 m high dune within the past 1000 years and is migrating towards the west. Changes in the cross-strata support the idea that star dune construction was accompanied by a change in the wind directions. [ABSTRACT FROM AUTHOR]

Fiber-Reinforced Polymer (FRP) composites have emerged as a promising alternative to conventional steel reinforcements in concrete structures owing to their benefits of corrosion resistance, higher strength-to-weight ratio, reduced maintenance cost, extended service life, and superior durability. However, there has been limited research on non-destructive testing (NDT) methods applicable for identifying damage in FRP-reinforced concrete (FRP-RC) elements. This knowledge gap has often limited its application in the construction industry. Engineers and owners often lack confidence in utilizing this relatively new construction material due to the challenge of assessing its condition. Thus, the main objective of this study is to determine the applicability of two of the most common NDT methods: the Ground-Penetrating Radar (GPR) and Phased Array Ultrasonic (PAU) methods for the detection of damage in FRP-RC elements. Three slab specimens with variations in FRP type (glass-, carbon- and basalt-FRP, i.e., GFRP, CFRP, and BFRP, respectively), bar diameter, bar depths, and defect types were investigated to determine the limitations and detection capabilities of these two NDT methods. The results show that GPR could detect damage in GFRP bars and CFRP strands, but PAU was limited to damage detection in CFRP strands. The findings of this study show the applicability of conventional NDT methods to FRP-RC and at the same time identify the areas with a need for further research. [ABSTRACT FROM AUTHOR]

To investigate a thin layer, we conducted borehole radar measurements at the GPR facility of Tohoku University, Japan. Both zero-offset profiling and tomography measurements were used to obtain water content distributions. Analyzing first arrival times with amplitudes, we found that a borehole radar can detect velocity changes caused by a high-water content, which acts as an electromagnetic waveguide. We conducted a 3D FDTD simulation and confirmed that the EM wave propagates as a guided wave in a thin layer related to antenna frequency. We determined a thin geological layer compared to an antenna frequency that cannot be detected by reflection measurement due to the poor radar resolution. By comparing the simulated data with measurement data, we could determine the geological boundary between host layers and a thin layer with high water content. In contrast, both the amplitude and the travel time of direct waves are affected by antenna positions and electrical parameters, including permittivity and conductivity. A vertical velocity profile was obtained by combining complementary zero-offset profiling and tomography data, which can be used to investigate water content distribution. The low-velocity thin layer was characterised by amplitude attenuation and late arrival time. [ABSTRACT FROM AUTHOR]

New government policy in China has facilitated research on the hazards in open-pit mine slopes. Remote sensing, geological, geophysical, and computer simulation techniques have been applied to obtain better information in slope investigation. In this study, we investigated the slope of the Qianlingshan open-pit mine area in Beijing, China. Electrical resistivity tomography and ground-penetrating radar were used, and the results were compared with borehole data to ascertain the slope structure, potential slip surface, and deformation zone. A geological model of the slope was established, and the slope stability was analyzed by numerical simulation. The results showed that the slope had an unstable surface layer over bedrock, and probable creep sliding-tension deformation. In addition, the slope would become less stable in wet conditions. The geological methods used in this study will provide a useful reference for further slope stability analysis and prevention. We took the Qianlingshan open-pit slope in Beijing, China, as the research object, by comprehensive application of historical remote sensing images analysis, field investigation, electrical resistivity tomography (ERT) and ground penetrating radar (GPR), and comparative analysis with borehole data, to obtain the slope parameter such as resistivity and velocity of electromagnetic wave, and to ascertain the slope structure, potential slip surface and deformation zone. On this basis, the geological generalisation model of slope was established, and the failure mechanism and stability of slope were analyzed by numerical simulation. [ABSTRACT FROM AUTHOR]

• Integrated approach for multi-scale and multi-resolution diagnostics of wall paintings by the combination of wave-based and optical techniques. • Surface and subsurface investigation for the stratigraphic reconstruction of past intervention and modification. • For the first time, the microwave reflectometry is here proposed in combination with digital holographic speckle pattern interferometry for wall paintings inspection. • Proof of concept of the proposed tool on the wall paintings masterpiece of Masolino, Masaccio and Filippino Lippi in the Brancacci chapel in Firenze. Based on the analysis of the renaissance wall paintings by Masaccio, Masolino, and Filippino Lippi in the Brancacci chapel in Firenze, this paper discusses the use of complementary non-destructive techniques based on microwave and optical methods for the characterization of the structural integrity of the wall paintings and their support in masonry. The selected non-destructive techniques are the InfraRed Thermography (IRT), Ground Penetrating Radar (GPR), Microwave Reflectometry (MWR), and Digital Holographic Speckle Pattern Interferometry (DHSPI). In particular, the paper analyses the in-situ applicability of these techniques for the identification of the sequence of past interventions during centuries (stratigraphy analysis) and decay phenomena and defects, such as out-of-plumb or swelling area/elements, detachments, cracks and voids inside the wall. The results are compared with data obtained by means of consolidated techniques and methods, such as the Photogrammetry (performed by Structure from Motion method) and knocking test. The last one is normally used by restorers and conservators to recognize the presence of detachments. The proposed diagnostic strategy provides a survey from large scale by means of imaging techniques, to small scale increasing the spatial resolution thanks to the scanning of the surface by means of spot techniques. Therefore, the macroscopic survey of wall paintings was carried out using photogrammetry, in order to also provide metric information, to quantify the sizing out-of-plumb and swelling of the masonry or to locate of cracks, and followed by IRT. This preliminary morphometric survey was, supplemented by GPR, MWR and DHSPI for improve the results of the investigation. By combining these three techniques it was possible to inspect the entire thickness of the masonry (60-70 cm) with resolutions ranging from a few millimetres up to several centimetres. The combination of microwave-based and optical-based methods proved to be a valuable addition to routine methods for the holistic masonry diagnosis. Standard practice based on visual inspection and knocking test can be significantly improved and objectified by the proposed full-field, multi-sensor, multi-resolution approach. [ABSTRACT FROM AUTHOR]