Search Results (36)

Metallic blocks and components are indispensable across the aerospace, energy, and heavy-engineering industries, where undetected internal flaws such as cracks, voids, and inclusions may precipitate catastrophic structural failure. Reliable nondestructive evaluation (NDE) is essential to ensure their integrity and operational safety. Among the available NDE techniques, phased array ultrasonic testing (PAUT) has emerged as one of the most accessible and widely adopted, by virtue of its rapid scanning, electronic beam steering, and capacity to image subsurface defects without disassembly. However, the interpretation of PAUT B-scan images remains hindered by background reflections, material-dependent echo characteristics, and substantial variability in defect size. In this work, a fine-tuned encoder–decoder deep learning network is proposed for the automated segmentation of internal defects in PAUT B-scan images of metallic block specimens. The network couples a ResNet50 encoder with a shallow detail stem, a multi-scale feature fusion module, and a detail refinement block, designed to preserve small defect echoes and sharpen weak defect boundaries characteristic of internal flaws. The proposed approach was compared with five state-of-the-art segmentation architectures, namely FCN, PSPNet, DeepLabv3+, HRNet-OCR, and SegFormer, as well as a conventional Otsu-thresholding baseline representing standard PAUT screening practice. Experimental results demonstrate that the proposed network attained the highest Dice score of 0.7964, defect intersection-over-union of 0.6617, and precision of 0.7094 among all evaluated models, while the Otsu baseline yielded the lowest scores, confirming the benefit of learned segmentation over fixed amplitude thresholding. These findings indicate that the proposed network achieves a favorable trade-off between defect localization accuracy and false-positive suppression, underscoring its potential for reliably segmenting internal defects in PAUT B-scan imaging. Full article

There are several methods which are utilized for flooding oil process monitoring, such as the seismic methods, and the electromagnetic methods. As the gas flooding oil process is complicated, conventional methods are not capable of monitoring the gas flooding oil process accurately. This study utilizes the Ground Penetrating Radar (GPR) method to monitor the CO₂ flooding oil and water flooding oil processes, as the difference in dielectric constants and conductivity of CO₂, oil and water is utilized to infer distributions of CO₂, oil and water. Moreover, as GPR data processing is time-consuming, it is impossible to process the GPR data in real-time by a conventional method, such as the full waveform inversion method. This study utilizes U-Net neural networks to invert for the subsurface dielectric constants and conductivity distributions of CO₂, oil and water in real-time. A deep learning inversion network based on the U-Net architecture is trained to extract multi-scale features through an encoder–decoder structure, achieving an end-to-end mapping from GPR echo signals to subsurface electrical parameters. The study utilizes the gprMax forward tool to simulate the dynamic response changes in rock-electrical parameters during flooding and constructs a high-resolution training dataset of 100,000 samples. Each sample contains the relationships between a subsurface electrical parameter model and its corresponding multi-transmitter, multi-receiver GPR responses. This method was first tested by the synthetic data of oil–water flooding and oil–water–gas flooding, and then it was tested by observed data from physical core experiments. Numerical and physical core experimental results show that the method accurately inverts the electrical parameter distributions of oil, water, and gas in the sandstone model, successfully capturing the position and morphology changes in the displacement front. The average relative error of dielectric constant inversion is controlled within 8% with the error mainly from the low dielectric constant regions and the relative error of conductivity is smaller than 10%, with the error mainly concentrated in high-conductivity water regions for conductivity inversion results. The results reveal the feasibility and superiority of the neural network-based deep learning method in GPR electromagnetic inversion, providing a new method for real-time flooding monitoring and intelligent reservoir development during oil and gas flooding. Moreover, the proposed approach offers a fast inversion solution and is less affected by the initial model and noise. Full article

Adhesive fracture in layered structures is governed by subsurface crack evolution that cannot be accessed using surface-based diagnostics. Methods such as digital image correlation and optical spectroscopy measure surface deformation but implicitly assume a straight and uniform crack front, an assumption that becomes invalid for interfacial fracture with wide crack openings and asymmetric propagation. In this work, terahertz time-domain spectroscopy (THz-TDS) is combined with double-cantilever beam testing to directly map subsurface crack-front geometry in opaque adhesive joints. A strontium titanate-doped epoxy is used to enhance dielectric contrast. Multilayer refractive index extraction, pulse deconvolution, and diffusion-based image enhancement are employed to separate overlapping terahertz echoes and reconstruct two-dimensional delay maps of interfacial separation. The measured crack geometry is coupled with load–displacement data and augmented beam theory to compute spatially averaged stresses and energy release rates. The measurements resolve crack openings down to approximately 100 μm and reveal pronounced width-wise non-uniform crack advance and crack-front curvature during stable growth. These observations demonstrate that surface-based crack-length measurements can either underpredict or overpredict fracture toughness depending on the measurement location. Fracture toughness values derived from width-averaged subsurface crack fronts agree with J-integral estimates obtained from surface digital image correlation. Signal-to-noise limitations near the crack tip define the primary resolution limit. The results establish THz-TDS as a quantitative tool for subsurface fracture mechanics and provide a framework for physically representative toughness measurements in layered and bonded structures. Full article

Concrete pavements frequently develop subsurface voids between surface and base layers during long-term service due to cyclic loading, environmental effects, and subgrade instability, which compromise structural integrity and traffic safety. Ground-penetrating radar (GPR) has been widely used as a non-destructive method for detecting such voids. However, the presence of coarse aggregates with strong electromagnetic scattering properties often introduces pseudo-reflection signals in radar images, hindering accurate void identification. To address this challenge, this study develops a high-fidelity three-phase concrete model incorporating aggregates, mortar, and the interfacial transition zone (ITZ). The Finite-Difference Time-Domain (FDTD) method is used to simulate electromagnetic wave propagation in both voided and intact structures. Simulation results reveal that aggregate-induced scattering can blur or distort reflection interfaces, generating pseudo-hyperbolic anomalies even in the absence of voids. In cases of thin-layer voids, real echo signals may be masked by aggregate scattering, leading to missed detections. GPR systems can be broadly classified into impulse, continuous-wave, and multi-frequency types. To validate the simulations, field tests using multi-frequency 2D/3D GPR systems and borehole verification were conducted. The results confirm the consistency between simulated and actual radar anomalies and validate the proposed model. This work provides theoretical insight and modeling strategies to enhance the interpretation accuracy of GPR data for subsurface void detection in concrete pavements. Full article

Localizing ground-penetrating radar (LGPR) benefits from deep subsurface coupling, ensuring robustness against surface variations and adverse weather. While LGPR is widely recognized as the complement of existing vehicle localization methods, its reliance on prior maps introduces significant challenges. Channel misalignment during traversal positioning and time-dimension distortion caused by non-uniform platform motion degrade matching accuracy. Furthermore, rain and snow conditions induce subsurface water-content variations that distort ground-penetrating radar (GPR) echoes, further complicating the localization process. To address these issues, we propose a weather-resilient adaptive spatio-temporal mask alignment algorithm for LGPR. The method employs adaptive alignment and dynamic time warping (DTW) strategies to sequentially resolve channel and time-dimension misalignments in GPR sequences, followed by calibration of GPR query sequences. Moreover, a multi-level discrete wavelet transform (MDWT) module enhances low-frequency GPR features while adaptive alignment along the channel dimension refines the signals and significantly improves localization accuracy under rain or snow. Additionally, a local matching DTW algorithm is introduced to perform robust temporal image-sequence alignment. Extensive experiments were conducted on both public LGPR datasets: GROUNDED and self-collected data covering five challenging scenarios. The results demonstrate superior localization accuracy and robustness compared to existing methods. Full article

The underwater corner reflector is a “concave” elastic structure, and its acoustic echo exhibits large elastic loss, which affects its practical use. To study the acoustic echo elastic loss characteristics of underwater corner reflectors, based on the characteristics of small concave elastic structures of underwater corner reflectors, theoretical calculations were performed using the method of a combination of finite element and boundary element. Taking the underwater rigid corner reflector as the benchmark, the acoustic echo differences between similar types of underwater elastic corner reflectors were compared. The regular acoustic echo elastic loss of underwater corner reflectors was analyzed, and verified through pool experiments. The results show that, whether single-grid or multi-grid corner reflector, the actual acoustic echoes of underwater corner reflectors conform to the characteristics of elastic bodies, which differ significantly from rigid bodies and exhibit obvious elastic loss. The elastic loss mainly manifests as reduced target strength (TS), narrower directional pattern width, and poorer frequency stability of target strength, which is detrimental to practical use. This study provides assistance in proposing targeted methods to suppress elastic loss. Full article

Spaceborne radar is a powerful tool for probing planetary subsurface structures. Earlier radar studies of the Moon have primarily examined large-scale horizontal structures. However, recent discoveries of vertical holes suggesting the existence of lava tubes and theoretically predicted subsurface gas voids formed by volatiles in magma have highlighted the importance of small-scale subsurface structures. We developed a method using SELENE Lunar Radar Sounder (LRS) data to detect small-scale subsurface echoes (hundreds of meters). Surface scattering simulations incorporating incoherent scattering from sub-resolution roughness were performed using a high-resolution digital elevation model generated by a generative adversarial network. Detection thresholds for subsurface echo candidates (SECs) were determined from the histograms of difference intensities between LRS and simulation B-scans. Results show that some SECs exist in the extension area of the analyzed graben. SECs were also detected continuously across multiple LRS ground tracks in areas unrelated to grabens. Using the radar equation analysis, the echo intensities of SECs could be explained for subsurface structures with 50–600 m widths and dielectric constants of 1–4. This suggests the existence of either subsurface voids or materials with a high porosity of more than 35%. Among the SECs detected continuously across multiple LRS ground tracks, those that are more or less aligned in the downward elevation direction are likely indicative of lava tubes. On the other hand, the SECs distributed along the extension of the graben are aligned parallel to the contour lines. These SECs likely suggest gas voids at the tip of the intrusive magma that formed the graben. Full article

This paper presents the world’s first radar detection experiment conducted in a 7500-m ultra-deep well. By applying ground-penetrating radar technology to petroleum logging, the developed borehole radar system successfully achieved stratigraphic information detection in the 7200–7500 m section of Shunbei Well No. 2. Utilizing electromagnetic wave reflection principles, the system acquires echo signals carrying medium characteristics through transmit–receive antenna arrays coupled with field-programmable gate array (FPGA)-based high-speed acquisition for real-time downhole data transmission. Experimental results demonstrate high consistency in Gamma Ray (GR) curves (correlation coefficient: 0.92) between radar data and Sinopec’s geological drilling data, particularly in key stratigraphic features such as casing reflections at a 7250-m depth (error of 0.013%). This breakthrough validates the operational stability and detection accuracy of borehole radar in complex subsurface environments, providing an innovative technological approach for ultra-deep hydrocarbon exploration. Full article

Air-coupled ground penetrating radar (GPR) systems are widely used for subsurface imaging in demining, geological surveys, and infrastructure assessment applications. However, strong surface reflections can introduce interference, leading to receiver saturation and reducing the clarity of subsurface features. This paper presents a novel surface reflection suppression algorithm for stepped-frequency continuous wave (SFCW) GPR systems. The proposed method estimates the surface reflection component and applies phase-compensated subtraction at the receiver site, effectively suppressing background reflections. A modular SFCW radar system was developed and tested in a laboratory setup simulating a low-altitude airborne deployment to validate the proposed approach. B-scan and time-domain analyses demonstrate significant suppression of surface reflections, improving the visibility of subsurface targets. Unlike previous static echo cancellation methods, the proposed method performs on-board pre-downconversion removal of surface clutter that compensates for varying ground distance, which is a unique contribution of this work. Full article

The polar regions of Mars, including the South and North Poles, are crucial for studying Martian climate and geological history, as they contain the largest reservoir of subsurface water ice. This study introduces a new approach for reflector detection, which includes radargram denoising to effectively enhance the signal of underground reflectors, peak detection to extract the positions of subsurface stratification from the radar echoes, and peak points connection to form continuous layers. The mapped enhancement denoising process involves a linear brightness adjustment and a fourth-order diffusion equation to enhance the signal of the subsurface layers for effective detection. The subsurface detection extracts the surface and subsurface peak points based on a peak detection algorithm, while using locally window-enhanced peak filtering and Kullback–Leibler (KL) divergence mapping to filter out non-stratified peak points. Finally, the layered connection process uses the proximity parameter to connect peak points in the same layer. Applied to multiple SHARAD (Shallow Radar) images at the Martian poles, this algorithm demonstrated a false detection rate below 5%. Compared to other methods, this method has a missed detection rate of less than 5% and, additionally, exhibits fewer discontinuities in layer connectivity. Therefore, this algorithm shows exceptional proficiency and applicability in analyzing the complex subsurface structures of the Martian polar regions. Full article

In recent decades, extensive research has led to the understanding that Mars once hosted substantial liquid-water reserves. While the current Martian landscape boasts significant water-ice deposits at its North and South poles, the elusive presence of liquid-water bodies has remained undetected. A breakthrough occurred with the identification of radar-echo reflections at the base of the Martian South Pole, using MARSIS (Mars Advanced Radar for Subsurface and Ionospheric Sounding) in 2018. These radar echoes strongly suggest the presence of a highly concentrated liquid-water body. However, a counter-narrative has emerged, contending that the subterranean conditions beneath the ice cap, encompassing factors like temperature and pressure, may be inhospitable to liquid water. Consequently, alternative hypotheses posit that the observed bright echoes could be attributed to conductive minerals or water-absorbing clay-like materials. The ongoing discourse regarding the presence of liquid water beneath the southern polar ice cap is a hot topic in the realm of Martian exploration. The primary focus of this paper is to provide a comprehensive overview of Martian radar detection, the recent controversies regarding liquid water’s existence in the Martian South Pole, and the implications regarding the potential existence of Martian life forms in the water on Mars. The revelation of liquid water on Mars fundamentally suggests an environment conducive to the viability of Martian life, consequently furnishing invaluable insights for future exploratory endeavors in the pursuit of Martian biospheres. In addition, this paper anticipates the forthcoming research dedicated to Martian liquid water and potential life forms, while also underscoring the profound significance of identifying liquid water on Mars in propelling the field of astrobiology forward. Full article

Waterlogging and soil salinity issues can be handled using surface or subsurface drainage networks, soil bed elevation, and soil and crop management patterns. A properly operating and maintained drainage system is important for both rural and urban inhabitants to protect lives and property from flooding and high groundwater levels, enhance health conditions, and safeguards water purity, soil salinity, and waterlogging. It also supports and increases crop yields and consequently rural incomes. This study assessed the maintenance condition of the main surface drains (Baloza and ELFarama) located in the Tina Plain (50,000 acres) and a portion of the Southeast El-Kantara regions (25,000 acres) in North Sinai, Egypt, based on the values of the Discharge Capacity Ratio (DCR) and Manning’s roughness (n). Ten measurement locations at the drain cross-section were used in the investigation. For the ELFarama Drain, the average values of n and DCR were found to be 0.029 and 86.2%, and for the Baloza Drain, they were 0.032 and 78.6%, respectively. Compared to the design values, the actual Manning’s roughness was higher, indicating that the drainage canals’ capacities had been reduced and that their upkeep was inadequate. In both drains, sedimentation is present and they need to be maintained, according to the hydrographic surveying results for the actual cross-sections compared to the planned cross-sections. A methodology for the channel maintenance method is presented. For removing vegetation and dredging sediment, a long-boom mechanical hydraulic excavator with a bucket is suggested and to be conducted every two years. To the results of this study, the amount of weed infestation in vegetated channels is the main factor that affects Manning’s roughness coefficient value. It is now easier to calculate the proportion of weeds that are submerged in vegetated channels using echo-sound sonar technology. The DCR is an affordable and simple methodology to assess the channel maintenance status for sustainable agriculture. Full article

The Rover-mounted Subsurface Penetrating Radar (RoSPR) is one of the scientific payloads onboard China’s first independent Mars exploration mission, Tianwen-1. The radar aims to characterize the thickness of the upper Martian soil and investigate the subsurface stratigraphy by collecting and processing the data. This article is mainly divided into two parts, the introduction of data pre-processing and analysis of pre-processed radar signals, aiming at helping scientists make more effective use of radar data. The first part describes the operating principle of the RoSPR and the procedure of radar data pre-processing at all levels. Data pre-processing is mainly designed to transfer the raw data format to a common PDS (Planetary Data System) and eliminate the influence of the instrument. In the signal analysis part, the performances of both self-check signals and echo signals of low- and high-frequency channels are analyzed, which indicate a stable radar system and are useful for background removal. Phase and time calibration is of great importance for improving data quality and making the radar data more accurate. Moreover, further processing is required to obtain clear radar images, such as filtering, background removal and gain setting. Full article

The SHAllow RADar (SHARAD) onboard Mars Reconnaissance Orbiter (MRO) is a 20 MHz Synthetic Aperture Radar (SAR) that probes the first hundreds of meters of the Martian subsurface. In order to interpret the detection of subsurface interfaces with ground penetrating radars, simulations using Digital Terrain Models (DTM) are necessary. This methodology paper focuses on the analysis of the first tens of meters of the Martian subsurface with SHARAD, comparing the use of different high-resolution DTMs for radar simulation, namely, from the High-Resolution Stereo Camera (HRSC) onboard the Mars Express and from the Context Camera (CTX) onboard MRO. The region of Terra Cimmeria was chosen as a demonstration area. It is a highly cratered southern midlatitude region, where, as will be discussed, the higher resolution of the aforementioned terrain models is mandatory to describe the surface at an acceptable level of detail for shallow subsurface radar interpretation. With a DTM corrected by photoclinometry using CTX imagery, we show that a reflector that was visible on SHARAD data but not on the simulation made with an HRSC DTM is, in fact, a surface echo that was not reproduced by the HRSC surface model. We also show that, unlike laser altimetry DTMs, optical DTMs are prone to artifacts that can make radar analysis more complicated for some scenarios. Reciprocally, we show that the comparison between radar and its corresponding simulated data is a way of assessing a DTM’s quality, which is especially useful in missions where ground control points are lacking, unlike Martian observations. Full article