Soil layers affect the vertical movement of moisture and salt, eventually resulting in land cover and land use pattern changes. This study explored the ability of ground penetrating radar (GPR) to detect soil layers in the modern Yellow River Delta of China and assessed its accuracy. It was found that soil moisture and salt had a strong dampening effect on the electromagnetic wave signal which resulted in blurred GPR images of the soil profile below 1 m. The cultivated soil layers of different crop types such as rice, wheat, corn, and cotton were accurately identified in GPR images. To estimate an individual soil layer thickness, the propagation velocity of the electromagnetic wave was calculated using soil mass moisture content, and the propagation time was confirmed by comparing the GPR image with the amplitude-time plot of the soil profile. The estimated thickness was 1.02 times the thickness determined in the field and the average estimation error was 0.04 m, which was 24.09% of the soil layer thickness determined in the field. The second derivative value of envelope amplitude energy with time (SDEA) was used to describe the amplitude change in the soil layers. The SDEA has negative logarithmic and power function relationships with soil mass moisture content and electrical conductivity, respectively. The present results provide a reference database for future quantitative soil investigation in the sedimentary plain area using GPR.
{"title":"Investigating soil layers with ground penetrating radar in the modern Yellow River Delta of China","authors":"Ping WANG, Xinju LI, Xiangyu MIN, Shuo XU, Guangming ZHAO, Deqiang FAN","doi":"10.1002/nsg.12289","DOIUrl":"https://doi.org/10.1002/nsg.12289","url":null,"abstract":"Soil layers affect the vertical movement of moisture and salt, eventually resulting in land cover and land use pattern changes. This study explored the ability of ground penetrating radar (GPR) to detect soil layers in the modern Yellow River Delta of China and assessed its accuracy. It was found that soil moisture and salt had a strong dampening effect on the electromagnetic wave signal which resulted in blurred GPR images of the soil profile below 1 m. The cultivated soil layers of different crop types such as rice, wheat, corn, and cotton were accurately identified in GPR images. To estimate an individual soil layer thickness, the propagation velocity of the electromagnetic wave was calculated using soil mass moisture content, and the propagation time was confirmed by comparing the GPR image with the amplitude-time plot of the soil profile. The estimated thickness was 1.02 times the thickness determined in the field and the average estimation error was 0.04 m, which was 24.09% of the soil layer thickness determined in the field. The second derivative value of envelope amplitude energy with time (SDEA) was used to describe the amplitude change in the soil layers. The SDEA has negative logarithmic and power function relationships with soil mass moisture content and electrical conductivity, respectively. The present results provide a reference database for future quantitative soil investigation in the sedimentary plain area using GPR.","PeriodicalId":49771,"journal":{"name":"Near Surface Geophysics","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138717272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fracture curvature has been observed from the millimetre to the kilometre scales. Nevertheless, characterizing curvature remains challenging due to data sparsity and geometric ambiguities. As a result, most numerical models often assume planar fractures to ease computations. To address this limitation, we present a novel approach for inferring fracture geometry from travel-time data of electromagnetic or seismic waves. Our model utilizes co-kriging interpolation of control points in a 3D surface mesh to simulate fracture curvature effectively, resulting in an unstructured triangular grid. We then refine the fracture surface into a structured grid with equidistant elements so that both small-scale heterogeneities and large-scale curvature can be modelled. To constrain the fracture geometry, we perform a deterministic travel-time inversion to optimally place these control points. We validate our methodology with synthetic data and address its limitations. Finally, we infer the geometry of a large (more than 200 m) fracture observed in single-hole ground-penetrating radar (GPR) field data. The fracture surface closely agrees with borehole televiewer observations and is also constrained far from the boreholes. Our modelling approach can be trivially adapted to multi-offset GPR or active seismic data.
{"title":"Modelling and inferring fracture curvature from borehole GPR data: Case study from the Bedretto Laboratory, Switzerland","authors":"Daniel Escallon, Alexis Shakas, Hansruedi Maurer","doi":"10.1002/nsg.12286","DOIUrl":"https://doi.org/10.1002/nsg.12286","url":null,"abstract":"Fracture curvature has been observed from the millimetre to the kilometre scales. Nevertheless, characterizing curvature remains challenging due to data sparsity and geometric ambiguities. As a result, most numerical models often assume planar fractures to ease computations. To address this limitation, we present a novel approach for inferring fracture geometry from travel-time data of electromagnetic or seismic waves. Our model utilizes co-kriging interpolation of control points in a 3D surface mesh to simulate fracture curvature effectively, resulting in an unstructured triangular grid. We then refine the fracture surface into a structured grid with equidistant elements so that both small-scale heterogeneities and large-scale curvature can be modelled. To constrain the fracture geometry, we perform a deterministic travel-time inversion to optimally place these control points. We validate our methodology with synthetic data and address its limitations. Finally, we infer the geometry of a large (more than 200 m) fracture observed in single-hole ground-penetrating radar (GPR) field data. The fracture surface closely agrees with borehole televiewer observations and is also constrained far from the boreholes. Our modelling approach can be trivially adapted to multi-offset GPR or active seismic data.","PeriodicalId":49771,"journal":{"name":"Near Surface Geophysics","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138531409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Surface wave (SW) methods extract dispersion properties of wavefields propagating through a seismic array (1D or 2D). This is achieved by analysing the phase velocity versus frequency (or wavelength) data. Afterwards, an inversion process is performed to construct near-surface S-wave velocity models. Among the SW methods, multichannel analysis of SWs (MASW) is commonly used for engineering applications, analysing dispersion characteristics by generating a dispersion image. However, classical MASW depends on the manual picking of dispersion curves, which can lead to subjective outcomes and require time and effort to obtain precise results. To avoid these pitfalls, many studies, including deep-learning techniques, have focused on automating the process. Similarly, we propose a deep-learning-based algorithm that estimates the S-wave velocity directly from the dispersion image of SWs. This algorithm consists of a convolutional neural network (CNN) designed to directly yield S-wave velocity profiles and a fully connected network (multi-layer perceptron) added to regularize predictions. Unlike typical SW techniques, the proposed approach does not incorporate prior information such as layer count and thickness. To ensure successful training, we modified the loss function to exploit the normalized mean squared error. The training dataset was generated by modelling synthetic shot gathers and transforming them into dispersion images for various 1D stratified velocity structures. After a sample is fed to the CNN network for inversion, the inversion network's output subsequently goes through an additional simple neural network (NN) to regularize the predicted S-wave velocity model (which is the post-processing step). The combined usage of deep-learning-based SW inversion with NN-based post-processing was assessed using test data. The proposed algorithm achieved an average relative error of approximately 7.49% in predicting the S-wave velocity and was successfully applied to the field data. Additionally, we discuss its performance on noisy data as well as its applicability to out-of-training data. Numerical examples demonstrated that the proposed method is robust to noise, whereas it requires additional training to handle data beyond the distribution of the training data.
{"title":"Prediction of S-wave velocity models from surface waves using deep learning","authors":"Sangin Cho, Sukjoon Pyun, Byunghoon Choi, Ganghoon Lee, Seonghyung Jang, Yunseok Choi","doi":"10.1002/nsg.12284","DOIUrl":"https://doi.org/10.1002/nsg.12284","url":null,"abstract":"Surface wave (SW) methods extract dispersion properties of wavefields propagating through a seismic array (1D or 2D). This is achieved by analysing the phase velocity versus frequency (or wavelength) data. Afterwards, an inversion process is performed to construct near-surface S-wave velocity models. Among the SW methods, multichannel analysis of SWs (MASW) is commonly used for engineering applications, analysing dispersion characteristics by generating a dispersion image. However, classical MASW depends on the manual picking of dispersion curves, which can lead to subjective outcomes and require time and effort to obtain precise results. To avoid these pitfalls, many studies, including deep-learning techniques, have focused on automating the process. Similarly, we propose a deep-learning-based algorithm that estimates the S-wave velocity directly from the dispersion image of SWs. This algorithm consists of a convolutional neural network (CNN) designed to directly yield S-wave velocity profiles and a fully connected network (multi-layer perceptron) added to regularize predictions. Unlike typical SW techniques, the proposed approach does not incorporate prior information such as layer count and thickness. To ensure successful training, we modified the loss function to exploit the normalized mean squared error. The training dataset was generated by modelling synthetic shot gathers and transforming them into dispersion images for various 1D stratified velocity structures. After a sample is fed to the CNN network for inversion, the inversion network's output subsequently goes through an additional simple neural network (NN) to regularize the predicted S-wave velocity model (which is the post-processing step). The combined usage of deep-learning-based SW inversion with NN-based post-processing was assessed using test data. The proposed algorithm achieved an average relative error of approximately 7.49% in predicting the S-wave velocity and was successfully applied to the field data. Additionally, we discuss its performance on noisy data as well as its applicability to out-of-training data. Numerical examples demonstrated that the proposed method is robust to noise, whereas it requires additional training to handle data beyond the distribution of the training data.","PeriodicalId":49771,"journal":{"name":"Near Surface Geophysics","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138531366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The electrical resistivity tomography (ERT) method is often challenged by the presence of reinforced concrete (RC) in urban and industrial environments, because the embedded metallic wire mesh can severely distort the distribution of subsurface currents. We investigate one typical scenario in real applications, in which an RC floor overlays the natural topsoil or rock. Our synthetic forward simulations show that the embedded wire mesh behaves like a local good conductor in data of small source-receiver separations and acts like an equal-potential object that keeps the potential from decaying at large source-receiver separations. Routine ERT inversions that ignore the RC cannot work properly because the thin and highly conductive wire mesh may be manifested as large uninterpretable low-resistivity anomalies in the imaging results. Two remedies are adopted to improve the ERT resolution in such cases. First, we find a top layer with high conductivity in our model to adequately represent the wire mesh; then, we initiate the inversion with the top-layer model as the starting and reference model. This warm-start approach overcomes the difficulty of recovering the large conductivity contrast between metallic objects and regular earth materials. Second, underground electrodes are added to the survey array, so more information from depth can be obtained to fight against the dominance of current channelling in the wire mesh. Finally, our strategies are used to invert a real ERT dataset from an indoor manufacturing plant, where RC covers the entire floor of the building and electrodes are in contact with the soil through open holes in the floor. Our simulation and field data inversion verify our findings and demonstrate the effectiveness of our solutions in improving the resolution of ERT when the survey is carried out over RC floor in urban and industrial environments.
{"title":"Electrical resistivity tomography through reinforced concrete floor","authors":"Lichun Yang, Dikun Yang, Quan Yuan","doi":"10.1002/nsg.12285","DOIUrl":"https://doi.org/10.1002/nsg.12285","url":null,"abstract":"The electrical resistivity tomography (ERT) method is often challenged by the presence of reinforced concrete (RC) in urban and industrial environments, because the embedded metallic wire mesh can severely distort the distribution of subsurface currents. We investigate one typical scenario in real applications, in which an RC floor overlays the natural topsoil or rock. Our synthetic forward simulations show that the embedded wire mesh behaves like a local good conductor in data of small source-receiver separations and acts like an equal-potential object that keeps the potential from decaying at large source-receiver separations. Routine ERT inversions that ignore the RC cannot work properly because the thin and highly conductive wire mesh may be manifested as large uninterpretable low-resistivity anomalies in the imaging results. Two remedies are adopted to improve the ERT resolution in such cases. First, we find a top layer with high conductivity in our model to adequately represent the wire mesh; then, we initiate the inversion with the top-layer model as the starting and reference model. This warm-start approach overcomes the difficulty of recovering the large conductivity contrast between metallic objects and regular earth materials. Second, underground electrodes are added to the survey array, so more information from depth can be obtained to fight against the dominance of current channelling in the wire mesh. Finally, our strategies are used to invert a real ERT dataset from an indoor manufacturing plant, where RC covers the entire floor of the building and electrodes are in contact with the soil through open holes in the floor. Our simulation and field data inversion verify our findings and demonstrate the effectiveness of our solutions in improving the resolution of ERT when the survey is carried out over RC floor in urban and industrial environments.","PeriodicalId":49771,"journal":{"name":"Near Surface Geophysics","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138542109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Electromagnetic wave velocity in ground-penetrating radar (GPR) constant offset data can be estimated via the diffraction hyperbola fitting method. This method is applicable when radargrams contain diffraction events (hyperbolic patterns) caused by scatters in the host smaller or equal to the dominant wavelength. An alternative method for velocity estimation, if no intrusive information is available for a direct correlation, requires the collection of multi-offset data. The method is quite common for broad geophysical applications; however, it seems not to be fully utilized for engineering applications, such as slabs/walls where thickness estimation and depth of the embedded features are critical requirements for structural assessments. This method would also overcome the limitations in velocity calibration in environments with no hyperbolic signal signatures. The aim of this study is to explore multi-offset high-frequency GPR applications, specifically the wide-angle reflection and refraction method, for structural engineering, to understand whether it is feasible, possible limitations, and advantages. Numerical models reproducing reinforced concrete elements and a cavity wall were analysed to understand the wave behaviour and predict the response prior to testing on real cases. The main purpose is to explore how reinforcing bars can affect the velocity spectra derived from semblance analysis and what the response would be in a case of multi-layered structure with increasing velocity with depth (cavity wall). The comparison with real cases showed that, despite some intrinsic limitations, high-frequency multi-offset approach could be part of standard workflow for all those surveys where no other velocity estimation method is feasible.
{"title":"High-frequency wide-angle reflection and refraction method for structural engineering ground-penetrating radar surveys","authors":"Davide Campo","doi":"10.1002/nsg.12277","DOIUrl":"https://doi.org/10.1002/nsg.12277","url":null,"abstract":"Electromagnetic wave velocity in ground-penetrating radar (GPR) constant offset data can be estimated via the diffraction hyperbola fitting method. This method is applicable when radargrams contain diffraction events (hyperbolic patterns) caused by scatters in the host smaller or equal to the dominant wavelength. An alternative method for velocity estimation, if no intrusive information is available for a direct correlation, requires the collection of multi-offset data. The method is quite common for broad geophysical applications; however, it seems not to be fully utilized for engineering applications, such as slabs/walls where thickness estimation and depth of the embedded features are critical requirements for structural assessments. This method would also overcome the limitations in velocity calibration in environments with no hyperbolic signal signatures. The aim of this study is to explore multi-offset high-frequency GPR applications, specifically the wide-angle reflection and refraction method, for structural engineering, to understand whether it is feasible, possible limitations, and advantages. Numerical models reproducing reinforced concrete elements and a cavity wall were analysed to understand the wave behaviour and predict the response prior to testing on real cases. The main purpose is to explore how reinforcing bars can affect the velocity spectra derived from semblance analysis and what the response would be in a case of multi-layered structure with increasing velocity with depth (cavity wall). The comparison with real cases showed that, despite some intrinsic limitations, high-frequency multi-offset approach could be part of standard workflow for all those surveys where no other velocity estimation method is feasible.","PeriodicalId":49771,"journal":{"name":"Near Surface Geophysics","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138531373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Puy Ayarza, Mariano Yenes, Yolanda Sánchez Sánchez, Imma Palomeras, José R. Martínez Catalán, Enrique Gil-Arranz, Juan Gómez Barreiro
The renaissance botanical garden of ‘El Bosque’ in Béjar (Salamanca, Spain) presents a pond bounded by a dam in its western part. The latter is formed by two masonry walls interconnected by buttresses. Cubic spaces in between are filled with a variable grain-size material (silty sand) that allows limited water flow. In recent years the southern part of the dam has experienced localized and random subsidence that jeopardizes the entrance to part of the garden. To regain access, a proper and reliable diagnosis of the origin, magnitude and relevance of the subsidence must be made. In this regard, we have undertaken a microgravity survey in the dam area to identify places with an anomalous distribution of the filling material in order to foresee further sinking or potential collapsing areas. The precise positioning (2 mm resolution) and accurate terrain correction needed in this kind of high-resolution gravity surveys (points every 1.5 m) was achieved by creating a detailed Digital Terrain Model (cm resolution) with a remotely piloted aircraft. In addition, we performed three electric resistivity tomography (ERT) profiles at different levels of the garden: i) on the dam itself, ii) right on the foot of the dam and parallel to it (5 m below and ∼17m to the W), and iii) a bit farther, but also parallel to the dam (8 m below and ∼27m to the W). The ERT profiles identified high conductivity in water-saturated areas and determined the paths that rainfall and pond's seepage water follow in the dam and its underground, formed by granites. The geophysical studies were paired with geotechnical analyses of the sunk materials. The study concluded that the thinnest fraction of the dam's filling material (i.e., silts) is being washed away, leaving behind sand with less density and stability, susceptible to collapse. Thus, the observed sinking is related to soil piping, i.e. to soil internal erosion and compaction issues that force the soil material to re-adjust geometrically and volumetrically.
{"title":"Assessing the dam's stability of the pond at the ‘El Bosque’ renaissance garden (Béjar, Spain)","authors":"Puy Ayarza, Mariano Yenes, Yolanda Sánchez Sánchez, Imma Palomeras, José R. Martínez Catalán, Enrique Gil-Arranz, Juan Gómez Barreiro","doi":"10.1002/nsg.12283","DOIUrl":"https://doi.org/10.1002/nsg.12283","url":null,"abstract":"The renaissance botanical garden of ‘El Bosque’ in Béjar (Salamanca, Spain) presents a pond bounded by a dam in its western part. The latter is formed by two masonry walls interconnected by buttresses. Cubic spaces in between are filled with a variable grain-size material (silty sand) that allows limited water flow. In recent years the southern part of the dam has experienced localized and random subsidence that jeopardizes the entrance to part of the garden. To regain access, a proper and reliable diagnosis of the origin, magnitude and relevance of the subsidence must be made. In this regard, we have undertaken a microgravity survey in the dam area to identify places with an anomalous distribution of the filling material in order to foresee further sinking or potential collapsing areas. The precise positioning (2 mm resolution) and accurate terrain correction needed in this kind of high-resolution gravity surveys (points every 1.5 m) was achieved by creating a detailed Digital Terrain Model (cm resolution) with a remotely piloted aircraft. In addition, we performed three electric resistivity tomography (ERT) profiles at different levels of the garden: i) on the dam itself, ii) right on the foot of the dam and parallel to it (5 m below and ∼17m to the W), and iii) a bit farther, but also parallel to the dam (8 m below and ∼27m to the W). The ERT profiles identified high conductivity in water-saturated areas and determined the paths that rainfall and pond's seepage water follow in the dam and its underground, formed by granites. The geophysical studies were paired with geotechnical analyses of the sunk materials. The study concluded that the thinnest fraction of the dam's filling material (i.e., silts) is being washed away, leaving behind sand with less density and stability, susceptible to collapse. Thus, the observed sinking is related to soil piping, i.e. to soil internal erosion and compaction issues that force the soil material to re-adjust geometrically and volumetrically.","PeriodicalId":49771,"journal":{"name":"Near Surface Geophysics","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138531360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sam Stadler, Stephan Schennen, Thomas Hiller, Jan Igel
Abstract Ground‐penetrating radar (GPR) is an effective tool for detecting landmines and improvised explosive devices (IEDs), and its performance is strongly influenced by subsurface properties as well as the characteristics of the target. To complement or replace labour‐intensive experiments on test sites, cost‐efficient electromagnetic wave propagation simulations using the Finite‐Difference Time‐Domain (FDTD) method are being increasingly used. However, to obtain realistic synthetic data, accurate modelling of signal alteration caused by dispersion, scattering of soil material, target contrast, shape, and inner setup, as well as the coupling effects of the antenna to the ground is required. In this study, we present a detailed 3D model of a shielded GPR antenna applied to various scenarios containing metallic and non‐metallic targets buried in different soils. The frequency‐dependent intrinsic material properties of soil samples were measured with the coaxial transmission‐line technique, while a discrete random media was used to implement the heterogeneity of a gravel based on its grain‐size distribution. Our simulations show very good agreement with experimental validation data collected under controlled conditions. We accurately reproduce the amplitude and frequency content, phase of target signals, subsurface's background noise, antenna crosstalk and its interference with target signals, and the effect of antenna elevation. The approach allows for systematic investigation of the effects of soil, target, and sensor properties on detection performance, providing insight into novel and complex GPR scenarios and the potential for a wide range of simulation possibilities for demining with GPR. These investigations have the potential to improve the safety and effectiveness of landmine and IED detection in the future, such as building a database for training deminers or developing automatic signal pattern recognition algorithms. This article is protected by copyright. All rights reserved
{"title":"Realistic simulation of GPR for landmine and IED detection including antenna models, soil dispersion and heterogeneity","authors":"Sam Stadler, Stephan Schennen, Thomas Hiller, Jan Igel","doi":"10.1002/nsg.12282","DOIUrl":"https://doi.org/10.1002/nsg.12282","url":null,"abstract":"Abstract Ground‐penetrating radar (GPR) is an effective tool for detecting landmines and improvised explosive devices (IEDs), and its performance is strongly influenced by subsurface properties as well as the characteristics of the target. To complement or replace labour‐intensive experiments on test sites, cost‐efficient electromagnetic wave propagation simulations using the Finite‐Difference Time‐Domain (FDTD) method are being increasingly used. However, to obtain realistic synthetic data, accurate modelling of signal alteration caused by dispersion, scattering of soil material, target contrast, shape, and inner setup, as well as the coupling effects of the antenna to the ground is required. In this study, we present a detailed 3D model of a shielded GPR antenna applied to various scenarios containing metallic and non‐metallic targets buried in different soils. The frequency‐dependent intrinsic material properties of soil samples were measured with the coaxial transmission‐line technique, while a discrete random media was used to implement the heterogeneity of a gravel based on its grain‐size distribution. Our simulations show very good agreement with experimental validation data collected under controlled conditions. We accurately reproduce the amplitude and frequency content, phase of target signals, subsurface's background noise, antenna crosstalk and its interference with target signals, and the effect of antenna elevation. The approach allows for systematic investigation of the effects of soil, target, and sensor properties on detection performance, providing insight into novel and complex GPR scenarios and the potential for a wide range of simulation possibilities for demining with GPR. These investigations have the potential to improve the safety and effectiveness of landmine and IED detection in the future, such as building a database for training deminers or developing automatic signal pattern recognition algorithms. This article is protected by copyright. All rights reserved","PeriodicalId":49771,"journal":{"name":"Near Surface Geophysics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135636254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Timely and accurate detection of water pipe leakage is critical to preventing the loss of freshwater and predicting potential hazards induced by the change in underground water conditions, thereby developing mitigation strategies to improve the resilience of pipeline infrastructure. Ground Penetrating Radar (GPR) has been widely applied to investigating ground conditions and detecting pipe leakage. However, due to uncertainties of complex underground environments and time‐lapse change, a proper interpretation of GPR data has been a challenging task. This paper aims to leverage hydromechanical (HM) modelling to predict electromagnetic (EM) responses of water leakage detection in diverse leakage cases. A high‐fidelity 3D digital model of an actual pipeline network, hosting pipes with various sizes and materials, was reconstructed to represent the complex geometry and various mediums. The interoperability between the digital model and the numerical models utilised in HM and EM simulations was improved to better capture the irregular pipelines. Based on Kriging interpolation and the volumetric Complex Refractive Index Model (CRIM), a linking technique was employed to replicate material heterogeneity caused by water intrusion. Thus, a framework was developed to accommodate the interoperability among digital modelling, HM modelling, and Finite‐Difference Time‐Domain (FDTD) forward modelling. Moreover, sensitivity studies were conducted to evaluate the influences of different time stages, leak positions, and pipe types on GPR responses. In GPR B‐scans, the presence of hyperbolic motion and horizontal reflections serve as indicators to estimate the location and scale of water leakage. Specifically, a downward‐shifting hyperbola indicates that the pipeline is submerged by leaked water, while the emergence of horizontal reflection is linked to the wetting front of saturated areas. The developed framework can be expanded for complicated applications, such as unknown locations and unforeseen failure modes of pipelines. It will increase the efficiency and accuracy of traditional interpretations of GPR‐based water leakage detection and thus enable automated interpretations by data‐driven methods. This article is protected by copyright. All rights reserved
{"title":"A Framework for GPR‐based Water Leakage Detection by Integrating Hydromechanical Modelling into Electromagnetic Modelling","authors":"Huamei Zhu, Feng Xiao, Yimin Zhou, Wallace Wai Lok Lai, Qianbing Zhang","doi":"10.1002/nsg.12281","DOIUrl":"https://doi.org/10.1002/nsg.12281","url":null,"abstract":"Abstract Timely and accurate detection of water pipe leakage is critical to preventing the loss of freshwater and predicting potential hazards induced by the change in underground water conditions, thereby developing mitigation strategies to improve the resilience of pipeline infrastructure. Ground Penetrating Radar (GPR) has been widely applied to investigating ground conditions and detecting pipe leakage. However, due to uncertainties of complex underground environments and time‐lapse change, a proper interpretation of GPR data has been a challenging task. This paper aims to leverage hydromechanical (HM) modelling to predict electromagnetic (EM) responses of water leakage detection in diverse leakage cases. A high‐fidelity 3D digital model of an actual pipeline network, hosting pipes with various sizes and materials, was reconstructed to represent the complex geometry and various mediums. The interoperability between the digital model and the numerical models utilised in HM and EM simulations was improved to better capture the irregular pipelines. Based on Kriging interpolation and the volumetric Complex Refractive Index Model (CRIM), a linking technique was employed to replicate material heterogeneity caused by water intrusion. Thus, a framework was developed to accommodate the interoperability among digital modelling, HM modelling, and Finite‐Difference Time‐Domain (FDTD) forward modelling. Moreover, sensitivity studies were conducted to evaluate the influences of different time stages, leak positions, and pipe types on GPR responses. In GPR B‐scans, the presence of hyperbolic motion and horizontal reflections serve as indicators to estimate the location and scale of water leakage. Specifically, a downward‐shifting hyperbola indicates that the pipeline is submerged by leaked water, while the emergence of horizontal reflection is linked to the wetting front of saturated areas. The developed framework can be expanded for complicated applications, such as unknown locations and unforeseen failure modes of pipelines. It will increase the efficiency and accuracy of traditional interpretations of GPR‐based water leakage detection and thus enable automated interpretations by data‐driven methods. This article is protected by copyright. All rights reserved","PeriodicalId":49771,"journal":{"name":"Near Surface Geophysics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135808953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jianyu Ling, Rongyi Qian, Jun Zhang, Zhenning Ma, Xu Liu
Abstract Determining of ground‐penetrating radar (GPR) velocity has always been a critical problem. The GPR velocity estimation method based on common midpoint (CMP) data has been widely used because of its simplicity. However, we found that in sediment investigation and soil assessment, transversal heterogeneity is universal, which violates the basic assumption of velocity estimation through CMP data. Due to the rapid change of underground media and the limitation of the scope of some surveying areas, the CMP survey line will inevitably be selected in the area where the velocity changes laterally, making it difficult to obtain accurate velocity. To address this problem, we propose a velocity correction method. First, we determined the characteristics of CMP data and the corresponding velocity spectra acquired in transversely heterogeneous media through numerical simulations. Subsequently, we found that the simulated CMP data could determine the location of changes in the underground medium and that the velocity obtained from the semblance analysis could be corrected according to the location where the medium changes laterally. We then used models wherein the thickness, relative permittivity, and proportion of abnormal parts varied independently or simultaneously to verify the proposed velocity correction method. The results show that our method can control the GPR velocity error within 3.44% and the precision is about 0.002 m/ns. Finally, we conducted a sediment investigation experiment on a channel bar in the lower reaches of the Yarlung Zangbo River. We determined the interface at which the sediment changed transversely and obtained the corresponding electromagnetic (EM) velocity using the proposed method. This study provides a reliable method for determining the GPR velocity in transversal heterogeneous media, which is of great significance for various practical applications. This article is protected by copyright. All rights reserved
{"title":"GPR velocity correction method in transversely heterogeneous media based on CMP data","authors":"Jianyu Ling, Rongyi Qian, Jun Zhang, Zhenning Ma, Xu Liu","doi":"10.1002/nsg.12278","DOIUrl":"https://doi.org/10.1002/nsg.12278","url":null,"abstract":"Abstract Determining of ground‐penetrating radar (GPR) velocity has always been a critical problem. The GPR velocity estimation method based on common midpoint (CMP) data has been widely used because of its simplicity. However, we found that in sediment investigation and soil assessment, transversal heterogeneity is universal, which violates the basic assumption of velocity estimation through CMP data. Due to the rapid change of underground media and the limitation of the scope of some surveying areas, the CMP survey line will inevitably be selected in the area where the velocity changes laterally, making it difficult to obtain accurate velocity. To address this problem, we propose a velocity correction method. First, we determined the characteristics of CMP data and the corresponding velocity spectra acquired in transversely heterogeneous media through numerical simulations. Subsequently, we found that the simulated CMP data could determine the location of changes in the underground medium and that the velocity obtained from the semblance analysis could be corrected according to the location where the medium changes laterally. We then used models wherein the thickness, relative permittivity, and proportion of abnormal parts varied independently or simultaneously to verify the proposed velocity correction method. The results show that our method can control the GPR velocity error within 3.44% and the precision is about 0.002 m/ns. Finally, we conducted a sediment investigation experiment on a channel bar in the lower reaches of the Yarlung Zangbo River. We determined the interface at which the sediment changed transversely and obtained the corresponding electromagnetic (EM) velocity using the proposed method. This study provides a reliable method for determining the GPR velocity in transversal heterogeneous media, which is of great significance for various practical applications. This article is protected by copyright. All rights reserved","PeriodicalId":49771,"journal":{"name":"Near Surface Geophysics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136157069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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