Data-driven deep learning technology has a strong non-linear mapping ability and has good development potential in geophysical inversion problems. Traditional inversion techniques offer broad generality, but they can remain trapped in local minima, particularly for three-dimensional tunnelling resistivity inversion. In this work, we present an inversion methodology that combines traditional physics-driven and deep learning data-driven inversion approaches. To further support deep neural networks' dependability on unseen data, the interpretability of their working mechanism is explored. We execute migration learning based on small sample data after identifying the critical parameters that restrict the effectiveness of inversion by analysing the feature maps of various model data. We demonstrate, using both synthetic examples and field data, that the proposed method can improve the accuracy in detecting water-bearing anomalies (caves and faults), which are typically encountered during tunnel excavation.
{"title":"Tunnel resistivity deep learning inversion method based on physics-driven and signal interpretability","authors":"Benchao Liu, Yuting Tang, Yongheng Zhang, Peng Jiang, Fengkai Zhang","doi":"10.1002/nsg.12294","DOIUrl":"https://doi.org/10.1002/nsg.12294","url":null,"abstract":"Data-driven deep learning technology has a strong non-linear mapping ability and has good development potential in geophysical inversion problems. Traditional inversion techniques offer broad generality, but they can remain trapped in local minima, particularly for three-dimensional tunnelling resistivity inversion. In this work, we present an inversion methodology that combines traditional physics-driven and deep learning data-driven inversion approaches. To further support deep neural networks' dependability on unseen data, the interpretability of their working mechanism is explored. We execute migration learning based on small sample data after identifying the critical parameters that restrict the effectiveness of inversion by analysing the feature maps of various model data. We demonstrate, using both synthetic examples and field data, that the proposed method can improve the accuracy in detecting water-bearing anomalies (caves and faults), which are typically encountered during tunnel excavation.","PeriodicalId":49771,"journal":{"name":"Near Surface Geophysics","volume":"2 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140071390","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}
Eleni Tokmaktsi, Nectaria Diamanti, Georgios Vargemezis, Antonios Giannopoulos, A. Peter Annan
As the transmitter and receiver (Tx and Rx, respectively) are located in close proximity during a typical ground-penetrating radar (GPR) survey, the powerful signal generated by the Tx and which is then recorded by the Rx at various time delays, can be saturated at early times (i.e., this is the direct wave (DW) signal reaching the Rx). This often causes the masking of shallow targets, complicating data interpretation. In this study, our aim is to examine the spatial distribution of the electromagnetic signals around the Tx, attempting to locate areas where the DW becomes minimum, whereas the signal strength from subsurface targets (i.e., reflected wave – RW) remains ideally unchanged. The position of these local minima in the DW signal could give rise to advantageous Tx–Rx configurations, where clear reflections from subsurface targets lying at shallow depths can be obtained with the least possible involvement of the DW. To perform such a study, we carried out static field measurements over a flat lying reflector as well as numerical simulations in a reflection, common-offset mode around a transmitting antenna. In the field, we also collected wide-angle reflection–refraction data to determine the GPR wave velocity in the uppermost layer. GPR signals were recorded by the Rx around the Tx in three concentric circles of various radii (i.e., varying the Tx/Rx separation), using a specific angular step and varying the Tx/Rx polarization each time. The synthetic data were produced using a three-dimensional finite-difference time-domain modelling tool. Field and numerically simulated data were analysed and compared to study the behaviour of both the DW and RW events around the Tx when changing the Tx/Rx distance, their respective angular position, as well as their relative polarization/orientation.
{"title":"Studying GPR's direct and reflected waves","authors":"Eleni Tokmaktsi, Nectaria Diamanti, Georgios Vargemezis, Antonios Giannopoulos, A. Peter Annan","doi":"10.1002/nsg.12292","DOIUrl":"https://doi.org/10.1002/nsg.12292","url":null,"abstract":"As the transmitter and receiver (Tx and Rx, respectively) are located in close proximity during a typical ground-penetrating radar (GPR) survey, the powerful signal generated by the Tx and which is then recorded by the Rx at various time delays, can be saturated at early times (i.e., this is the direct wave (DW) signal reaching the Rx). This often causes the masking of shallow targets, complicating data interpretation. In this study, our aim is to examine the spatial distribution of the electromagnetic signals around the Tx, attempting to locate areas where the DW becomes minimum, whereas the signal strength from subsurface targets (i.e., reflected wave – RW) remains ideally unchanged. The position of these local minima in the DW signal could give rise to advantageous Tx–Rx configurations, where clear reflections from subsurface targets lying at shallow depths can be obtained with the least possible involvement of the DW. To perform such a study, we carried out static field measurements over a flat lying reflector as well as numerical simulations in a reflection, common-offset mode around a transmitting antenna. In the field, we also collected wide-angle reflection–refraction data to determine the GPR wave velocity in the uppermost layer. GPR signals were recorded by the Rx around the Tx in three concentric circles of various radii (i.e., varying the Tx/Rx separation), using a specific angular step and varying the Tx/Rx polarization each time. The synthetic data were produced using a three-dimensional finite-difference time-domain modelling tool. Field and numerically simulated data were analysed and compared to study the behaviour of both the DW and RW events around the Tx when changing the Tx/Rx distance, their respective angular position, as well as their relative polarization/orientation.","PeriodicalId":49771,"journal":{"name":"Near Surface Geophysics","volume":"53 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140071350","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 information from seismic data can be used for near‐surface analysis, static computation and noise suppression. The multichannel analysis of surface waves method is a useful approach for obtaining the shear wave velocity of the near surface; however, rapidly generating an image of dispersive energy in the presence of coherent noise is a challenge. In this study, we propose the imaging of the dispersive energy of the Rayleigh wave using a spatial smoothing propagation method. In this method, forward and backward spatial smoothing algorithms were used to restore the rank of the covariance matrix in strong coherent noise. Subsequently, an image of the dispersive energy was rapidly generated by the propagation method using a linear operation equivalent to the eigenvalue decomposition. The proposed method was evaluated using both synthetic and field data. The results showed that the method was easy to use and has higher resolution representation, efficiency and noise robustness compared with conventional methods.
{"title":"Efficient and high‐resolution surface‐wave dispersive energy imaging using a proposed spatial smoothing propagation method","authors":"Tao He, Suping Peng, Henggao Geng","doi":"10.1002/nsg.12291","DOIUrl":"https://doi.org/10.1002/nsg.12291","url":null,"abstract":"Surface‐wave information from seismic data can be used for near‐surface analysis, static computation and noise suppression. The multichannel analysis of surface waves method is a useful approach for obtaining the shear wave velocity of the near surface; however, rapidly generating an image of dispersive energy in the presence of coherent noise is a challenge. In this study, we propose the imaging of the dispersive energy of the Rayleigh wave using a spatial smoothing propagation method. In this method, forward and backward spatial smoothing algorithms were used to restore the rank of the covariance matrix in strong coherent noise. Subsequently, an image of the dispersive energy was rapidly generated by the propagation method using a linear operation equivalent to the eigenvalue decomposition. The proposed method was evaluated using both synthetic and field data. The results showed that the method was easy to use and has higher resolution representation, efficiency and noise robustness compared with conventional methods.","PeriodicalId":49771,"journal":{"name":"Near Surface Geophysics","volume":"130 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140036983","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}
Benyamin Shariatinik, Erwan Gloaguen, Jasmin Raymond, Louis-Charles Boutin, Gabriel Fabien-Ouellet
Geothermal Energy Systems such as heat pump relying on aquifers uses renewable sources of energy that are accessible in urban areas. It is necessary to characterize the subsurface hydraulic properties prior to the installation of such systems. In this context, heat tracing experiment is a typical field test that can help with characterization of the subsurface. During a heat tracing experiment, monitoring with downhole temperature sensors, water-level pressure transducers and electrical resistivity tomography (ERT) can be used to help to characterize the hydrogeological properties. Previous monitoring tools have shortcomings such as low-resolution data and over-smoothing, thus they fail to reproduce the heterogeneity of hydrogeological properties. Ensemble Kalman filter (EnKF) is a promising tool that can overcome the over-smoothing problem to replicate the hydrogeological property heterogeneity. In this work, we proposed a new procedure to assimilate time-lapse cross-borehole ERT data into a numerical model of groundwater flow and heat transfer, where the ground water is extracted, and heated water is reinjected into an unconfined sandy-gravel aquifer. The finite element model (FEFLOW 7.3) of groundwater flow and heat transfer is integrated with petrophysical relationship and electrical forward modeling (Resipy) to estimate cross-borehole ERT measurements. Then, the estimated apparent resistivity is assimilated to update the hydraulic conductivity model using EnKF. The results of the application of the proposed approach to a experimental site located in Quebec City (Canada) demonstrate that the heterogeneity of K is correctly reproduce since the updated K model is reasonably consistent with the lithological log. In addition, the proposed approach was able to replicate the cross-borehole ERT field and temperature measurements. The comparison between prior and posterior distribution of K with slug test results shows that the EnKF made the final (assimilated) distribution of K move toward K values inferred with slug tests.
地热能源系统（如依靠含水层的热泵）使用的是城市地区可以利用的可再生能源。在安装此类系统之前，有必要确定地下水的水力特性。在这种情况下，热跟踪实验是一种典型的现场测试，可帮助确定地下水的特性。在热跟踪实验过程中，可使用井下温度传感器、水位压力传感器和电阻率层析成像（ERT）进行监测，以帮助确定水文地质特性的特征。以往的监测工具存在低分辨率数据和过度平滑等缺陷，因此无法再现水文地质特性的异质性。集合卡尔曼滤波器（EnKF）是一种很有前途的工具，它可以克服过度平滑问题，从而再现水文地质属性的异质性。在这项工作中，我们提出了一种新的程序，将延时跨钻孔 ERT 数据同化到地下水流和热传导的数值模型中。地下水流动和传热有限元模型（FEFLOW 7.3）与岩石物理关系和电正演模型（Resipy）相结合，估算跨钻孔 ERT 测量值。然后，利用 EnKF 同化估算的视电阻率，更新水力传导模型。在位于加拿大魁北克市的一个实验点应用该方法的结果表明，由于更新后的 K 模型与岩性记录相当一致，K 的异质性得到了正确再现。此外，所提出的方法还能够复制跨钻孔 ERT 场和温度测量结果。K 的先验分布和后验分布与岩浆测试结果的比较表明，EnKF 使 K 的最终（同化）分布趋向于岩浆测试推断的 K 值。
{"title":"ERT Data Assimilation to Characterize Aquifer Hydraulic Conductivity Heterogeneity through a Heat-tracing Experiment","authors":"Benyamin Shariatinik, Erwan Gloaguen, Jasmin Raymond, Louis-Charles Boutin, Gabriel Fabien-Ouellet","doi":"10.1002/nsg.12288","DOIUrl":"https://doi.org/10.1002/nsg.12288","url":null,"abstract":"Geothermal Energy Systems such as heat pump relying on aquifers uses renewable sources of energy that are accessible in urban areas. It is necessary to characterize the subsurface hydraulic properties prior to the installation of such systems. In this context, heat tracing experiment is a typical field test that can help with characterization of the subsurface. During a heat tracing experiment, monitoring with downhole temperature sensors, water-level pressure transducers and electrical resistivity tomography (ERT) can be used to help to characterize the hydrogeological properties. Previous monitoring tools have shortcomings such as low-resolution data and over-smoothing, thus they fail to reproduce the heterogeneity of hydrogeological properties. Ensemble Kalman filter (EnKF) is a promising tool that can overcome the over-smoothing problem to replicate the hydrogeological property heterogeneity. In this work, we proposed a new procedure to assimilate time-lapse cross-borehole ERT data into a numerical model of groundwater flow and heat transfer, where the ground water is extracted, and heated water is reinjected into an unconfined sandy-gravel aquifer. The finite element model (FEFLOW 7.3) of groundwater flow and heat transfer is integrated with petrophysical relationship and electrical forward modeling (Resipy) to estimate cross-borehole ERT measurements. Then, the estimated apparent resistivity is assimilated to update the hydraulic conductivity model using EnKF. The results of the application of the proposed approach to a experimental site located in Quebec City (Canada) demonstrate that the heterogeneity of K is correctly reproduce since the updated K model is reasonably consistent with the lithological log. In addition, the proposed approach was able to replicate the cross-borehole ERT field and temperature measurements. The comparison between prior and posterior distribution of K with slug test results shows that the EnKF made the final (assimilated) distribution of K move toward K values inferred with slug tests.","PeriodicalId":49771,"journal":{"name":"Near Surface Geophysics","volume":"23 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139027917","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}
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":"69 1 1","pages":""},"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":"76 1","pages":""},"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":"28 1","pages":""},"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":"162 1","pages":""},"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":"35 1","pages":""},"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}
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