Quantification of non‐uniqueness and uncertainty is important for transient electromagnetism (TEM). To address this issue, we develop a trans‐dimensional Bayesian inversion schema for TEM data interpretation. The trans‐dimensional posterior probability density (PPD) offers a solution to model selection and quantifies parameter uncertainty resulting from the model selection from all possible models rather than determining a single model. We use the reversible‐jump Markov chain Monte Carlo sampler to draw ensembles of models to approximate PPD. In addition to providing reasonable model selection, we address the reliability of the inversion results for uncertainty analysis. This strategy offers reasonable guidance when interpreting the inversion results. We make the following improvements in this paper. First, in terms of algorithmic acceleration, we use the nonlinear optimization inversion results as the initial model and implement the multi‐chain parallel method. Second, we develop double factors to control the sampling step size of the proposed distribution, so that the sampling models cover the high‐probability region of the parameter space as much as possible. Finally, we provide the potential scale reduction factor‐η convergence criteria to assess the convergence of the samples and ensure the rationality of the output models. The proposed methodology is first tested on synthetic data and subsequently applied to a field dataset. The TEM inversion results show that probability inversion can provide reliable references for data interpretation through uncertainty analysis.
非唯一性和不确定性的量化对于瞬态电磁学（TEM）非常重要。为解决这一问题，我们开发了一种用于 TEM 数据解释的跨维贝叶斯反演模式。跨维后验概率密度（PPD）为模型选择提供了一种解决方案，并量化了从所有可能模型中选择模型而不是确定单一模型所产生的参数不确定性。我们使用可逆跳转马尔科夫链蒙特卡洛采样器绘制模型集合，以近似 PPD。除了提供合理的模型选择，我们还解决了不确定性分析中反演结果的可靠性问题。这一策略为解释反演结果提供了合理的指导。我们在本文中做了以下改进。首先，在算法加速方面，我们将非线性优化反演结果作为初始模型，并实现了多链并行方法。其次，我们开发了双因子来控制建议分布的采样步长，从而使采样模型尽可能覆盖参数空间的高概率区域。最后，我们提供了潜在规模缩减因子-η收敛标准来评估样本的收敛性，确保输出模型的合理性。建议的方法首先在合成数据上进行了测试，随后应用于实地数据集。TEM 反演结果表明，概率反演可通过不确定性分析为数据解释提供可靠的参考。
{"title":"Bayesian inversion and uncertainty analysis","authors":"Nuoya Zhang, Huaifeng Sun, Dong Liu, Shangbin Liu","doi":"10.1002/nsg.12299","DOIUrl":"https://doi.org/10.1002/nsg.12299","url":null,"abstract":"Quantification of non‐uniqueness and uncertainty is important for transient electromagnetism (TEM). To address this issue, we develop a trans‐dimensional Bayesian inversion schema for TEM data interpretation. The trans‐dimensional posterior probability density (PPD) offers a solution to model selection and quantifies parameter uncertainty resulting from the model selection from all possible models rather than determining a single model. We use the reversible‐jump Markov chain Monte Carlo sampler to draw ensembles of models to approximate PPD. In addition to providing reasonable model selection, we address the reliability of the inversion results for uncertainty analysis. This strategy offers reasonable guidance when interpreting the inversion results. We make the following improvements in this paper. First, in terms of algorithmic acceleration, we use the nonlinear optimization inversion results as the initial model and implement the multi‐chain parallel method. Second, we develop double factors to control the sampling step size of the proposed distribution, so that the sampling models cover the high‐probability region of the parameter space as much as possible. Finally, we provide the potential scale reduction factor‐<jats:italic>η</jats:italic> convergence criteria to assess the convergence of the samples and ensure the rationality of the output models. The proposed methodology is first tested on synthetic data and subsequently applied to a field dataset. The TEM inversion results show that probability inversion can provide reliable references for data interpretation through uncertainty analysis.","PeriodicalId":49771,"journal":{"name":"Near Surface Geophysics","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140637390","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}
Multi‐channel analysis of surface waves is a seismic method employed to obtain useful information about shear‐wave velocities in the near surface. A fundamental step in this methodology is the extraction of dispersion curves from dispersion spectra, with the latter usually obtained by applying specific processing algorithms onto the recorded shot gathers. Although the extraction process can be automated to some extent, it usually requires extensive quality control, which can be arduous for large datasets. We present a novel approach that leverages deep learning to identify a direct mapping between seismic shot gathers and their associated dispersion curves (both fundamental and first higher order modes), therefore by‐passing the need to compute dispersion spectra. Given a site of interest, a set of 1D compressional and shear velocities and density models are created using prior knowledge of the local geology; pairs of seismic shot gathers and Rayleigh‐wave phase dispersion curves are then numerically modelled and used to train a simplified residual network. The proposed approach is shown to achieve high‐quality predictions of dispersion curves on a synthetic test dataset and is, ultimately, successfully deployed on a field dataset. Various uncertainty quantification and convolutional neural network visualization techniques are also presented to assess the quality of the inference process and better understand the underlying learning process of the network. The predicted dispersion curves are inverted for both the synthetic and field data; in the latter case, the resulting shear‐wave velocity model is plausible and consistent with prior geological knowledge of the area. Finally, a comparison between the manually picked fundamental modes with the predictions from our model allows for a benchmark of the performance of the proposed workflow.
{"title":"Deep learning‐based extraction of surface wave dispersion curves from seismic shot gathers","authors":"Danilo Chamorro, Jiahua Zhao, Claire Birnie, Myrna Staring, Moritz Fliedner, Matteo Ravasi","doi":"10.1002/nsg.12298","DOIUrl":"https://doi.org/10.1002/nsg.12298","url":null,"abstract":"Multi‐channel analysis of surface waves is a seismic method employed to obtain useful information about shear‐wave velocities in the near surface. A fundamental step in this methodology is the extraction of dispersion curves from dispersion spectra, with the latter usually obtained by applying specific processing algorithms onto the recorded shot gathers. Although the extraction process can be automated to some extent, it usually requires extensive quality control, which can be arduous for large datasets. We present a novel approach that leverages deep learning to identify a direct mapping between seismic shot gathers and their associated dispersion curves (both fundamental and first higher order modes), therefore by‐passing the need to compute dispersion spectra. Given a site of interest, a set of 1D compressional and shear velocities and density models are created using prior knowledge of the local geology; pairs of seismic shot gathers and Rayleigh‐wave phase dispersion curves are then numerically modelled and used to train a simplified residual network. The proposed approach is shown to achieve high‐quality predictions of dispersion curves on a synthetic test dataset and is, ultimately, successfully deployed on a field dataset. Various uncertainty quantification and convolutional neural network visualization techniques are also presented to assess the quality of the inference process and better understand the underlying learning process of the network. The predicted dispersion curves are inverted for both the synthetic and field data; in the latter case, the resulting shear‐wave velocity model is plausible and consistent with prior geological knowledge of the area. Finally, a comparison between the manually picked fundamental modes with the predictions from our model allows for a benchmark of the performance of the proposed workflow.","PeriodicalId":49771,"journal":{"name":"Near Surface Geophysics","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140563680","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}
Han Che, Hongyan Shen, Qingchun Li, Guoxin Liu, Chenrui Yang, Yunpeng Sun, Shuai Liu
Dispersion curve inversion is one of the core contents of Rayleigh wave data processing. However, the dispersion curve inversion has the characteristics of multi‐parameter, multi‐extremum as well as nonlinearity. In the face of Rayleigh wave data processing under complex seismic‐geological conditions, it is difficult to reconstruct an underground structure quickly and accurately apply a single global‐searching non‐linear inversion algorithm. For this reason, we proposed a strategy to invert multi‐order mode Rayleigh wave dispersion curves by combining with grey wolf optimization (GWO) and cuckoo search (CS) algorithms. On the basis of introducing the mechanism of iterative chaotic map with infinite collapses (ICMIC) and the strategy of dimension learning–based hunting (DLH), an improved GWO was developed that was called IDGWO (ICMIC and DLH GWO). After searching the near‐optimal region through IDGWO, the CS with a variable step‐size Lévy flight search mechanism was switched adaptively to complete the final inversion. The correctness of our method was verified by the multi‐order mode dispersion curve inversion of a six‐layer high‐velocity interlayer model. Then it was further applied to the processing of real seismic datasets. The research results show that our method fully utilizes the advantages of each of the two global‐searching non‐linear algorithms after integrating IDGWO and CS, while effectively balancing the ability between global search and local exploitation, further improving the convergence speed and inversion accuracy and having good anti‐noise performance.
{"title":"Multi‐mode non‐linear inversion of Rayleigh wave dispersion curves with grey wolf optimization and cuckoo search algorithm","authors":"Han Che, Hongyan Shen, Qingchun Li, Guoxin Liu, Chenrui Yang, Yunpeng Sun, Shuai Liu","doi":"10.1002/nsg.12296","DOIUrl":"https://doi.org/10.1002/nsg.12296","url":null,"abstract":"Dispersion curve inversion is one of the core contents of Rayleigh wave data processing. However, the dispersion curve inversion has the characteristics of multi‐parameter, multi‐extremum as well as nonlinearity. In the face of Rayleigh wave data processing under complex seismic‐geological conditions, it is difficult to reconstruct an underground structure quickly and accurately apply a single global‐searching non‐linear inversion algorithm. For this reason, we proposed a strategy to invert multi‐order mode Rayleigh wave dispersion curves by combining with grey wolf optimization (GWO) and cuckoo search (CS) algorithms. On the basis of introducing the mechanism of iterative chaotic map with infinite collapses (ICMIC) and the strategy of dimension learning–based hunting (DLH), an improved GWO was developed that was called IDGWO (ICMIC and DLH GWO). After searching the near‐optimal region through IDGWO, the CS with a variable step‐size Lévy flight search mechanism was switched adaptively to complete the final inversion. The correctness of our method was verified by the multi‐order mode dispersion curve inversion of a six‐layer high‐velocity interlayer model. Then it was further applied to the processing of real seismic datasets. The research results show that our method fully utilizes the advantages of each of the two global‐searching non‐linear algorithms after integrating IDGWO and CS, while effectively balancing the ability between global search and local exploitation, further improving the convergence speed and inversion accuracy and having good anti‐noise performance.","PeriodicalId":49771,"journal":{"name":"Near Surface Geophysics","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140563446","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}
Nikolaus Rein, Marius P. Isken, Dorina Domigall, Matthias Ohrnberger, Katrin Hannemann, Frank Krüger, Michael Korn, Torsten Dahm
Within the framework of the Intercontinental Scientific Drilling Programme (ICDP) ‘Drilling the Eger Rift’ project, five boreholes were drilled in the Vogtland (Germany) and West Bohemia (Czech Republic) regions. Three of them will be used to install high‐frequency three‐dimensional (3D) seismic arrays. The pilot 3D array is located 1.5 km south of Landwüst (Vogtland). The borehole, with a depth of 402 m, was equipped with eight geophones and a fibre optic cable behind the casing used for distributed acoustic sensing (DAS) measurements. The borehole is surrounded by a surface array consisting of 12 seismic stations with an aperture of 400 m. During drilling, a highly fractured zone was detected between 90 m and 165 m depth and interpreted as a possible fault zone. To characterize the fault zone, two vertical seismic profiling (VSP) experiments with drop weight sources at the surface were conducted. The aim of the VSP experiments was to estimate a local 3D seismic velocity tomography including the imaging of the steep fault zone. Our 3D tomography indicates P‐wave velocities between 1500 m/s and 3000 m/s at shallow depths (0–20 m) and higher P‐wave velocities of up to 5000 m/s at greater depths. In addition, the results suggest a NW–SE striking low‐velocity zone (LVZ; characterized by = 1500–3000 m/s), which crosses the borehole at a depth of about 90–165 m. This LVZ is inferred to be a shallow non‐tectonic, steep fault zone with a dip angle of about . The depth and width of the fault zone are supported by logging data as electrical conductivity, core recovery and changes in lithology. In this study, we present an example to test and verify 3D tomography and imaging approaches of shallow non‐tectonic fault zones based on active seismic experiments using simple surface drop weights as sources and borehole chains as well as borehole DAS behind casing as sensors, complemented by seismic stand‐alone surface arrays.
{"title":"Characterizing shallow fault zones by integrating profile, borehole and array measurements of seismic data and distributed acoustic sensing","authors":"Nikolaus Rein, Marius P. Isken, Dorina Domigall, Matthias Ohrnberger, Katrin Hannemann, Frank Krüger, Michael Korn, Torsten Dahm","doi":"10.1002/nsg.12293","DOIUrl":"https://doi.org/10.1002/nsg.12293","url":null,"abstract":"Within the framework of the Intercontinental Scientific Drilling Programme (ICDP) ‘Drilling the Eger Rift’ project, five boreholes were drilled in the Vogtland (Germany) and West Bohemia (Czech Republic) regions. Three of them will be used to install high‐frequency three‐dimensional (3D) seismic arrays. The pilot 3D array is located 1.5 km south of Landwüst (Vogtland). The borehole, with a depth of 402 m, was equipped with eight geophones and a fibre optic cable behind the casing used for distributed acoustic sensing (DAS) measurements. The borehole is surrounded by a surface array consisting of 12 seismic stations with an aperture of 400 m. During drilling, a highly fractured zone was detected between 90 m and 165 m depth and interpreted as a possible fault zone. To characterize the fault zone, two vertical seismic profiling (VSP) experiments with drop weight sources at the surface were conducted. The aim of the VSP experiments was to estimate a local 3D seismic velocity tomography including the imaging of the steep fault zone. Our 3D tomography indicates P‐wave velocities between 1500 m/s and 3000 m/s at shallow depths (0–20 m) and higher P‐wave velocities of up to 5000 m/s at greater depths. In addition, the results suggest a NW–SE striking low‐velocity zone (LVZ; characterized by = 1500–3000 m/s), which crosses the borehole at a depth of about 90–165 m. This LVZ is inferred to be a shallow non‐tectonic, steep fault zone with a dip angle of about . The depth and width of the fault zone are supported by logging data as electrical conductivity, core recovery and changes in lithology. In this study, we present an example to test and verify 3D tomography and imaging approaches of shallow non‐tectonic fault zones based on active seismic experiments using simple surface drop weights as sources and borehole chains as well as borehole DAS behind casing as sensors, complemented by seismic stand‐alone surface arrays.","PeriodicalId":49771,"journal":{"name":"Near Surface Geophysics","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140563435","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}
There are various methods suggested for modelling the geometry of sedimentary basins by using gravity anomalies in the literature. When dealing with datasets that are non-uniformly distributed across a study area, the choice of modelling method can significantly impact data reliability and computational resource usage. In this study, we present a gravity modelling approach utilizing prismatic vertical polyhedra. First, we summarize the requirement of such a method by highlighting limitations associated with a commonly employed modelling method that uses rectangular grid-following vertical prisms for modelling. By contrast, we propose a method that adapts a polygonal mesh to the distribution of input gravity data points, each polygonal mesh cell containing one data point and using polygonal grid-following vertical prisms for gravity modelling. To validate our method, we conduct tests using two synthetically constructed subsurface models – one featuring a normal fault and the other a deep basin. These are used to generate synthetic gravity observation data at irregularly spaced points that broadly follow the geology. The data are then inverted for obtaining subsurface structures by modelling with (a) rectangular prisms on a regular grid and (b) with our polygonal prisms on the tessellated grid. The inversion process involves calculating the heights of the prisms in both approaches, assuming a constant density contrast. The comparative analysis demonstrates the superior effectiveness of our approach (b). Finally, we apply our newly developed method to real gravity data recently collected from Gezin province, situated in the north-eastern region of the Lake Hazar pull-apart basin in Eastern Turkey. Our modelling results reveal previously underestimated basin geometry, suggesting the presence of an additional, previously unidentified fault to the east of Gezin, which forms the southern boundary of the basin.
{"title":"Gravity modelling by using vertical prismatic polyhedra and application to a sedimentary basin in Eastern Anatolia","authors":"Nedim Gökhan Aydın, Turgay İşseven","doi":"10.1002/nsg.12297","DOIUrl":"https://doi.org/10.1002/nsg.12297","url":null,"abstract":"There are various methods suggested for modelling the geometry of sedimentary basins by using gravity anomalies in the literature. When dealing with datasets that are non-uniformly distributed across a study area, the choice of modelling method can significantly impact data reliability and computational resource usage. In this study, we present a gravity modelling approach utilizing prismatic vertical polyhedra. First, we summarize the requirement of such a method by highlighting limitations associated with a commonly employed modelling method that uses rectangular grid-following vertical prisms for modelling. By contrast, we propose a method that adapts a polygonal mesh to the distribution of input gravity data points, each polygonal mesh cell containing one data point and using polygonal grid-following vertical prisms for gravity modelling. To validate our method, we conduct tests using two synthetically constructed subsurface models – one featuring a normal fault and the other a deep basin. These are used to generate synthetic gravity observation data at irregularly spaced points that broadly follow the geology. The data are then inverted for obtaining subsurface structures by modelling with (a) rectangular prisms on a regular grid and (b) with our polygonal prisms on the tessellated grid. The inversion process involves calculating the heights of the prisms in both approaches, assuming a constant density contrast. The comparative analysis demonstrates the superior effectiveness of our approach (b). Finally, we apply our newly developed method to real gravity data recently collected from Gezin province, situated in the north-eastern region of the Lake Hazar pull-apart basin in Eastern Turkey. Our modelling results reveal previously underestimated basin geometry, suggesting the presence of an additional, previously unidentified fault to the east of Gezin, which forms the southern boundary of the basin.","PeriodicalId":49771,"journal":{"name":"Near Surface Geophysics","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140324689","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}
Ali Gebril, Mohamed A. Khalil, R. M. Joeckel, James Rose
Shallow, dominantly silt‐ and clay‐filled erosional troughs in Quaternary sediments under the Flathead Valley (northwestern Montana, USA) are very likely to be hydraulic barriers limiting the horizontal flow of groundwater. Accurately mapping them is important because of increasing demand for groundwater. We used a legacy Bouguer gravity map measured in 1968. The directional derivatives of the map are computed, and the map was enhanced by implementing edge detection tools. We produced generalized derivative, maximum horizontal gradient, total gradient and tilt gradient maps through two‐dimensional Fourier transform analysis. These maps were remarkably successful in locating buried troughs in the northern and northwestern parts of the study area, closely matching locations determined previously from compiled borehole data. Our results also identify hitherto unknown extensions of troughs and indicate that some of the buried troughs may be connected.
{"title":"Analysis of legacy gravity data reveals sediment‐filled troughs buried under Flathead Valley, Montana, USA","authors":"Ali Gebril, Mohamed A. Khalil, R. M. Joeckel, James Rose","doi":"10.1002/nsg.12295","DOIUrl":"https://doi.org/10.1002/nsg.12295","url":null,"abstract":"Shallow, dominantly silt‐ and clay‐filled erosional troughs in Quaternary sediments under the Flathead Valley (northwestern Montana, USA) are very likely to be hydraulic barriers limiting the horizontal flow of groundwater. Accurately mapping them is important because of increasing demand for groundwater. We used a legacy Bouguer gravity map measured in 1968. The directional derivatives of the map are computed, and the map was enhanced by implementing edge detection tools. We produced generalized derivative, maximum horizontal gradient, total gradient and tilt gradient maps through two‐dimensional Fourier transform analysis. These maps were remarkably successful in locating buried troughs in the northern and northwestern parts of the study area, closely matching locations determined previously from compiled borehole data. Our results also identify hitherto unknown extensions of troughs and indicate that some of the buried troughs may be connected.","PeriodicalId":49771,"journal":{"name":"Near Surface Geophysics","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140203533","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}
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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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}
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