Xueyu Zhao, Jie Wang, Dongxue Zhao, Michael Sefton, J. Triantafilis
The sugarcane growing soil in far-north Queensland is sandy, and infertile. To ensure productivity, nutrient guidelines recommend lime application rates based on soil cation exchange capacity (CEC). However, laboratory determination of CEC is expensive. Because CEC is often correlated with soil apparent electrical conductivity (ECa, mS/m) measured from electromagnetic induction (EM) instruments, ECa can be used to predict CEC. Using ECa may lead to uncertainty in prediction, but estimates of true electrical conductivity (σ, mS/m) generated from inversion of ECa, can be correlated with depth-specific CEC. In this study, we compared linear regression (LR) between ECa from a DUALEM-421S and CEC at specific depths ( i.e., topsoil [0–0.3 m], subsurface [0.3–0.6 m], subsoil [0.6–0.9 m] and deep subsoil [0.9–1.2 m]), with a LR of σ using a quasi-2d (q-2d) or quasi-3d (q-3d) inversion of DUALEM-421S ECa and CEC at all depths. The use of a multiple linear regression (MLR) to predict CEC, using σ with depth and location ( i.e., Easting and Northing) is also explored along with σ from the other EM products ( i.e., DUALEM-1S and DUALEM-21S). The minimum number of calibration sample locations ( i.e., n = 165, 150,…, 15) is also investigated. The LR between ECa ( e.g., 1mPcon) and CEC were very weak (R2 = 0.27) and weak (0.36) in the topsoil and subsurface, respectively, but moderate in the subsoil (0.57) and deep subsoil (0.67). The LR between σ, estimated from q-2d (R2 = 0.66) and q-3d (0.64) inversion of DUALEM-421S ECa, and CEC at all depths was moderate. In terms of prediction agreement, the Lin's concordance correlation coefficient (LCCC) was moderate for q-2d (0.79) and q-3d (0.75). Using a MLR with depth, coordinates and σ, led to an improvement in calibration using q-2d (R2 = 0.71) or q-3d (0.67), with prediction agreement substantial for q-2d (LCCC = 0.83) and moderate for q-3d (0.78), with comparable agreement from DUALEM-1S and DUALEM-2S (0.77) estimates of σ. The minimum number of calibration samples for a strong MLR R2 (>0.7) and substantial and good agreement was 15 for q-2d and 30 for q-3d, respectively. The final digital soil mapping of topsoil CEC developed using MLR and σ estimated from q-3d of DUALEM-421S ECa could be used to apply the Australian sugarcane industry lime application guidelines with areas with intermediate (3–6 cmol[+]/kg) and small (<3 cmol[+]/kg) topsoil CEC requiring 4 and 2.25 t/ha, respectively.
{"title":"Mapping Cation Exchange Capacity (CEC) Across Sugarcane Fields with Different Comparisons by Using DUALEM Data","authors":"Xueyu Zhao, Jie Wang, Dongxue Zhao, Michael Sefton, J. Triantafilis","doi":"10.32389/jeeg22-002","DOIUrl":"https://doi.org/10.32389/jeeg22-002","url":null,"abstract":"The sugarcane growing soil in far-north Queensland is sandy, and infertile. To ensure productivity, nutrient guidelines recommend lime application rates based on soil cation exchange capacity (CEC). However, laboratory determination of CEC is expensive. Because CEC is often correlated with soil apparent electrical conductivity (ECa, mS/m) measured from electromagnetic induction (EM) instruments, ECa can be used to predict CEC. Using ECa may lead to uncertainty in prediction, but estimates of true electrical conductivity (σ, mS/m) generated from inversion of ECa, can be correlated with depth-specific CEC. In this study, we compared linear regression (LR) between ECa from a DUALEM-421S and CEC at specific depths ( i.e., topsoil [0–0.3 m], subsurface [0.3–0.6 m], subsoil [0.6–0.9 m] and deep subsoil [0.9–1.2 m]), with a LR of σ using a quasi-2d (q-2d) or quasi-3d (q-3d) inversion of DUALEM-421S ECa and CEC at all depths. The use of a multiple linear regression (MLR) to predict CEC, using σ with depth and location ( i.e., Easting and Northing) is also explored along with σ from the other EM products ( i.e., DUALEM-1S and DUALEM-21S). The minimum number of calibration sample locations ( i.e., n = 165, 150,…, 15) is also investigated. The LR between ECa ( e.g., 1mPcon) and CEC were very weak (R2 = 0.27) and weak (0.36) in the topsoil and subsurface, respectively, but moderate in the subsoil (0.57) and deep subsoil (0.67). The LR between σ, estimated from q-2d (R2 = 0.66) and q-3d (0.64) inversion of DUALEM-421S ECa, and CEC at all depths was moderate. In terms of prediction agreement, the Lin's concordance correlation coefficient (LCCC) was moderate for q-2d (0.79) and q-3d (0.75). Using a MLR with depth, coordinates and σ, led to an improvement in calibration using q-2d (R2 = 0.71) or q-3d (0.67), with prediction agreement substantial for q-2d (LCCC = 0.83) and moderate for q-3d (0.78), with comparable agreement from DUALEM-1S and DUALEM-2S (0.77) estimates of σ. The minimum number of calibration samples for a strong MLR R2 (>0.7) and substantial and good agreement was 15 for q-2d and 30 for q-3d, respectively. The final digital soil mapping of topsoil CEC developed using MLR and σ estimated from q-3d of DUALEM-421S ECa could be used to apply the Australian sugarcane industry lime application guidelines with areas with intermediate (3–6 cmol[+]/kg) and small (<3 cmol[+]/kg) topsoil CEC requiring 4 and 2.25 t/ha, respectively.","PeriodicalId":15748,"journal":{"name":"Journal of Environmental and Engineering Geophysics","volume":"48 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90716827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Urban environment can be considered a complex system consisting of the engineered pavement physical structure over the buried utilities (water, gas, sewer) network embedded in the background soil environment. Assessment of buried pipeline civil infrastructures using proximal geophysical methods in such instances has to consider possible interferences, difficulties, and incorrect inferences. In this study, we have conducted a numerical modelling investigation to understand and evaluate how electrical resistivity profiling (ERP) and ground penetrating radar (GPR) can be utilised to provide subsurface information that otherwise may not be possible if either one of the techniques is used. A model geometry consisting of a typical pavement structure (asphalt, base/subbase, and background soil) with a single 2 m pipe buried at a depth of 1 m was used. Strong lateral variations in soil type were incorporated over the short pipe section in order to understand the complexities that can arise, especially with ERP measurements. The 3D electrical resistivity measurements were simulated in Comsol using the 4-probe method, while the 2D GPR measurements were simulated in gprMax to obtain the subsurface information. The results from both ERP and GPR were used to develop a practical framework that can be utilised by relevant authorities for proximal condition assessment of their buried assets. It was suggested that ERP can be used as a first level screening tool over the whole pipeline length, followed by discretely selected GPR scans in order to further gain information on the pipe health. This is attractive practically since, following delineations of a large pipe section into shorter subsections, advanced condition assessment approaches that are generally intrusive in nature can then be economically deployed within the subsections suspected of experiencing significant corrosion damage.
{"title":"Numerical Study on Urban Infrastructure Diagnosis in Laterally Heterogenous Soils Using Resistivity and Ground Penetrating Radar Techniques","authors":"R. Deo, Nikhil A Singh, K. Kishore, J. Kodikara","doi":"10.32389/jeeg22-022","DOIUrl":"https://doi.org/10.32389/jeeg22-022","url":null,"abstract":"Urban environment can be considered a complex system consisting of the engineered pavement physical structure over the buried utilities (water, gas, sewer) network embedded in the background soil environment. Assessment of buried pipeline civil infrastructures using proximal geophysical methods in such instances has to consider possible interferences, difficulties, and incorrect inferences. In this study, we have conducted a numerical modelling investigation to understand and evaluate how electrical resistivity profiling (ERP) and ground penetrating radar (GPR) can be utilised to provide subsurface information that otherwise may not be possible if either one of the techniques is used. A model geometry consisting of a typical pavement structure (asphalt, base/subbase, and background soil) with a single 2 m pipe buried at a depth of 1 m was used. Strong lateral variations in soil type were incorporated over the short pipe section in order to understand the complexities that can arise, especially with ERP measurements. The 3D electrical resistivity measurements were simulated in Comsol using the 4-probe method, while the 2D GPR measurements were simulated in gprMax to obtain the subsurface information. The results from both ERP and GPR were used to develop a practical framework that can be utilised by relevant authorities for proximal condition assessment of their buried assets. It was suggested that ERP can be used as a first level screening tool over the whole pipeline length, followed by discretely selected GPR scans in order to further gain information on the pipe health. This is attractive practically since, following delineations of a large pipe section into shorter subsections, advanced condition assessment approaches that are generally intrusive in nature can then be economically deployed within the subsections suspected of experiencing significant corrosion damage.","PeriodicalId":15748,"journal":{"name":"Journal of Environmental and Engineering Geophysics","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89702252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The use of ground penetrating radar (GPR) for soil characterization has grown rapidly in recent years due to substantial increases in computer processing power and advances in GPR methodologies. However, few studies have focused on applied GPR analysis for soil characterization and decision making in agricultural systems. In this study, we explored applications of some common qualitative and quantitative methods for GPR analysis and characterization of subsurface conditions in a silvopasture system. We analyzed GPR results using traditional visual interpretation methods to delineate depth to bedrock, clay layers, and other important soil features. Estimates of depth to bedrock correlated well with values measured in the field ([Formula: see text]), and estimates of depth to clay layers were marginally correlated with observed values ([Formula: see text]). We also extracted attributes from GPR images to train a random forest regression model to predict coarse fragment percentage and percent clay content. GPR attributes were found to be good predictors of soil coarse fragments, with an R2 value of 0.81 and root mean square error (RMSE) of 18.82 for test data. Our results demonstrate GPR can provide valuable information on subsurface features in silvopastoral systems. These results also suggest a strong potential for machine learning algorithms in GPR data analytics. Data generated using these methods could be integrated with or used to validate existing digital soil mapping methods and contribute to better understanding of subsurface characteristics for optimized soil management in silvopastoral systems.
{"title":"Applications and Analytical Methods of Ground Penetrating Radar for Soil Characterization in a Silvopastoral System","authors":"Harrison W. Smith, P. Owens, A. Ashworth","doi":"10.32389/jeeg22-001","DOIUrl":"https://doi.org/10.32389/jeeg22-001","url":null,"abstract":"The use of ground penetrating radar (GPR) for soil characterization has grown rapidly in recent years due to substantial increases in computer processing power and advances in GPR methodologies. However, few studies have focused on applied GPR analysis for soil characterization and decision making in agricultural systems. In this study, we explored applications of some common qualitative and quantitative methods for GPR analysis and characterization of subsurface conditions in a silvopasture system. We analyzed GPR results using traditional visual interpretation methods to delineate depth to bedrock, clay layers, and other important soil features. Estimates of depth to bedrock correlated well with values measured in the field ([Formula: see text]), and estimates of depth to clay layers were marginally correlated with observed values ([Formula: see text]). We also extracted attributes from GPR images to train a random forest regression model to predict coarse fragment percentage and percent clay content. GPR attributes were found to be good predictors of soil coarse fragments, with an R2 value of 0.81 and root mean square error (RMSE) of 18.82 for test data. Our results demonstrate GPR can provide valuable information on subsurface features in silvopastoral systems. These results also suggest a strong potential for machine learning algorithms in GPR data analytics. Data generated using these methods could be integrated with or used to validate existing digital soil mapping methods and contribute to better understanding of subsurface characteristics for optimized soil management in silvopastoral systems.","PeriodicalId":15748,"journal":{"name":"Journal of Environmental and Engineering Geophysics","volume":"386 ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72441606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
B. Allred, Asim Biswas, C. Lobsey, Laurie A. Whitesell
{"title":"Introduction to the Journal of Environmental and Engineering Geophysics Special Issue on the Application of Proximal and Remote Sensing Technologies to Soil Investigations","authors":"B. Allred, Asim Biswas, C. Lobsey, Laurie A. Whitesell","doi":"10.32389/jeeg22-080","DOIUrl":"https://doi.org/10.32389/jeeg22-080","url":null,"abstract":"","PeriodicalId":15748,"journal":{"name":"Journal of Environmental and Engineering Geophysics","volume":"18 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76696776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nikhil A Singh, K. Kishore, R. Deo, Ye Lu, J. Kodikara
Timely monitoring of pavement sub-surface layer thickness and condition evaluation is essential to ensure stable pavement performance and safety under heavy traffic loading. In addition, accurate estimation of pavement layer thicknesses is required for condition evaluation, overlay design/ quality control assurance, and structural capacity evaluation of existing pavements to predict its remaining service life. Traditionally this vital information is ascertained through coring/drilling and visual inspections. In contrast to these current techniques, ground-penetrating radar (GPR) is a non-destructive technique gaining popularity in pavement asphalt layer thickness estimation and structural condition monitoring. Its high-quality data contains vital pavement condition information, and survey costs are reasonably economic. In this work, GPR data were acquired along a toll road in Queensland, Australia, using the GSSI 4-channel SIR30 GPR unit. Asphalt layer thickness information is considered an important input parameter for condition assessment, pavement performance, and lifetime modelling. This work presents an automated segmentation framework to evaluate pavement conditions for a large pavement network. The developed algorithm uses GPR asphalt thickness data as input and generates segments with decision boundaries utilising a cascaded k-means and DBSCAN approach that works in two steps: 1) centroid initialisation using k-means algorithm, 2) clustering using DBSCAN algorithm. Presented in this paper is the workflow of the cascaded method that is applicable to automated analysis of GPR asphalt thickness data. The performance of the cascaded k-means and DBSCAN algorithm was evaluated in terms of entropy compared with traditional k-means and traditional DBSCAN algorithms. The results show that the proposed method outperforms its constituents. Based on the results of this study, the method presented in this paper is cost-effective, economical and robust for segmenting large pavement network with GPR data.
{"title":"Automated Segmentation Framework for Asphalt Layer Thickness from GPR Data Using a Cascaded k-Means - DBSCAN Algorithm","authors":"Nikhil A Singh, K. Kishore, R. Deo, Ye Lu, J. Kodikara","doi":"10.32389/jeeg22-019","DOIUrl":"https://doi.org/10.32389/jeeg22-019","url":null,"abstract":"Timely monitoring of pavement sub-surface layer thickness and condition evaluation is essential to ensure stable pavement performance and safety under heavy traffic loading. In addition, accurate estimation of pavement layer thicknesses is required for condition evaluation, overlay design/ quality control assurance, and structural capacity evaluation of existing pavements to predict its remaining service life. Traditionally this vital information is ascertained through coring/drilling and visual inspections. In contrast to these current techniques, ground-penetrating radar (GPR) is a non-destructive technique gaining popularity in pavement asphalt layer thickness estimation and structural condition monitoring. Its high-quality data contains vital pavement condition information, and survey costs are reasonably economic. In this work, GPR data were acquired along a toll road in Queensland, Australia, using the GSSI 4-channel SIR30 GPR unit. Asphalt layer thickness information is considered an important input parameter for condition assessment, pavement performance, and lifetime modelling. This work presents an automated segmentation framework to evaluate pavement conditions for a large pavement network. The developed algorithm uses GPR asphalt thickness data as input and generates segments with decision boundaries utilising a cascaded k-means and DBSCAN approach that works in two steps: 1) centroid initialisation using k-means algorithm, 2) clustering using DBSCAN algorithm. Presented in this paper is the workflow of the cascaded method that is applicable to automated analysis of GPR asphalt thickness data. The performance of the cascaded k-means and DBSCAN algorithm was evaluated in terms of entropy compared with traditional k-means and traditional DBSCAN algorithms. The results show that the proposed method outperforms its constituents. Based on the results of this study, the method presented in this paper is cost-effective, economical and robust for segmenting large pavement network with GPR data.","PeriodicalId":15748,"journal":{"name":"Journal of Environmental and Engineering Geophysics","volume":"39 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81684307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Md Abdus Samad, L. Wodajo, P. B. Rad, Md Lal Mamud, C. Hickey
Soil erosion is one of the most significant challenges for soil management and agri-food production threatening human habitat and livelihood. Although soil erosion due to surficial processes is well-studied, erosion due to subsurface processes such as internal soil pipes has often been overlooked. Internal soil pipes directly contribute to the total soil loss in agricultural fields and impede agricultural sustainability. Locating, measuring, and mapping internal soil pipes and their networks are vital to assessing the total soil loss in agricultural fields. Their hidden and uncorrelated nature of subsurface occurrences constricts the applicability of manual and remote sensing-based detection techniques. Non-invasive agrogeophysical methods can overcome these limitations with detailed subsurface pictures and high spatial resolution. In this study, the applicability of three agrogeophysical methods including seismic refraction tomography (SRT), electrical resistivity tomography (ERT), and ground-penetrating radar (GPR) was tested at Goodwin Creek, an experimental field site with established internal soil pipes. SRT showed low P and S wave velocities anomalies in soil pipe-affected zones. ERT results indicated the location of soil pipes with high resistivity anomalies. However, both SRT and ERT lack resolution to identify individual soil pipes. GPR diffraction hyperbolas and their apexes however effectively identified individual soil pipes. The agrogeophysical anomalies for soil pipes were compared with the low penetration resistance of the cone penetrologger (CPL) results. Correspondence between low PR in CPL and agrogeophysical anomalies verify the locations of internal soil pipe-affected zones. Moreover, the fragipan layer is identified below the soil pipe-affected zone by all three methods.
{"title":"Integrated Agrogeophysical Approach for Investigating Soil Pipes in Agricultural Fields","authors":"Md Abdus Samad, L. Wodajo, P. B. Rad, Md Lal Mamud, C. Hickey","doi":"10.32389/jeeg22-007","DOIUrl":"https://doi.org/10.32389/jeeg22-007","url":null,"abstract":"Soil erosion is one of the most significant challenges for soil management and agri-food production threatening human habitat and livelihood. Although soil erosion due to surficial processes is well-studied, erosion due to subsurface processes such as internal soil pipes has often been overlooked. Internal soil pipes directly contribute to the total soil loss in agricultural fields and impede agricultural sustainability. Locating, measuring, and mapping internal soil pipes and their networks are vital to assessing the total soil loss in agricultural fields. Their hidden and uncorrelated nature of subsurface occurrences constricts the applicability of manual and remote sensing-based detection techniques. Non-invasive agrogeophysical methods can overcome these limitations with detailed subsurface pictures and high spatial resolution. In this study, the applicability of three agrogeophysical methods including seismic refraction tomography (SRT), electrical resistivity tomography (ERT), and ground-penetrating radar (GPR) was tested at Goodwin Creek, an experimental field site with established internal soil pipes. SRT showed low P and S wave velocities anomalies in soil pipe-affected zones. ERT results indicated the location of soil pipes with high resistivity anomalies. However, both SRT and ERT lack resolution to identify individual soil pipes. GPR diffraction hyperbolas and their apexes however effectively identified individual soil pipes. The agrogeophysical anomalies for soil pipes were compared with the low penetration resistance of the cone penetrologger (CPL) results. Correspondence between low PR in CPL and agrogeophysical anomalies verify the locations of internal soil pipe-affected zones. Moreover, the fragipan layer is identified below the soil pipe-affected zone by all three methods.","PeriodicalId":15748,"journal":{"name":"Journal of Environmental and Engineering Geophysics","volume":"7 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77741371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Koki Oikawa, H. Saito, S. Kuroda, Kazunori Takahashi
Ground-penetrating radar (GPR) is a non-destructive and non-invasive geophysical survey method that has been used to characterize soil volumetric water content (VWC) dynamics. An array antenna GPR system was used to collect nearly seamless, time-lapse multi-offset GPR data during an in-situ infiltration test on sand dunes with limited traces. Because the data volume was significant, an approach was utilized to automatically determine electromagnetic wave velocities from sparse common midpoint (CMP) data using standard velocity analysis, such as semblance analysis. The objective of this study was to develop a methodology that allows one to automatically perform velocity analysis by interpolating sparse CMP data obtained with the array GPR system. The proposed method determined the optimal normal moveout velocity values and the removal range of the F-K zone pass filter that minimized errors between the original and interpolated CMP data using leave-one-out cross-validation (LOOCV). After interpolating the sparse CMP data with the F-K zone pass filter, semblance analysis was used to determine the time-lapse velocity structure of the soil profile during water infiltration. The velocity data were converted to VWC data based on the Topp equation, which relates the soil VWC to the soil dielectric constant. The proposed method was tested using CMP data obtained via numerical simulation and experiments. The VWC profile from the proposed approach matched well with the independently observed VWC profiles obtained from an invasive probe-type soil moisture sensor.
{"title":"Continuous Automatic Estimation of Volumetric Water Content Profile During Infiltration Using Sparse Multi-Offset GPR Data","authors":"Koki Oikawa, H. Saito, S. Kuroda, Kazunori Takahashi","doi":"10.32389/jeeg22-016","DOIUrl":"https://doi.org/10.32389/jeeg22-016","url":null,"abstract":"Ground-penetrating radar (GPR) is a non-destructive and non-invasive geophysical survey method that has been used to characterize soil volumetric water content (VWC) dynamics. An array antenna GPR system was used to collect nearly seamless, time-lapse multi-offset GPR data during an in-situ infiltration test on sand dunes with limited traces. Because the data volume was significant, an approach was utilized to automatically determine electromagnetic wave velocities from sparse common midpoint (CMP) data using standard velocity analysis, such as semblance analysis. The objective of this study was to develop a methodology that allows one to automatically perform velocity analysis by interpolating sparse CMP data obtained with the array GPR system. The proposed method determined the optimal normal moveout velocity values and the removal range of the F-K zone pass filter that minimized errors between the original and interpolated CMP data using leave-one-out cross-validation (LOOCV). After interpolating the sparse CMP data with the F-K zone pass filter, semblance analysis was used to determine the time-lapse velocity structure of the soil profile during water infiltration. The velocity data were converted to VWC data based on the Topp equation, which relates the soil VWC to the soil dielectric constant. The proposed method was tested using CMP data obtained via numerical simulation and experiments. The VWC profile from the proposed approach matched well with the independently observed VWC profiles obtained from an invasive probe-type soil moisture sensor.","PeriodicalId":15748,"journal":{"name":"Journal of Environmental and Engineering Geophysics","volume":"37 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82191357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As offshore wind power is renewable energy produced through the installation and operation of large-scale offshore infrastructure, risk management is crucial in power platforms. Safety accidents caused by external factors during the operation of submarine power cables can lead to enormous costs, thus necessitating the monitoring of burial depth and route information of cables. In this study, we developed a 3D acoustic imaging method that obtains information on the route and depth of completely buried power cables. An acoustic source-based engineering ocean seismic 3D (EOS3D) system has been used to detect buried objects in the subsurface because conventional sonars, such as multi-beam echo sounder (MBES) and side-scan sonar (SSS), which are used to analyze seafloor characteristics, have limitations in detecting completely buried cables in the subsurface. Field data were obtained as 8-channel data using a chirp source (2–8 kHz) designed to obtain a 25 × 25 cm horizontal spatial resolution from real-time kinematic (RTK) positioning. The image stack method was proposed to effectively detect buried cables, with the vertical gradient analyzed using signals decomposed into representative bin sizes and low-mid-high-frequency components. The acoustic anomalies of buried objects, identified as export cables and protectors, were processed into images using the proposed image stack method and gradient analysis. This case study of buried wind power cables using 3D acoustic imaging could be utilized in burial assessment survey (BAS)-data acquisition, processing/analysis processes, and operation and management of buried cables.
{"title":"A Case Study of Completely Buried Wind-Power Cable Detection Using 3D Acoustic Imaging","authors":"Jiho Ha, Jungkyun Shin","doi":"10.32389/jeeg22-003","DOIUrl":"https://doi.org/10.32389/jeeg22-003","url":null,"abstract":"As offshore wind power is renewable energy produced through the installation and operation of large-scale offshore infrastructure, risk management is crucial in power platforms. Safety accidents caused by external factors during the operation of submarine power cables can lead to enormous costs, thus necessitating the monitoring of burial depth and route information of cables. In this study, we developed a 3D acoustic imaging method that obtains information on the route and depth of completely buried power cables. An acoustic source-based engineering ocean seismic 3D (EOS3D) system has been used to detect buried objects in the subsurface because conventional sonars, such as multi-beam echo sounder (MBES) and side-scan sonar (SSS), which are used to analyze seafloor characteristics, have limitations in detecting completely buried cables in the subsurface. Field data were obtained as 8-channel data using a chirp source (2–8 kHz) designed to obtain a 25 × 25 cm horizontal spatial resolution from real-time kinematic (RTK) positioning. The image stack method was proposed to effectively detect buried cables, with the vertical gradient analyzed using signals decomposed into representative bin sizes and low-mid-high-frequency components. The acoustic anomalies of buried objects, identified as export cables and protectors, were processed into images using the proposed image stack method and gradient analysis. This case study of buried wind power cables using 3D acoustic imaging could be utilized in burial assessment survey (BAS)-data acquisition, processing/analysis processes, and operation and management of buried cables.","PeriodicalId":15748,"journal":{"name":"Journal of Environmental and Engineering Geophysics","volume":"54 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83326358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Direct current resistivity prospecting is a commonly geophysical method for environmental and engineering applications. In this paper, we propose a fuzzy C-means clustering model constrained inversion algorithm for two-dimensional DC resistivity. To fit arbitrary geological structure and surface of the earth, our inversion algorithm is developed based on unstructured model mesh. To be consistent with the geological structure, the fuzzy C-means clustering model constraint is added to the inversion cost function with the minimum structure model constraint, and the Gauss-Newton optimization method is used to seek solutions of the nonlinear inverse problem. Finally, we verify the performance of our algorithm by synthetic and field data sets. The results show that the resistivity and boundary can be better restored when the correct number and value of priori cluster centers were set. By testing the field data, the inversion algorithm can obtain obvious abnormal boundaries.
{"title":"Two-dimensional Inversion of DC Resistivity Data on Unstructured Grids Using Fuzzy C-means Clustering Model Constraint","authors":"Kaidi Xu, Man Li, Zhiyong Zhang, Ke Yi, F. Zhou","doi":"10.32389/jeeg22-028","DOIUrl":"https://doi.org/10.32389/jeeg22-028","url":null,"abstract":"Direct current resistivity prospecting is a commonly geophysical method for environmental and engineering applications. In this paper, we propose a fuzzy C-means clustering model constrained inversion algorithm for two-dimensional DC resistivity. To fit arbitrary geological structure and surface of the earth, our inversion algorithm is developed based on unstructured model mesh. To be consistent with the geological structure, the fuzzy C-means clustering model constraint is added to the inversion cost function with the minimum structure model constraint, and the Gauss-Newton optimization method is used to seek solutions of the nonlinear inverse problem. Finally, we verify the performance of our algorithm by synthetic and field data sets. The results show that the resistivity and boundary can be better restored when the correct number and value of priori cluster centers were set. By testing the field data, the inversion algorithm can obtain obvious abnormal boundaries.","PeriodicalId":15748,"journal":{"name":"Journal of Environmental and Engineering Geophysics","volume":"15 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82540383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Understanding the magnetic anomaly in terms of the subsurface causative source plays a pivotal role in mineral exploration as well as in other geological applications. Automatic modeling of such isolated profile data is still rising, and fundamental improvements are needed in analyzing the association of subsurface rocks with the magnetic anomaly in terms of various independent model parameters. Here, we propose a MATLAB-based simulated annealing algorithm to simplify the interpretation process of magnetic anomalies. The performance of the adopted approach over various synthetic models of simple geometries like spheres, dikes, sills, and prisms is analyzed with and without contaminated noise. These geometries are widely used for some specific types of ore bodies such as iron, base metals, and mineralization such as skarns, massive sulfides, etc. Finally, two different real deposits of Chromite Ore and Uranium Ore are taken along with their magnetic anomalies to interpret their subsurface geometries in terms of model parameters. The estimated structures are verified to have a great affinity with the structures obtained in previously published works of literature. Furthermore, the present computational algorithm provides a user-friendly approach without any computational difficulties with minimum cost.
{"title":"Interpretation of Isolated Magnetic Profile Using Simulated Annealing Approach","authors":"Sunaina Shinu, C. P. Dubey","doi":"10.32389/jeeg22-014","DOIUrl":"https://doi.org/10.32389/jeeg22-014","url":null,"abstract":"Understanding the magnetic anomaly in terms of the subsurface causative source plays a pivotal role in mineral exploration as well as in other geological applications. Automatic modeling of such isolated profile data is still rising, and fundamental improvements are needed in analyzing the association of subsurface rocks with the magnetic anomaly in terms of various independent model parameters. Here, we propose a MATLAB-based simulated annealing algorithm to simplify the interpretation process of magnetic anomalies. The performance of the adopted approach over various synthetic models of simple geometries like spheres, dikes, sills, and prisms is analyzed with and without contaminated noise. These geometries are widely used for some specific types of ore bodies such as iron, base metals, and mineralization such as skarns, massive sulfides, etc. Finally, two different real deposits of Chromite Ore and Uranium Ore are taken along with their magnetic anomalies to interpret their subsurface geometries in terms of model parameters. The estimated structures are verified to have a great affinity with the structures obtained in previously published works of literature. Furthermore, the present computational algorithm provides a user-friendly approach without any computational difficulties with minimum cost.","PeriodicalId":15748,"journal":{"name":"Journal of Environmental and Engineering Geophysics","volume":"29 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84134522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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