Just-Accepted Journal Articles

Just-Accepted Articles are peer-reviewed, accepted manuscripts that have been assigned Digital Object Identifiers (DOIs) and undergo the normal publication process (copy editing, page composition, proofing by author, and finalization). A Just-Accepted article listing includes an abstract and the DOI (scroll).  The article is removed when the final version of the manuscript is ready and assigned to a Journal of Environmental & Engineering Geophysics (JEEG) issue, becoming the official version of the article. The Just Accepted article has the same DOI that appears on the official version of the article; therefore, citations made to an article during the Just-Accepted stage will continue to link to the article's official version.  EEGS Members can access the full, preliminary article via this "member-only" link:  Just-Accepted Articles with full article PDF


Noise Reduction of Aeromagnetic Data Using Artificial Neural Network
Authors: Osama Elghrabawy

DOI: 10.32389/JEEG22-013

ABSTRACT: The high frequency content of high-resolution aeromagnetic data is of particular interest to geophysicists to identify mineral deposits, shallow faults, and dikes. However high resolution aeromagnetic data contaminated by cultural noise generated from aircraft and man-made features. The culture noise must be removed before starting the interpretation process. Manual techniques are more selective of the noise, however slower and more expensive because they require considerable hands-on interaction. The present study develops a novel method for detecting and removing the culture noise from aeromagnetic data based on an artificial neural network (ANN) in automatic way, and comparing the results with conventional algorithm using the non-linear filter. The proposed method is tested using a theoretical example that combine a magnetic anomaly due to a dyke with three sources of cultural noise, besides using a practical example to increase the number of a training pattern. The network is trained based on the backpropagation training function, where the algorithm updates the weight and bias states as per the Levenberg–Marquardt optimization. The optimization is reached during the training and validation process after 3,000 iterations. The correlation coefficient () is utilized along with the mean squared error (MSE) as performance indices of the ANN. The ANN demonstrates the capability to detect the spiky data based on the optimal weights, thus allowing for removing and replacing them with clean data using the piecewise cubic Hermite interpolating polynomial (PCHIP) function. The practical utility of the two-method is discussed using high-resolution aeromagnetic data from the Tushka area located in the southwestern desert of Egypt. Comparing the denoising results using the two methods shows that the current approach is more effective in processing and more closely recovering the original magnetic data.

Keywords: aeromagnetic data processing; noise reduction; artificial neural network


Continuous automatic estimation of volumetric water content profile during infiltration using sparse multi-offset GPR data
Authors:  Koki Oikawa; Hirotaka Saito; Seiichiro Kuroda; Kazunori Takahashi

DOI: 10.32389/JEEG22-016

ABSTRACT: Ground-penetrating radar (GPR) is a non-destructive and non-invasive geophysical survey method, which 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 a limited number of traces. Because the data volume was significant, an approach to automatically determine electromagnetic wave velocities from sparse common midpoint (CMP) data using standard velocity analysis, such as semblance analysis, was utilized. 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. In the proposed method, 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 were determined using cross-validation. 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 content data based on Topp’s equation, which relates the soil VWC to the dielectric constant. The proposed method was tested using CMP data obtained via numerical simulation and experiments. The VWC profile resulting from the proposed approach matched well with the independently observed VWC profiles obtained from an invasive probe-type soil moisture sensor.

Keywords: Sparse CMP; Semblance; F-K zone pass filter, NMO correction, cross validation


A case study of completely buried wind-power cable detection using 3D acoustic imaging
Authors:  Jiho Ha; Jungkyun Shin

DOI: 10.32389/JEEG22-003

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.

Keywords: wind-power cable; Engineering Ocean Seismic 3D; 3D acoustic imaging; burial assessment survey


Numerical study on urban infrastructure diagnosis in laterally heterogenous soils using resistivity and ground penetrating radar techniques

Authors:  Ravin N Deo; Singh Nikhil; Kishore Kaushal; Jayantha Kodikara

DOI: 10.32389/JEEG22-022

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.

Keywords: electrical resistivity profiling; Ground Penetrating Radar; pipeline, condition assessment; urban environment


Applications and Analytical Methods of Ground Penetrating Radar for Soil Characterization in a Silvopastoral System
Authors:  Harrison Wakefield Smith; Phillip Owens; Amanda Ashworth

DOI: 10.32389/JEEG22-001

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 (r_s=0.61,p<0.01), and estimates of depth to clay layers were marginally correlated with observed values (r_s=047,p=0.09). 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.

Keywords: ground penetrating radar; soil characterization; agriculture; random forest; modeling


Integrated Agrogeophysical Approach for Investigating Soil Pipes in Agricultural Fields
Authors:  Md Abdus Samad; Leti T. Wodajo; Parsa Bakhtiari Rad; Md Lal Mamud; Craig J. Hickey

DOI: 10.32389/JEEG22-007

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.

Keywords: Soil erosion Soil pipes Seismic refraction tomography Electrical resistivity tomography GPR