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

Validation and Potential Improvement of Soil Survey Maps Using Proximal Soil Sensing
Authors:  Felippe Hoffmann Silva Karp; Viacheslav I. Adamchuk; Alex Melnitchouck; Barry Allred; Pierre Dutilleul; Luis R. Martinez

DOI: 10.32389/JEEG22-018

ABSTRACT: There is potential use of proximal soil sensors (PSS) to contribute to soil surveys and improve their results, and this study focused on the evaluation of this potential. An analysis using a high-resolution soil survey (1:5000), terrain data, and an ensemble of PSS (gamma ray emission, ground penetrating radar – GPR, apparent electrical conductivity from electromagnetic induction, and galvanic contact) was conducted. First, a geostatistical analysis was performed to characterize the spatial variability of each variable for each sensor and interpolate the data to a common support. The GPR data presented well-delineated groups of depths with similar spatial structure. These groups matched the field soil horizon depths, thus representing the potential for this sensor in soil characterization. A significant correlation was found between most of the variables from each sensor. However, no complete agreement was observed among the data from different PSS. In addition, a visual comparison of the maps showed that each PSS captured the soil spatial variability of the field and delineated regions distinctively. To validate the soil separation provided by the high-resolution soil survey and evaluate the capability of the PSS to distinguish the different soils, an analysis of variance was performed. Although none of the sensors could differentiate all the soils in the field, maps containing an overlay between sensors and soil models provided an important insight: overall, the soils were located correctly but the boundaries needed to be adjusted. Spatial clustering was used to perform a multivariate analysis of the data. A final map containing well-delimited homogenous PSS-based zones was obtained. Accordingly, it is possible to conclude that this approach and the resulting maps can be used to improve the delineation of boundaries between different soil types.

Keywords: soil types; data fusion; spatial cluster

Influence of Radiative Components and Meteorological Conditions on Simulation of Slope-Specific Heat Balance
Authors:  Mito Nishioka; Megumi Yamashita; Hirotaka Saito

DOI: 10.32389/JEEG22-011

ABSTRACT:  Fields have uneven surfaces, such as ridges, and the shortwave radiation of fields differs depending on the orientation of the slope of the unevenness, resulting in variations in the distribution of moisture on the ground surface. Therefore, it is necessary to estimate the heat and water balances spatially, taking into account the variation of the moisture distribution on the ground surface. Previously, one-dimensional simulations have been used to estimate the heat and water balance of non-sloping surfaces. To estimate the heat and water balance spatially while taking into account ground surface roughness, it is necessary to first estimate the heat and water balance of the ground surface on the basis of slope orientation. The purpose of this study was to clarify those factors, in addition to shortwave radiation, that affect the heat balance of a bare sloping surface at different orientations. To achieve this, the heat balance calculated using observational data of bare ground, including unevenness, was compared with the heat balance estimated by HYDRUS-1D simulation for each slope orientation. Additionally, the brightness index of RGB images was calculated and compared with the relative ground surface brightness and the estimated heat balance for each slope orientation. The estimated results at night and at sunrise/sunset were extremely small in comparison with the calculated results, and the heat balance simulation in the absence of shortwave radiation remained an issue. The relationship between ground surface brightness and ground conduction heat was completely different depending on slope direction, suggesting that ground surface heat transfer is affected substantially by factors other than shortwave radiation related to slope orientation. The findings indicate that it is necessary to examine the effects of heat transfer in detail to estimate the heat balance related to slope orientation.

Keywords: Heat balance; Net radiation; Slope orientation; Monitoring; HYDRUS-1D

Research on Time-domain Airborne EM Full-field Apparent Resistivity Imaging Method for Arbitrary Transmitting Waveform

Authors:  Jianbo Zheng; Yanfu Qi

DOI: 10.32389/JEEG22-023

ABSTRACT:  The time-domain airborne electromagnetic (AEM) system can do fast EM surveys over mountainous areas by carrying its detection equipment on an airplane. Due to the dense sampling of AEM method, it generates a huge amount of data. As a result, the resistivity imaging methods have become the first choice for data interpretation because they are fast. However, the traditional imaging methods do not fully consider the influence of the transmitting waveform. When they are used to deal with the AEM data with complex current waveform, the imaging results are seriously affected. Therefore, we develop a universal full-field apparent resistivity imaging method for AEM data with arbitrary transmitting waveform. Firstly, we calculate the convolution of time derivative of the current waveform and step response to obtain the time-domain AEM response of the arbitrary transmitting waveform. Then the full-field apparent resistivity imaging method based on the inverse function theorem is used to complete the rapid imaging of AEM data with complex waveform. Finally, we apply our imaging codes to both synthetic and field data to verify its correctness.

Keywords: Time-domain; Airborne EM; Transmitting waveform; Full-field apparent resistivity.

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