ABSTRACT In large cities with a growing population, the expansion of construction and demolition (C&D) waste of buildings can lead to the annual production of millions of tons of waste. Any solution for inexpensive, rapid, and accurate identification of these depots' layering structure and composition can greatly benefit urban management. Geoelectric methods can serve as a simple, cost-effective, and sufficiently accurate means of identifying these materials. In this study, the layering structure of C&D waste buried in line 4 of the Hesar landfill in Karaj City, Iran, was determined using a geoelectric method involving the assessment of electrical resistivity variations in both vertical and horizontal directions. Wenner-Schlumberger array was utilized for the measurements, and 2,870 electrical resistivity points were collected. RES2D-INV software was utilized to interpret the data and plot geological sections. In addition, a series of laboratory tests, including Sieve analysis and water content, were performed on samples collected by continuous core boring to better understand the waste composition. According to the study results, compared to the field data, it was concluded that the ERT could accurately predict the changes in various soil layers, aggregate sizes, and water content. In addition, data from boreholes revealed that most of the materials buried in this depot are concrete. The results of this study can assist urban management in recycling C&D waste. Recycling C&D waste recovers some economic value from C&D waste, clearing out C&D waste depots and eliminating the environmental desirability issues that they can cause in surrounding areas.

ABSTRACT Metallic structures are a common noise source in Slingram electromagnetic surveys because they interact with the primary magnetic field generated by the transmitter coil and create a secondary magnetic field that distorts the apparent conductivity readings from a layered or homogeneous subsurface. The main challenge is understanding how their influence varies depending on the loop configuration and the distance at which the surface structure no longer distorts the apparent conductivity readings. We propose a methodology that integrates experimental techniques and numerical modeling to elucidate the distortions induced by a fence-like structure placed at varying distances from the coils of a low-induction conductivity meter. Additionally, we aim to evaluate the model responses using numerical simulations with electromagnetic systems operating at higher frequency ranges. To quantify the distortions in apparent conductivity, we conducted field measurements by keeping the electromagnetic coils in a fixed position while changing the placement of the conductive structure. The readings obtained before installing the conductive structure were used as the background reference response to accurately determine the distortions. The same experimental procedure was tested in terrains with different resistivities to evaluate the changes caused by the same structure in different backgrounds. Our results show that this approach allowed us to infer that the deviations in apparent conductivity caused by the presence of the fence are more noticeable where the background resistivity is higher. However, these deviations were observed only for short distances of about 1/5 to 1/2 of the coil separation for the evaluated targets. The integration between the experimental survey and the numerical modeling suggests that distortion increases when the instrument is operated at induction numbers higher than ∼0.2. For all tested conductivity models and frequency ranges, minor distortions were observed with the vertical coplanar coil configuration compared to the horizontal coplanar configuration for the transmitter-receiver pair. Negligible distortions were observed for fences with unconnected wires, suggesting minor interference from loose metallic objects on the ground surface that do not form closed loops, favoring EM induction.

ABSTRACT Swells in marine seismic data disrupt the continuity of the reflection events and make interpretation difficult. The swell effect removal process consists of sea-bottom detection and correction, which removes the swell effect by shifting the detected sea-bottom signal to the known or estimated seafloor location. The quality of correction depends on the accuracy of the sea-bottom detection. Since general sea-bottom signal detection techniques have been applied directly after pre-processing, many mispick scenarios exist due to the characteristics of the data, and additional processes are needed. This study proposes a method for accurately detecting sea-bottom reflection signals using the guideline. The errors are caused by mistaking other reflection events as sea-bottom signals. The guideline can improve detection accuracy by providing rough event times of sea-bottom reflection. The guideline was extracted from the gradient vector flow (GVF) technique, a segmentation method in image processing. In the GVF data, the seafloor signals are smoothed, and the energy is focused on the sea bottom. The threshold method detected sea-bottom signals in the range after the guideline. The detected signals were shifted to estimated seafloor locations to attenuate the swell effect. The GVF-based guideline (GVF-GL) method was applied to field data acquired by the airgun and chirp sources with different frequencies. The sea-bottom location was successfully detected, and the continuity of the sea-bottom and subsurface signals in the section was improved.

ABSTRACT As we seek to make geophysical models more consistent with geological models, more and more a priori information is incorporated into geophysical inverse problems. How we make the most use of a priori information is still a key issue in geophysical inversion, particularly for effectively representing such information in inversion problems. To address this problem, we propose a symmetric polynomial constrained inversion algorithm that can represent multiple known physical properties for the lithology. The electrical resistivity synthetic model tests show that the inversion results constrained with symmetric polynomials can make the model resistivity be in line with a priori information and obtain more realistic sharp boundaries. Finally, we give a practical verification of our inversion algorithm with field data.

ABSTRACT This study reports the combination of remote sensing and ground geophysical techniques to locate an abandoned and hidden municipal solid waste landfill located in a fluvial plain in the French Western Alps. Following earthworks and further floods that eroded into the river bank, wastes made of a mixture of plastic, metal and soil/earth, were uncovered and some of them flowed into the river. The existence of an abandoned landfill, several decades-old, was known, but the knowledge of its exact location was forgotten. Historic aerial photographs back to 1948 allowed delineation in space and time of the location of a platform that was used for landfill operations between around 1973 and 1983. A LiDAR DEM acquired in 2012 allowed was used to locate topographic depressions 0.1 to 0.4 m in depth, notably inside the platform. These depressions are interpreted as resulting from differential compaction originating from the presence of compressible wastes. Geophysical mapping techniques (magnetic and electromagnetic) confirmed the presence of anomalies inside the identified platform. Geophysical imaging techniques (ground-penetrating radar, electrical resistivity tomography) provided a quantitative evaluation of the width and depth of the individual pits. The combination of the different techniques allowed for estimating the first-order volume of waste. The methodology adopted in this work is applicable to detect landfills exhibiting differential compaction and physical contrasts.

Next No AccessJournal of Environmental and Engineering GeophysicsVolume 28, Issue 3Editors' ForewordAuthors: Afshin AghayanAleksandra VarnavinaAfshin Aghayan and Aleksandra Varnavinahttps://doi.org/10.32389/JEEG23-097 SectionsAboutFull TextPDF/ePub ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookTwitterLinked InReddit "Editors' Foreword." Journal of Environmental and Engineering Geophysics, 28(3), p. iiiReferences Chen, L. , Wang, R. , Gong, Z. , Fu, C. , Xu, X. , Wang, K. , Li, D. , and Shao, W. 2023, Tunnel Geohazard Evaluation Using Improved Seismic Time-Reversal Mirror: Journal of Environmental and Engineering Geophysics, 28(3) 113–123. AbstractGoogle Scholar Pope, G. G. , & Nyquist, J. E. 2023, Apparent Hysteresis in Archie's Law: Implications of Changing Pore Fluid Conductivity for Electrical Resistivity Monitoring of Infiltration: Journal of Environmental and Engineering Geophysics, 28(3) 125–136. AbstractGoogle Scholar Yang, Y. , & Qin, C. 2023, Analysis on the induced polarization characteristics of "three-phase" in coal goaves: Journal of Environmental and Engineering Geophysics, 28(3) 137–145. AbstractGoogle Scholar Weiqiang, L. 2023, Deep learning based noise identification for CSAMT data processing: Journal of Environmental and Engineering Geophysics, 28(3) 147–152. AbstractGoogle ScholarFiguresReferencesRelatedDetails Volume 28Issue 3Sep 2023Pages: iii-154ISSN (print):1083-1363 ISSN (online):1943-2658 publication data© 2023 Publisher:Environmental & Engineering Geophysical Society HistoryPublished Online: 31 May 2024Published in print: 01 Sep 2023 CITATION INFORMATION Afshin Aghayan and Aleksandra Varnavina, (2023), "Editors' Foreword," Journal of Environmental and Engineering Geophysics 28: iii-iii. https://doi.org/10.32389/JEEG23-097 Plain-Language Summary PDF DownloadLoading ...

Next No AccessJournal of Environmental and Engineering GeophysicsVolume 27, Issue 4Introduction to the Journal of Environmental and Engineering Geophysics Special Issue on the Application of Proximal and Remote Sensing Technologies to Soil InvestigationsAuthors: Barry AllredAsim BiswasCraig LobseyLaurie WhitesellBarry Allred*Corresponding Author – E-mail Address: [email protected] USDA Agricultural Research Service, Soil Drainage Research Unit, Columbus, Ohio, USA Search for more papers by this author, Asim Biswas University of Guelph, School of Environmental Sciences, Guelph, Ontario, Canada Search for more papers by this author, Craig Lobsey University of Southern Queensland, School of Mechanical and Electrical Engineering, Toowoomba, Queensland, Australia Search for more papers by this author, and Laurie Whitesell Society of Exploration Geophysicists, Near Surface Geophysics & Mining and Mineral Exploration, Tulsa, Oklahoma, USA Search for more papers by this authorhttps://doi.org/10.32389/JEEG22-080 SectionsAboutFull TextPDF PlusePUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookTwitterLinked InReddit "Introduction to the Journal of Environmental and Engineering Geophysics Special Issue on the Application of Proximal and Remote Sensing Technologies to Soil Investigations." Journal of Environmental and Engineering Geophysics, 27(4), pp. vi–viiFiguresReferencesRelatedDetails Volume 27Issue 4Dec 2022Pages: vi-244ISSN (print):1083-1363 ISSN (online):1943-2658 publication data© 2022 Publisher:Environmental & Engineering Geophysical Society HistoryPublished Online: 21 Apr 2023Published in print: 01 Dec 2022 CITATION INFORMATION Barry Allred, Asim Biswas, Craig Lobsey, and Laurie Whitesell, (2022), "Introduction to the Journal of Environmental and Engineering Geophysics Special Issue on the Application of Proximal and Remote Sensing Technologies to Soil Investigations," Journal of Environmental and Engineering Geophysics 27: vi-vii. https://doi.org/10.32389/JEEG22-080 Plain-Language Summary Loading ...

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.

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.

ABSTRACT Joint inversion of different geophysical methods is a powerful tool to overcome the limitations of individual inversions. Body wave tomography is used to obtain P-wave velocity models by inversion of P-wave travel times. Surface wave tomography is used to obtain S-wave velocity models through inversion of the dispersion curves data. Both methods have inherent limitations. We focus on the joint body and surface waves tomography inversion to reduce the limitations of each individual inversion. In our joint inversion scheme, the Poisson ratio was used as the link between P-wave and S-wave velocities, and the same geometry was imposed on the final velocity models. The joint inversion algorithm was applied to a 2D synthetic dataset and then to two 2D field datasets. We compare the obtained velocity models from individual inversions and the joint inversion. We show that the proposed joint inversion method not only produces superior velocity models but also generates physically more meaningful and accurate Poisson ratio models.