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- Volume 21, Issue 2, 2023
Near Surface Geophysics - Volume 21, Issue 2, 2023
Volume 21, Issue 2, 2023
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Two‐dimensional joint inversion of electromagnetic soundings at low induction numbers and direct current resistivity
AbstractDirect current resistivity and electromagnetic methods at low induction numbers are commonly used to characterize near‐surface structures. Although both methods are related to the same property, resistivity or conductivity, they have different sensitivities. The electromagnetic method at low induction numbers is more sensitive to conductive structures, but faces problems resolving resistive bodies, whereas the direct current method can image conductive and resistive variations. Additionally, the electromagnetic method at low induction numbers is less expensive and faster to collect the data, which seems a promising way to provide information at a low cost to enhance the model robustness. Aside from that, the exploration depth in both methods is not the same, but they can complement each other. In this work, a joint inversion algorithm based on a linear approximation was developed to incorporate both data. To achieve the goal, we rewrite the linear integral equations of electromagnetic data at low induction numbers in terms of resistivity logarithm to overcome the differences in magnitude and incorporate both data sets. We explore the potential of the joint inversion testing the algorithm with two synthetic models. In our tests, the joint model improvements are mainly in the conductive geometry and show favourable influence compared to the individual models. Finally, we tested the algorithm with field data collected in the coastal aquifer at Maneadero Valley. Despite the high anthropogenic electromagnetic noise in the area, the joint inversion results are coherent with the individual inversions of electromagnetic soundings at low induction numbers and direct current resistivity along with the geological setting.
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Preferential flow between rivers and aquifers in alluvial floodplains: A key to modelling and sustainably managing shallow groundwater resources
Authors J. Michael Martin, Mark E. Everett, Peter S. K. Knappett and Ryan C. EwingAbstractPreferential flow between rivers and aquifers in alluvial floodplains may be a core component of shallow groundwater transport and, consequently, its understanding is key to modelling and managing groundwater resources. At a clay wedge separating present‐day streamflow and bank storage from an adjacent shallow aquifer, we image a suspected sand‐dominated structure. This structure cuts through the clay wedge and possesses temporally dynamic electrical resistivity as seen in time‐lapse electrical resistivity tomographic (ERT) images collected over a 61‐day study period. During days 11–12, following heavy rainstorms, the cross section of the electrically resistive sand fades into the background resistivity structure, reappearing the following day. This research shows that preferential flow can be imaged in time‐lapse ERT in buried sand‐dominated structures between a floodplain and the adjacent river. Our analysis demonstrates that sand conduits can transport infiltrated rainwater from the floodplain into the river as a bank spring and, hypothetically, at high‐stage streamflow, from the river into the adjacent shallow aquifer. In both directions, these conduits for preferential flow exert an important control on the regulation and distribution of water, sediments and contaminants. This phenomenon will help hydrological models to incorporate more real‐world phenomena and ultimately better prepare groundwater managers to sustainably steward shallow groundwater resources.
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Improved Tucker decomposition algorithm for noise suppression of 3D GPR data in road detection
Authors Kun Yan, Zhihua Zhang and Xianlei XuAbstractThe echo data of ground‐penetrating radar (GPR) have a low signal‐to‐noise ratio. Denoising and interference suppression are important for improving the accuracy of underground target recognition and detection. In this paper, a new method of noise analysis and suppression of 3D GPR is proposed, transforming the problem of noise reduction into an optimization problem regarding a third‐order tensor. This method is improved with the following features; a bidimensional empirical mode decomposition (BEMD) algorithm is employed to decompose the 3D GPR noise. The main source of the noise is determined by computing the standard deviation of each component decomposed. For the shortcoming of the existing GPR denoising methods that reduce the dimension of 3D GPR data, the proposed approach can denoise 3D GPR data directly to avoid the loss of data caused by reducing the dimension. This preserves the desired signal and extracts noise through the Tucker decomposition of 3D GPR data. An improved high‐order orthogonal iteration algorithm is utilized to optimize the decomposition. The peak signal‐to‐noise ratio (PSNR) of the signal for each survey line is calculated to evaluate the effectiveness of noise reduction. Simulations and real data sets are provided to compare the performance of algorithms. The results show that the mean PSNR after noise suppression with our method is 8.915 dB and 16.458 dB higher than wavelet transform and singular value decomposition algorithms, respectively. This demonstrates that the proposed approach can better suppress 3D GPR noise and provides technical support for improving 3D GPR data quality and road detection accuracy.
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Geophysical mapping of freshwater lens in Big Pine Key, Florida: Electromagnetic Induction Calibration and Application
Authors Michael Eyob Kiflai and Dean WhitmanAbstractGeoelectric and electromagnetic (EM) methods are rapid and non‐invasive geophysical techniques for estimating groundwater properties and characterizing the spatial and temporal variability of subsurface formations. However, to quantitatively interpret the EM data, the systematic error due to calibration problems and random error must be considered. We conducted coincident EM and electrical resistivity tomography (ERT) surveys in January and December 2018 on Big Pine Key, FL. In this study, we used vertical electrical sounding (VES) data extracted from a 220 m long ERT profile to calibrate the EM data. The inverted VES data were used as input in the EM forward model to estimate the quadrature component response. Then, the observed offset between the calculated and observed quadrature data was corrected using a multiple linear regression model. Finally, the calibrated quadrature data were converted to apparent electrical conductivity and inverted as a 2‐layer model using both the full solution and the low induction approximation. These models were used to assess the temporal and spatial variations of conductivity on a 2.2 km long E–W trending profile across the island. The calibrated apparent electrical conductivity on the profile decreased between January and December 2018, with the largest decreases in the lower elevation regions of the profile. In addition, the 2‐layer models inverted using the full solution and low induction approximation showed the depth of the freshwater interface increased by December 2018. These observations suggest the recovery of the freshwater lens due to precipitation. Based on this study, we concluded the VES at pilot locations can be used for calibration purposes and verification of the accuracy of EM measurements.
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Volumes & issues
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Volume 22 (2024)
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Volume 21 (2023)
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Volume 20 (2022)
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Volume 19 (2021)
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Volume 18 (2020)
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Volume 17 (2019)
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Volume 16 (2018)
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Volume 15 (2017)
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Volume 14 (2015 - 2016)
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Volume 13 (2015)
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Volume 12 (2013 - 2014)
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Volume 11 (2013)
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Volume 10 (2012)
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Volume 9 (2011)
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Volume 8 (2010)
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Volume 7 (2009)
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Volume 6 (2008)
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Volume 5 (2007)
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Volume 4 (2006)
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Volume 3 (2005)
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Volume 2 (2004)
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Volume 1 (2003)