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- Volume 17, Issue 2, 2019
Near Surface Geophysics - Volume 17, Issue 2, 2019
Volume 17, Issue 2, 2019
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The downward continuation of aeromagnetic data from magnetic source ensembles
More LessABSTRACTDownward continuation is a commonly used geophysical filter that is applied to aeromagnetic data. It takes the data and produces from it the data which would have been measured had the sensor been closer to the source. A new downward continuation algorithm is introduced in this study, which is much more stable than the traditional method. It downward continues the data by a distance that is a fraction of the current depth, rather than by a fixed distance. Because of this, the data cannot be continued past the depth of the potential field sources (unlike the conventional method), and so the method is more stable. The method produces different anomaly amplitudes than the conventional method, but this is not an issue if certain further processing techniques (such as sunshading or the tilt angle) are to be used. The method is demonstrated on aeromagnetic data from Southern Africa.
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Full‐waveform inversion of complex resistivity IP spectra: Sensitivity analysis and inversion tests using local and global optimization strategies on synthetic datasets
Authors Alessandro Vinciguerra, Mattia Aleardi and Paolo CostantiniABSTRACTWe implement two different inversion strategies for solving the full‐waveform inversion of spectral induced polarization data: the first is a local inversion based on the Levenberg–Marquardt algorithm, whereas the second is a global optimization that makes use of the Particle Swarm Optimization algorithm. In addition, the analysis of the residual function maps and the singular value decomposition of the inversion kernel are used to analyse the complexity of the objective function and the ill‐posedness of this inverse problem. We limit the attention to synthetic data with the aim to maintain the inversion at a simple level and to draw essential conclusions about the ill‐conditioning of the considered inverse problem and about the suitability of the two inversion approaches we use. We consider two differexnt double‐dispersion reference models that generate resistivity amplitude and phase spectra with different characteristics. Realistic noise is added to the synthetic data to better simulate a field data set. We also apply a dedicated processing sequence to increase the signal‐to‐noise ratio of the observed data. It turns out that the full‐waveform inversion of spectral induced polarization is a well‐posed problem in case of double‐peaked resistivity spectra, whereas it becomes hopelessly ill‐conditioned when the subsurface model generates single‐peaked spectra. In particular, the analysis of the residual function maps demonstrates that in case of double‐peaked spectra the objective function is characterized by a well‐defined single minimum. Conversely, elongated valleys with similar misfit values arise for single‐peaked spectra. In this case, the eigenvectors in model space demonstrate that the estimate of the τ1 and c1 parameters is a non‐unique problem, and for this reason it would be advisable to reparametrize the inverse problem by considering a single‐dispersion model. In addition, our tests demonstrated the superior exploitation capability of the linearized inversion with respect to the global optimization, that is the ability to converge towards the minimum in case of objective functions with low gradient values.
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Application of ground penetrating radar to detect tunnel lining defects based on improved full waveform inversion and reverse time migration
Authors Fengkai Zhang, Bin Liu, Lanbo Liu, Jing Wang, Chunjin Lin, Lei Yang, Yao Li, Qingsong Zhang and Weimin YangABSTRACTGround penetrating radar is a popular approach to detect defects in tunnel lining. However, the interpretation is usually based on the original image, which is very different from the real shape of the lining defects. Full waveform inversion and reverse time migration are helpful to solve this problem. Full waveform inversion can invert the relative permittivity distribution and reverse time migration can migrate reflection events to their proper locations. Traditional full waveform inversion method is only applicable to cross‐hole ground penetrating radar data or surface multi‐offset ground penetrating radar data. We propose an improved full waveform inversion method which offers satisfactory inversion result for surface common‐offset radar. The forward modelled waveform and the objective function curve show that our new full waveform inversion method is much more accurate than traditional full waveform inversion for common‐offset radar. Traditional reverse time migration has weaker amplitude with increasing depth; we use an energy matrix to improve the imaging effect. Moreover, our reverse time migration is based on the relative permittivity distribution obtained from full waveform inversion, which provides more accurate imaging result. Through several numerical and engineering examples, we discuss the application of both methods in tunnel lining inspection. The results show that for tunnel lining without rebars, the combined methods can give satisfactory imaging results. But the image quality deteriorates rapidly when dealing with rebars.
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Fusion of multiple time‐domain GPR datasets of different center frequencies
Authors Xianlei Xu, Junpeng Li, Xu Qiao and Gui FangABSTRACTGround‐penetrating radar is widely used in non‐destructive underground inspection because of its precision and high resolution, especially at shallow layers. However, single‐antenna ground‐penetrating radar data may lead to erroneous evaluations. To avoid this problem, we propose a fusion method for ground‐penetrating radar data acquired at different center frequencies. The datasets are first preprocessed using zero calibration and denoizing, and subsequently processed further using a novel algorithm for spatial (horizontal and vertical) calibration that produces results in the same spatial coordinate system. We carried out a comparative fusion experiment using Fourier, wavelet, S and principal‐component transforms and using data acquired at center frequencies 100, 200 and 400 MHz. Our results show that the proposed fusion algorithm can effectively exploit the complementarity and redundancy of information at different center frequencies, with the wavelet‐transform‐based fusion showing the best performance on image quality according to different metrics. Moreover, the proposed method enhanced the information entropy and the average gradient, whereas the root‐mean‐square error of the fusion data remained below 24%.
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The fate of the historic fortifications at Alcatraz island based on terrestrial laser scans and ground‐penetrating radar interpretations from the recreation yard
Authors T.S. de Smet, M.E. Everett, R.R. Warden, T. Komas, J.N. Hagin, P. Gavette, J.A. Martini and L. BarkerABSTRACTAlcatraz Island National Historic Landmark is best known as a former high‐security federal penitentiary that once housed many of the United States most notorious gangsters. Today, it is a popular tourist destination adjacent to the Golden Gate Bridge in scenic San Francisco Bay. Alcatraz is less known in its former military role as a 19th‐century coastal fortification protecting the interests of a rapidly westward‐expanding nation during the turbulent era of Manifest Destiny, the 1849 Gold Rush and the Civil War. The fortification, with its underground ammunition magazines and tunnels, is important from a military history perspective, marking the transition to earthen structures from the traditional brick and masonry constructions that characterized earlier 19th‐century coastal defences. In this paper, geophysical interpretations based upon an attribute analysis of ground‐penetrating radar data are combined with terrestrial laser scans, georectifications based on historical documents, maps and photographs to develop an iterative model for locating and determining the extent and integrity of subsurface historical architectural remains beneath the former recreation yard of the Alcatraz penitentiary. Using this approach, remnants of buried structures including a ‘bombproof’ earthwork traverse and its underlying vaulted brick masonry tunnel and ventilation ducts were discovered to run east‐west beneath the recreation yard. This integrative and iterative process permits accurate spatial identification of these and other 19th‐century architectural structures depicted in historical documents. This approach can be applied to subsurface investigations at other important cultural landmarks worldwide.
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Relationship between electrical conductivity and water content of peat and gyttja: implications for electrical surveys of drained peatlands
Authors J. Walter, E. Lück, C. Heller, A. Bauriegel and J. ZeitzABSTRACTThe application of electrical resistivity tomography to peatlands supports conventional coring by providing data on the current condition of peatlands, including data on stratigraphy, peat properties and thickness of organic deposits. Data on the current condition of drained peatlands are particularly required to improve estimates of carbon storage as well as losses and emissions from agriculturally used peatlands. However, most of the studies focusing on electrical resistivity tomography surveys have been conducted on natural peatlands with higher groundwater levels. Peatlands drained for agriculture have not often been studied using geophysical techniques. Drained sites are characterized by low groundwater levels and high groundwater fluctuations during the year, which lead to varying levels of water saturation. To validate better electrical resistivity tomography surveys of drained peatlands, the aim of this laboratory study is to investigate the influence of varying water saturation levels on electrical conductivity (reciprocal of resistivity) for a variety of peat and gyttja types, as well as for different degrees of peat decomposition. Results show that different levels of water saturation strongly influence bulk electrical conductivity. Distinct differences in this relationship exist between peat and gyttja substrates and between different degrees of peat decomposition. Peat shows an exponential relationship for all degrees of decomposition, whereas gyttja, in particular organic‐rich gyttja, is characterized by a rather unimodal relationship. The slopes for the relationship between electrical conductivity and water content are steeper at high degrees of decomposition than for peat of low degrees of decomposition. These results have direct implications for field electrical resistivity tomography surveys. In drained peatlands that are strongly susceptible to drying, electrical resistivity tomography surveys have a high potential to monitor the actual field water content. In addition, at comparable water saturations, high or low degrees of decomposition can be inferred from electrical conductivity.
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Machine learning to estimate soil moisture from geophysical measurements of electrical conductivity
Authors Davood Moghadas and Annika BadorreckABSTRACTSoil water content (θ) is a key variable in different earth science disciplines since it mediates the water and energy exchange between the surface and atmosphere. Electrical and electromagnetic geophysical techniques have been widely used to estimate soil electrical conductivity (σ) and soil moisture. However, obtaining the relationship is not straightforward due to the non‐linearity and also dependency on many different soil and environmental properties. The purpose of this paper is to determine if artificial neural network is an appropriate machine learning technique for relating electrical conductivity to soil water content. In this respect, time‐lapse electrical resistivity tomography measurements were carried out along a transect in the Chicken Creek catchment (Brandenburg, Germany). To ensure proper retrieval of the σ and θ, reference values were measured near the beginning of the transect via an excavated pit using 5TE capacitance sensors installed at different depths. We explored robustness and pertinence of the artificial neural network approach in comparison with Rhoades model (as a commonly used petrophysical relationship) to convert the inversely estimated σ from electrical resistivity tomography to the θ. The proposed approach was successfully validated and benchmarked by comparing the estimated values with the reference data. This study showed the superiority of the artificial neural network approach to the Rhoades model to obtain relationship. In particular, artificial neural network allowed for more accurate estimation of the temporal wetting front than the petrophysical model. The proposed methodology thus offers a great promise for deriving spatiotemporal soil moisture patterns from geophysical data and obtaining the in situ relationship, taking into account the non‐linear variations of the soil moisture.
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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)