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- Volume 20, Issue 4, 2022
Near Surface Geophysics - Volume 20, Issue 4, 2022
Volume 20, Issue 4, 2022
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Near‐surface three‐dimensional multicomponent source and receiver S‐wave survey in the Tannwald Basin, Germany: Acquisition and data processing
Authors Thomas Burschil, Hermann Buness and Cedric SchmelzbachABSTRACTShallow 3‐D reflection seismic surveys using S‐waves have rarely been carried out, even though S‐waves can provide higher resolution subsurface images than P‐waves. We conducted a 3‐D near‐surface multicomponent source and receiver survey in Quaternary sediments. We employed a small electrodynamic seismic source with a horizontal shaking unit operated in two orientations. Three‐component geophones in an orthogonal layout covering an area of 117×99 m2 were used for recording. Changes in weather and ground conditions, including freezing and thawing during acquisition, directly influenced the data quality and resulted in discernible relative time shifts in the data. Our seismic processing flow included a four‐component rotation of the data from the Cartesian acquisition geometry into the ‘natural’ coordinate frame to orient sources and receivers in radial or transverse orientation to separate different S‐wave polarizations. The rotation increased the signal strength and helped, for example, to improve the quality of the images of the basin base. The irregular offset distribution in the common midpoint gathers impedes filtering to suppress surface waves in the f–k domain. We, therefore, applied a common‐reflection surface processing flow. After regularization, we could better remove the energy of the surface waves. Both stacked 3‐D S‐wave volumes of vertical and horizontal polarizations provide images of the Quaternary overdeepened Tannwald Basin that was partly known from previous P‐ and S‐wave 2‐D surveys. Compared to a P‐wave profile adjacent to the volume, however, the S‐wave volumes provide higher resolution images of the basin base and internal structure. The basin base is well mapped in three dimensions and shows undulations that were not obvious from the P‐wave data. Comparing the S‐wave volumes of different polarizations, we find only minor differences in the stacks and interpretations.
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Marine karst environment characterization using joint geophysical and geotechnical data
Authors Judith Dusart, Pascal Tarits, Sara Bazin, Rocio Isorna and Jean‐François d'EuABSTRACTSubmarine karstic environments are complex and challenging to study. Seismic investigations usually have difficulty getting geological information because of a lack of penetration due to the high reflectivity of the calcareous substratum. To circumvent this problem, we studied how to combine marine electrical resistivity tomography (MERT) with geotechnical data to investigate the porosity structure from the geotechnical to the geophysical scale. We applied the technique to the submarine karstic plateau of Banc de Guérande (Saint‐Nazaire, France), which is mainly composed of hard calcarenite and sandy pockets. We obtained sections of two‐dimensional resistivity models from the MERT data inversion. We used existing geotechnical data on extracted cores at several boreholes close to the MERT profiles using a multi‐sensor core logging (MSCL) bench. We used porosity proxies derived from Archie's law and porosity data from the MSCL inferred from gamma density measurements on the core to combine the data of very different scales (metre for MERT and centimetre for MSCL). The comparison between measurements showed a good similarity between in situ MERT and borehole MSCL data at depths greater than ∼10 m below the seafloor. A larger difference was observed close to the seabed, where the MERT porosity was higher than the MSCL porosity. The extraction of water‐saturated cores and the numerous core fractures could explain this difference near the surface. The results were analysed with respect to the scale difference between geophysical and geotechnical data. The conclusions suggested that the difference between MERT and MSCL porosities could be testified from the local heterogeneity of the soil and indicated whether the surrounding substratum was more porous (and thus fractured or dissolved) than the core or vice versa. The study highlighted the necessity of an excellent collocation of the data to retrieve reliable information from the comparison between geophysical and geotechnical data.
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Geophysical monitoring of a laboratory‐scale internal erosion experiment
Authors Yara Maalouf, Grégory Bièvre, Christophe Voisin and Naji KhouryABSTRACTEarth dams are structures used worldwide for water management. Their failure over time is notably due to water seepage generating internal erosion. There is a growing need to detect the processes at work as early as possible. This study presents a controlled laboratory experiment aimed at detecting and monitoring water seepage into a soil sample. The experiment was monitored with electrical resistivity tomography, velocimeters and video recording. The video recording of the downstream side of the soil sample shows successive episodes of mass movements associated with a progressive water flow increase. The electrical resistivity tomography, limited by a low temporal resolution, shows an evolution of the resistivity in agreement with the evolution of the soil sample (e.g., saturation and mass movements), but with strong limitations regarding the robustness of the results. The continuous seismic recording reveals extra rupture episodes that occur inside the volume of the soil sample, which were not recorded by the video. Their distribution in time and energy illustrates strongly nonlinear changes in the soil sample, with several phases of acceleration. A controlled source monitoring using external repetitive events allows probing the medium with an enhanced temporal resolution compared to electrical resistivity tomography. The apparent seismic velocity of the soil sample reveals a nonlinear decrease, high at the beginning of the experiment, and then stalled until the different mass movements enlarge the amount of water inside the sample along with the water flow. The different techniques used, especially seismic monitoring, describe a complex and strongly nonlinear process of internal erosion centred around the coupling between water flow and internal damage. Finally, these findings suggest that seismic methods could be able to distinguish the four different phases of internal erosion (namely, initiation, continuation, progression and failure) discussed in the geotechnical literature.
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Investigation of the linear structures of the Menderes Massif (Western Anatolia) using the moving average differences ‘mad’ boundary analysis method
Authors Nedim Gökhan Aydın and Turgay İşsevenABSTRACTThe Menderes Massif, located at Western Anatolia, Turkey, is a wide area that is under an extensional regime, resulting in the area having large grabens along with many faults and being geothermally rich. Due to having a large number of linear structures, the Menderes Massif has been a popular area for boundary analysis studies using gravity and magnetic prospecting. Since both prospecting methods result in potential field anomalies that are directly related to the positions of the anomaly sources, boundary analyses prove useful to mark the location of linear subsurface features. In this study, we have examined the Menderes Massif with a different boundary analysis approach that we call moving average differences. We have briefly introduced the method via synthetic gravity anomalies and carried out a tests to show that the algorithm is effective. Next, we have applied the moving average differences method to the actual Bouguer gravity data from the Menderes Massif and interpreted the results, briefly. Finally, we have compared our results with the already‐known faults and two other boundary analysis studies’ results from the area. The moving average differences results revealed new lineaments spread all around the Menderes Massif, which are possibly contacts/faults that are yet to be mapped. Our results also indicate that the moving average differences algorithm we have implemented provides useful information about the lineaments within the gravity anomalies, sufficient for it to be acknowledged as a boundary analysis method.
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GPR denoising via shearlet transformation and a data‐driven tight frame
Authors Liang Zhang, Jingtian Tang, Yaqi Li, Zhengguang Liu, Wenjie Chen and Guang LiABSTRACTGround‐penetrating radar (GPR) is commonly used to detect buried and near‐surface geophysical structures. GPR denoising is necessary because some level of interference, such as from clutter, random noise and/or the column artefact, are inevitable and can cause false geological interpretations. Existing sparse representation methods, including wavelet transformation, curvelet transformation and dictionary learning, are critical in GPR denoising. However, they perform poorly in some cases because GPR data cannot be represented efficiently under severe interference. Thus, this study proposes an approach that combines shearlet transformation (ST) and a data‐driven tight frame (DDTF) to improve data sparsity. The ST can provide the prior information of GPR data to the DDTF, while the DDTF can self‐adaptively represent GPR data. First, we separate significant reflections and interferences using ST. Second, we apply the DDTF to further suppress the interferences by setting different thresholds in different ST scales and directions. Finally, we adopt inverse transformations to recover the GPR data. In the experiments, ST is used to show the differences between the significant reflections and interferences of the synthetic GPR data. We also sequentially remove each interference of the synthetic GPR data to clearly highlight the performance of the method. To ensure the effectiveness of the ST‐DDTF approach, we test the method using synthetic GPR data from different models, along with some example field GPR data. The ST‐DDTF method, which is aimed at improving data sparsity, shows state‐of‐the‐art results relative to more standard GPR denoising methods. Although our approach is time consuming, it is useful in processing small GPR data and obtaining accurate denoising results.
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Vertical electrical sounding data inversion using continuous ant colony optimization algorithm: A case study from Hassi R'Mel, Algeria
Authors Lyes Bouchaoui, Jalal Ferahtia, Mohammed Farfour and Nouredine DjarfourABSTRACTAmong the existing geophysical methods, the vertical electrical sounding remains a fast and economical way to detect groundwater resources. However, the interpretation of the vertical electrical sounding data often suffers from non‐uniqueness due to the ill‐posed nature of the inverse problem. In recent years, metaheuristic algorithms have been successfully used for solving ill‐conditioned and ill‐posed problems. This work presents a scheme that uses the continuous ant colony optimization (ACOR) technique to invert vertical electrical sounding data. The ACOR is a global search algorithm that explores and finds the globally optimal solution over a search space by mimicking the behaviour of biological ants. The development of this algorithm was due to the requirement to interpret a set of vertical electrical sounding collected at the region of Hassi R'Mel (Algerian Sahara). The area has a particular geological/geoelectrical structure, which renders the interpretation of vertical electrical sounding challenging as standard inversion approaches tend to fail to recover a reliable resistivity model. The ACOR algorithm was initially tested with synthetic data from models simulating the geological/hydrogeological structure of the studied area. The results verified the robustness and stability of the ACOR algorithm even in the presence of a high level of noise. Furthermore, the tests indicated that the ACOR algorithm performed better when compared to other inversion techniques for this particular geoelectrical structure. Five vertical electrical sounding profiles using a Schlumberger array collected in the region of Hassi R'Mel were inverted using the ACOR algorithm. The models confirmed the presence of the two central aquifer systems and showed the geometry of the aquifer with the most favourable conditions for water accumulations.
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Machine learning‐accelerated gradient‐based Markov chain Monte Carlo inversion applied to electrical resistivity tomography
Authors Mattia Aleardi, Alessandro Vinciguerra, Eusebio Stucchi and Azadeh HojatABSTRACTExpensive forward model evaluations and the curse of dimensionality usually hinder applications of Markov chain Monte Carlo algorithms to geophysical inverse problems. Another challenge of these methods is related to the definition of an appropriate proposal distribution that simultaneously should be inexpensive to manipulate and a good approximation of the posterior density. Here we present a gradient‐based Markov chain Monte Carlo inversion algorithm that is applied to cast the electrical resistivity tomography into a probabilistic framework. The sampling is accelerated by exploiting the Hessian and gradient information of the negative log‐posterior to define a proposal that is a local, Gaussian approximation of the target posterior probability. On the one hand, the computing time to run the many forward evaluations needed for both the data likelihood evaluation and the Hessian and gradient computation is decreased by training a residual neural network to predict the forward mapping between the resistivity model and the apparent resistivity value. On the other hand, the curse of dimensionality issue and the computational effort related to the Hessian and gradient manipulation are decreased by compressing data and model spaces through a discrete cosine transform. A non‐parametric distribution is assumed as the prior probability density function. The method is first demonstrated on synthetic data and then applied to field measurements. The outcomes provided by the presented approach are also benchmarked against those obtained when a computationally expensive finite‐element code is employed for forward modelling , with the results of a gradient‐free Markov chain Monte Carlo inversion, and also compared with the predictions of a deterministic inversion. The implemented approach not only guarantees uncertainty assessments and model predictions comparable with those achieved by more standard inversion strategies, but also drastically decreases the computational cost of the probabilistic inversion, making it similar to that of a deterministic inversion.
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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)