SM5.2 | Geophysical imaging of near-surface structures and processes
EDI

Geophysical imaging techniques are widely used to characterize structures and processes in the shallow subsurface. Methods include imaging using P-wave seismic but also S-wave and multi-component techniques, (complex) electrical resistivity, electromagnetic, and ground-penetrating radar methods, as well as passive monitoring based on ambient noise or electrical self-potentials. Advances in experimental design, instrumentation, data acquisition, data processing, numerical modelling, and inversion constantly push the limits of spatial and temporal resolution. Despite these advances, the interpretation of geophysical images and properties often remains ambiguous. Persistent challenges addressed in this session include optimal data acquisition strategies, (automated) data processing and error quantification, appropriate spatial and temporal regularization of model parameters, integration of non-geophysical measurements and geological realism into the imaging process, joint inversion, as well as the quantitative interpretation of tomograms through suitable petro-physical relations.

In light of these topics, we invite submissions concerning a broad spectrum of near-surface geophysical imaging methods and applications at different spatial and temporal scales. Novel developments in the combination of complementary measurement methods, machine learning, and process-monitoring applications are particularly welcome.

Co-organized by EMRP2/GM2
Convener: Florian WagnerECSECS | Co-conveners: Ellen Van De VijverECSECS, James Irving, Frédéric Nguyen, Sonja Halina WadasECSECS, Cesare Comina, Thomas Burschil
Orals
| Mon, 24 Apr, 14:00–18:00 (CEST)
 
Room G2
Posters on site
| Attendance Mon, 24 Apr, 08:30–10:15 (CEST)
 
Hall X2
Posters virtual
| Attendance Mon, 24 Apr, 08:30–10:15 (CEST)
 
vHall GMPV/G/GD/SM
Orals |
Mon, 14:00
Mon, 08:30
Mon, 08:30

Orals: Mon, 24 Apr | Room G2

Chairpersons: Florian Wagner, Ellen Van De Vijver, James Irving
14:00–14:05
Geoelectrical methods
14:05–14:15
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EGU23-8542
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SM5.2
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On-site presentation
Peter Lelièvre, Elias Vandenberg, Heidi Hebb, Karl Butler, Xushan Lu, and Colin Farquharson

DC electrical resistivity surveying has shown much promise for investigating dikes and other earthen flood barriers. We are interested in the applicability of such data for aiding with maintenance and construction efforts in the Tantramar region of New Brunswick and Nova Scotia, Canada, where agricultural dikes form an important part of critical flood prevention infrastructure. Specifically, our goal is to develop efficient field survey and data processing protocols for detecting possible internal issues in the dikes ahead of further, more detailed geophysical surveying. The field survey protocol must be cost and time effective, given the large lengths of dikes that must be surveyed. The Tantramar dikes are expected to exhibit strong subsurface heterogeneity but accurately characterizing their internal structure may be challenging. Dikes have significant 3D geometry and traditional 2D DC surveying, and subsequent 2D inversion, fails to provide reliable and interpretable results. 3D surveying and inversion may be required but this represents significantly higher field costs. We performed a detailed synthetic inverse modelling study to help design our field surveying protocols. We used a representative model of a dike in the Tantramar region and we worked with the specifics of the surveying equipment available to us. We investigated and compared three possible data acquisition layouts proposed by other authors, we thoroughly compared the results of 2D versus 3D inversion on those layouts, and we performed a detailed investigation to assess best practices for 3D inversion mesh design. We are also incorporating joint interpretation with EM data, collected using mobile survey devices such as the Geonics EM31. Results from synthetic forward and inverse modelling are helping us develop future field data collection, processing and modelling protocols.

How to cite: Lelièvre, P., Vandenberg, E., Hebb, H., Butler, K., Lu, X., and Farquharson, C.: A protocol for assessing the effectiveness of electrical resistivity imaging for agricultural dike investigation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8542, https://doi.org/10.5194/egusphere-egu23-8542, 2023.

14:15–14:25
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EGU23-12499
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SM5.2
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ECS
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On-site presentation
Maxime Gautier, Stéphanie Gautier, and Rodolphe Cattin

Anthropogenic and natural hazards assessments need a good knowledge of the structures. A classical approach based on geological observations or soil mechanics investigations is often insufficient to characterize both the structures and the nature of subsurface materials. For several decades, near-surface geophysical methods have been integrated into a multidisciplinary strategy to improve the characterization of small-scale features of the subsurface. Electrical Resistivity Tomography (ERT) is a standard approach among these methods. This technique has several advantages, including easy deployment in the field and sensitivity to lithology, fluid contents, or chemistry. With this method, it is possible to detect and characterize the geometry of sliding surfaces on landslides and actives faults. It is also possible to set a permanent survey and obtain time-lapse images to describe temporal changes of resistivity within the subsurface and investigate dynamic processes, such as groundwater flows or soil moisture variations.
The ERT method consists of recording apparent resistivity data and inverting them to map the resistivity distribution at depth and to capture possible time changes. Many softwares, such as  Res2DInv, R2, or PyGimli, are now available to carry out the inversion. However, the quality assessment of the obtained models remains an open and challenging question. Indeed,  the robustness of the ERT results depends on factors such as the acquisition geometry, data error,  the resistivity contrast in the subsurface, the inversion procedure, and its parametrization. 
To overcome these limitations and allow a more accurate interpretation of the ERT models, we propose a new approach for assessing the reliability of ERT images. We propose a new algorithm called PySAM (Python Sensitivity Approach iMprovement) based on the open-source library PyGimli. This new tool provides relative and absolute error assessment on resistivity images from any ERT inversion software. We first illustrate the relevance of this new tool from synthetic tests associated with a well-contrained resisvity model. Next, we revisit the ERT image of the Topographic Frontal Thrust (TFT), a major active fault located in South Central Bhutan, and discuss its geometry which is a crucial parameter to discuss strain accommodation, and improve the seismic hazard assessment in Nepal, Bhutan, and northern India, one of the most densely populated regions.

How to cite: Gautier, M., Gautier, S., and Cattin, R.: A new approach to quantify the reliability of Electrical Resistivity Tomography (ERT) images, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12499, https://doi.org/10.5194/egusphere-egu23-12499, 2023.

14:25–14:35
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EGU23-2363
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SM5.2
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ECS
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On-site presentation
Clemens Moser, Adrian Flores Orozco, and Andrew Binley

The induced polarization (IP) method is an extension of the electrical resistivity method that allows the measurement of both the electrical conductive and capacitive properties of the subsurface; it is one of the main methods applied in landfills to characterize the geometry and composition of waste as well as the migration of leachate. Commonly, landfill IP investigations are based on measurements along several 2D lines. Considering the complexity of landfills, we investigate here the resolving capabilities of 2D parallel electrode lines with inline measurements, and 3D electrode configurations (grid array with electrodes set in a quadratic mesh and circular array with electrodes set in four concentric circles) through a numerical study and field measurements. The field surveys were conducted on two landfills with different waste composition, with measurements in the frequency range between 1 and 240 Hz to solve the frequency-dependence of the electrical properties. The results of both the numerical study and the field data show a lack of sensitivity in the case of the 2D configuration leading to the creation of artefacts in the conductivity magnitude and phase imaging result. An underestimation of IP values is also seen for these arrays; such effects are particularly critical in the case of heterogeneously distributed IP anomalies, which are typical in landfills. In contrast, the tested 3D configurations are able to resolve the geometry of the electrical units correctly and anomalies are more sharply defined compared to the results obtained by 2D configurations. Furthermore, our results show that the grid array with crossline measurements and multiple dipole orientations provides better results than the circular array, which lacks in the resolution in the central area. Additional investigations of the frequency-dependence of the field data demonstrate that for the different study areas only 3D configurations provide smooth spectra of the conductivity magnitude and phase, which is essential for an accurate estimation of relaxation (e.g., Cole Cole) parameters.

How to cite: Moser, C., Flores Orozco, A., and Binley, A.: Resolving capabilities of 3D electrode configurations for spectral induced polarization surveys, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2363, https://doi.org/10.5194/egusphere-egu23-2363, 2023.

14:35–14:45
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EGU23-5073
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SM5.2
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On-site presentation
Jacques Deparis, Ianis Gaudot, Francois Bretaudeau, Jean-Michel Baltassat, and Christophe Garnier

EZPONDA is a FEDER funded project, which aims to study both the mechanichal and chemichal processes related to the erosion of the coastal area in the Basque country, France. In the Socoa flysh cliff, the presence of fractures roughly perpendicular to the shoreline control the nucleation and the growth of underground erosion cavities. The ‘Socoa Semaphore cavity’ is the most striking one, with a propagation of the void up to 30 m inland. Mapping sea cliff fracturation extent around this cavity is a critical aspect to anticipate possible future erosion processes.

Assuming the permeability of the fractured material is higher than the permeability of the nonfractured material, mapping water infiltration in the subsurface may be used as a proxy to map the fracturation extent. In this work, we propose to monitor the sea water infiltration during high tide using passive seismic listening and self potential electrical response to illuminate fractures in the surrounding of the ‘Socoa Semaphore cavity’.

72 vertical component autonomous 5 Hz seismic sensor were deployed at the surface over 5000 m2 with an average interstation distance of 10 m. Continuous records were collected between 19/09/2020 and 22/09/2020 (4 days) during a large tidal event to include 8 high tides with a coefficient higher than 100.  It should be noted that the first two days of measurements were carried out over the weekend. The self-potential signals were recorded at the ground surface using a set of 20 nonpolarizable Pb/Pbcl2 electrodes. Data were recording using a Campbell Scientific CR1000 datalogger, with multiplexer chips used to switch between the pole electrodes. Voltage were measured between the 19/09/2020 11am to 21/09/2020 16pm.

The seismic spectrograms show that between 5-20 Hz, anthropological activities such as trafic and harbour modulate the seismic energy for all sensors. In the 20-40 Hz frequency range, the sea height modulates the seismic energy for all sensors, with a seismic energy decreasing as a function to the distance to the coast.  For a large frequency range between 10-50 Hz, we observe that the relative change in median spectral amplitude during high tides with respect to the median amplitude during the full observation period exhibits highest value over a restricted area (400 m2) located east to the the ‘Socoa Semaphore cavity’, which extends far beyond the known void extent. We argue that this area with a singular geophysical signature may be related to the presence of fracturation. Self potential measurement shows a lower noise during the night (around 4 mV) compare to the day (about 10 mV). In addition the noise is higher on Monday (about 20 mV). Self potential measurement show periodic oscillations with a period of 6.4 hours approximately, corresponding to half the tidal cycle. Amplitude variations of self potential signal is more delicate to be interpreted and need further development. 

How to cite: Deparis, J., Gaudot, I., Bretaudeau, F., Baltassat, J.-M., and Garnier, C.: Mapping sea cliff fracturation using passive seismic and self potential responses : case study of the Socoa flysh cliff (Basque Country, France), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5073, https://doi.org/10.5194/egusphere-egu23-5073, 2023.

14:45–14:55
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EGU23-2585
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SM5.2
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ECS
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Virtual presentation
Artur Marciniak, Mariusz Majdański, Sebastian Kowalczyk, Justyna Cader, Adam Nawrot, Bartosz Owoc, Iwona Stan-Kłeczek, Andrzej Górszczyk, Wojciech Gajek, Szymon Oryński, and Rafał Czarny

The problem of landslides is one of the greatest challenges in geohazard research. Due to their unpredictability, and complicated genesis, their detailed and accurate observation is necessary. Despite many studies on the subject, a general scheme for their recognition has still not been developed. An additional, and important fact that has recently been observed is the impact of the current state of the climate, and the human response to it. 

In the presented research results, an example where anthropogenic factors can have a significant impact on the evolution of a creeping landslide is described. As a result of changes in precipitation over years, artificial snowmaking is necessary to extend and even maintain the ski season on ski slopes and results in the unique characteristics of those landslides. In this presentation we shows the results of 4 years of geophysical observations, integrating multiple methods from geophysical imaging and remote sensing to determine the characteristics of the landslide, its changes and potential danger. The methods used, such as passive seismological monitoring, seismic tomography, electrical resistivity tomography, reflection imaging, terrestrial laser scanning and electromagnetic slingram in a time-lapse scheme allowed us to obtain an image of a temporally and spatially variable structure with remarkable accuracy. Additionally, there were also made an AMT profile with deep recognition range. The results obtained and their joint interpretation can serve as a reference in the study of similar landslide cases, where anthropogenic and climatic factors can significantly impact the evolution of such phenomena.

This research was funded by the National Science Centre, Poland (NCN), grant number 2020/37/N/ST10/01486.

How to cite: Marciniak, A., Majdański, M., Kowalczyk, S., Cader, J., Nawrot, A., Owoc, B., Stan-Kłeczek, I., Górszczyk, A., Gajek, W., Oryński, S., and Czarny, R.: Integrated imaging of a landslide as a result of 4 years of observations  – A case study from Outhern Carpathians, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2585, https://doi.org/10.5194/egusphere-egu23-2585, 2023.

14:55–15:05
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EGU23-8469
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SM5.2
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ECS
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On-site presentation
Fatima-Ezzahra Aallem, Anas Charbaoui, Laamrani Ahmed, Azzouz Kchikach, Mohammed Jaffal, and Younes Jnaoui

Experiments on a plot-scale of apparent electrical conductivity (ECa) and resistivity (ERT) variation with correlation of soil properties were studied for soil mapping at the experimental farm of Mohammed VI Polytechnic University of Benguerir (UM6P). The ElectroMagnetic Induction (EMI) technique was applied using a soil sensor EM38-MK2 which provides auxiliary ECa data sets with accuracy. The other method ERT was used to measure the electrical resistivity. The study was supported by soil sampling to ensure the reliability and potential of ECa measurements for soil mapping.  ECa readings in mS/ m ranged from 12 to 26 and 8 to 20 respectively in the vertical (ECa-V) and horizontal mod. ECa and ERT readings correlated best with soil properties such as texture (clay and sand), and upper soil chemical properties (OM, CEC, Ca2+, Fe2+and Mg2+). A modest correlation was found between ECa-V, clay and subsurface water content (r = 0.80), (r = 0.79). The linear relationship found between apparent electrical conductivity and soil clay content explained 80% of the measured variability. The results of the study raised the hope that soil mapping by ECa measurement can fairly represent the spatial variation of soil properties such as texture, chemical fertility and organic matter content. The use of spatial variability in EC as a co-variate in statistical analysis could be a complementary tool in the evaluation of experimental results.

How to cite: Aallem, F.-E., Charbaoui, A., Ahmed, L., Kchikach, A., Jaffal, M., and Jnaoui, Y.: Geophysical methods of soil fertility mapping for precision agriculture applications in semi-arid regions (Morocco), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8469, https://doi.org/10.5194/egusphere-egu23-8469, 2023.

Electromagnetic and ground-penetrating radar methods
15:05–15:25
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EGU23-7067
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SM5.2
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ECS
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solicited
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On-site presentation
Tim Klose, Julien Guillemoteau, Giulio Vignoli, Philipp Koyan, Judith Walter, Andreas Herrmann, and Jens Tronicke

In geophysical data inversion, one way to decrease the non-uniqueness of the solutions is to incorporate structural constraints. Such structural constraints are typically derived from collocated geophysical data, which are more sensitive to subsurface structures and parameter contrasts than the to-be-inverted data. When using a smooth regularization operator, a straightforward approach is to reduce the local weight of the smoothness constraints in model regions where we expect an interface. However, when using such an inversion approach, the capability to reconstruct a sharp interface relies only on the structural a priori information; i.e., model areas where no structural a priori information is available are solely controlled by the standard smoothness constraints. Therefore, this approach is not optimal in practice, as the structural a priori information is often not complete.

In this study, we evaluate a structurally-constrained inversion approach based on the Minimum Gradient Support (MGS) regularization, which is capable to promote sharp interfaces also in areas where no structural a priori information is explicitly specified. We test and evaluate this regularization approach for the inversion of frequency-domain electromagnetic induction (FD-EMI) data, where we use a constant-offset 3D GPR data set to derive structural a priori information. Our field data set covers an area of about 120 m x 50 m and has been collected at a field site in Kremmen, Germany, to explore peat deposits. Our results demonstrate that the proposed structurally-constrained inversion approach helps in finding a reliable subsurface structures (e.g., peat thickness) as well as a reliable reconstruction of the subsurface electrical conductivity distribution within the peat formation (e.g., related to varying degrees of peat decomposition) and within the sandy substratum.

How to cite: Klose, T., Guillemoteau, J., Vignoli, G., Koyan, P., Walter, J., Herrmann, A., and Tronicke, J.: Structurally-constrained FD-EMI data inversion using a Minimum Gradient Support (MGS) regularization, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7067, https://doi.org/10.5194/egusphere-egu23-7067, 2023.

15:25–15:35
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EGU23-917
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SM5.2
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ECS
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On-site presentation
Anna Platz, Ute Weckmann, and Cedric Patzer

The Radio-Magnetotelluric (RMT) method is a geophysical near-surface imaging technique with a broad range of possible applications. In 2020, the GFZ Potsdam has acquired a newly developed horizontal magnetic dipole transmitter that allows the application of the RMT method even in regions with an insufficient coverage of radio transmitters which normally serve as source signal. First controlled-source RMT measurements were conducted at three different locations in Chile in 2020. Further measurements were recently conducted in Ireland.  As we are able to store the raw time series, we have full control over the subsequent data processing. The processing tools at GFZ include the modular processing suite EMERALD, which was originally designed for MT processing, but has recently been adapted to process RMT data. One main difference is that in RMT the transmitter data is considered as signal, while in natural source MT this would be regarded as electromagnetic noise that needs to be removed using automated robust statistical approaches. However, processing the entire time series in an automated manner has a large drawback: The different emitted frequencies are transmitted in a sweep implying that only a smaller fraction of the time series contains the required signal for a particular target frequency and leading to an unfavourable signal-to-noise ratio. Since it is technically impossible to have the same time base for the data logger and the transmitter with an accuracy of a few nanoseconds, an automated detection scheme is required to find time segments that contain the transmitter signal. Usually, several Gigabytes of raw time series are collected during field measurements, making manual editing and supervision of the time series virtually impossible. However, a careful selection of appropriate time segments is essential for the success of the data processing. To address the challenge, machine learning algorithms have a high potential to solve both problems. Initial experience was gained with a recurrent neural network approach in order to identify suitable time segments (Patzer & Weckmann, EMTF 2021 – conference contribution and personal communication). However, many questions remained open, e.g. if other machine learning algorithms can result in better performances, which machine learning algorithms are in principle suitable for the characteristics and properties of RMT time series and which parameters should be used as input variables (features) for the algorithms. A large number of machine learning algorithms exist, which can be divided into different groups according to their operating principle and their activity fields. We will test unsupervised methods, especially for clustering the data, to identify a set of suitable input variables. Subsequently, we will use these features to train supervised algorithms as logistic regression, support vector machine and different kinds of neural networks to find the best performing algorithm. We will mainly use the RMT data from Chile within the training process. Furthermore, we will test if the trained algorithm is applicable to other new data sets measured at different locations (e.g. Ireland) and/or with different equipment.

How to cite: Platz, A., Weckmann, U., and Patzer, C.: Smart data selection – Using machine learning for an automated controlled-source Radio-Magnetotelluric data processing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-917, https://doi.org/10.5194/egusphere-egu23-917, 2023.

15:35–15:45
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EGU23-12015
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SM5.2
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ECS
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On-site presentation
Wouter Deleersnyder, David Dudal, and Thomas Hermans

In (time-domain) Electromagnetic Induction (EMI) surveys, an image of the electrical conductivity of the subsurface is obtained non-invasively. An accurate interpretation of the data is computationally expensive as it requires a full (high fidelity) 3D simulation of the induced electric currents embedded within an iterative and ill-posed inverse problem. Therefore, this forward model is usually approximated with a 1D forward model (low fidelity model) which only considers horizontal layers and for which fast analytical forward models exist. Recent work [1] has shown that a multidimensional forward model can be relevant in time-domain Airborne EM inversion. To be more precise, we provided an appraisal tool for quasi/pseudo-2D inversion to indicate that fast forward 3D modelling for time-domain (Airborne) EM data is still worthwhile and, in fact, necessary, in some areas. Surrogate modelling and machine learning may replace 3D forward modelling on a mesh during a 3D inversion.

In this contribution, we first demonstrate the initial steps towards creating an efficient surrogate model for 3D modelling with only 5000 samples in the training dataset. Rather than predicting the high-fidelity or 3D data directly, we predict the relative error between the high and low fidelity data. The idea behind this approach is that predicting the difference with a relatively good low-fidelity model is easier and more robust than trying to find a surrogate for the full data set. The computation of low fidelity data via the 1D approximation is no longer a computational burden, yet it explains most of the variability in the observed data. The residual variability, originating from the non-1D nature of the subsurface, is predicted with a Gaussian process regression model. Combining the low-fidelity model with a trained correction term via Machine Learning saves significant computation times. We show encouraging results, currently limited to two layers, where the trained surrogate model proves to produce a significant ‘learning gain’ in 92,5% of the cases (see Figure 1), meaning that it can significantly reduce the residual multidimensional variability. The cases where the surrogate model makes the prediction of the high-fidelity data worse, occur at the limits of the training data space, indicating that those cases could be resolved by generating more training data in those areas.

Figure 1 – The learning gain on the test dataset by using the trained surrogate model

 

References

[1] Deleersnyder, W., Dudal, D., & Hermans, T. (2022). Novel Airborne EM Image Appraisal Tool for Imperfect Forward Modeling. Remote Sensing14(22), 5757. https://doi.org/10.3390/rs14225757

How to cite: Deleersnyder, W., Dudal, D., and Hermans, T.: Machine learning assisted fast forward 3D modelling for time-domain electromagnetic induction data – lessons from a simplified case, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12015, https://doi.org/10.5194/egusphere-egu23-12015, 2023.

Coffee break
Chairpersons: Sonja Halina Wadas, Thomas Burschil, Frédéric Nguyen
16:15–16:25
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EGU23-12845
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SM5.2
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ECS
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On-site presentation
Johanna Klahold, Benjamin Schwarz, Alexander Bauer, and James Irving

Diffraction imaging has become an established tool in exploration seismology thanks to its potential to provide high-resolution information that is complementary to that contained in the corresponding reflected wavefield. In ground-penetrating radar (GPR) research, data processing schemes often neglect the diffracted wavefield, focusing instead on higher-amplitude reflected arrivals. However, these data typically contain a rich diffraction background due to the structural complexity of the near surface environment. Whereas the application of diffraction imaging to 2D GPR data has already been demonstrated, the potential of diffraction imaging for 3D GPR data is still underexplored.

Building on recent studies, we adapt a coherence-based diffraction imaging workflow, originally designed for seismic data, to common-offset GPR data. The first step of the proposed scheme is the separation of diffracted arrivals from the often predominant reflections, i.e. the faint diffracted portion of the data is separated and made accessible for dedicated processing. To this end, we approximate the reflected wavefield in a fully data-driven fashion by means of a coherent stacking scheme, and we subtract it from the data. The remaining diffracted wavefield can then be further enhanced through a second local coherent stacking procedure. Ultimately, wavefield focusing of the diffraction-only data yields an image of the distribution of subsurface scatterers.

The above-described analysis is applied to a range of 3D GPR data sets in an exploratory fashion. The localization of diffracting structures in these data sets provides valuable additional information about small-scale subsurface heterogeneities that can complement standard reflection analyses.

How to cite: Klahold, J., Schwarz, B., Bauer, A., and Irving, J.: Exploring the potential of 3D diffraction imaging for GPR data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12845, https://doi.org/10.5194/egusphere-egu23-12845, 2023.

16:25–16:35
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EGU23-9713
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SM5.2
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ECS
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On-site presentation
Alexander Bauer, Benjamin Schwarz, Jan Walda, and Dirk Gajewski

Within the last decade, the diffracted wavefield has gained increasing importance for the processing of both seismic and ground-penetrating radar (GPR) measurements. In both communities, the separation of the diffracted wavefield remains a notorious challenge that has been approached with different deterministic methods, ranging from poststack wavefront attributes to plane-wave destruction and coherent wavefield separation. While each of these deterministic methods has characteristic advantages and drawbacks, all of them require the adaptation of processing parameters for each application, particularly when crossing scales from seismic to GPR measurements. In this study, we propose to train a convolutional autoencoder to separate the reflected and diffracted wavefields in a generalized fashion. For this purpose, we have generated highly variable synthetic seismic data that contain reflections, diffractions and noise using an algorithm that allows to compute each component individually, resulting in an automatized generation of data and labels. In order to account for the complexity of field data, we complemented the synthetic data with a large set of reference seismic and GPR field data results from coherent wavefield separation, a deterministic method, in which the reflected wavefield is modeled and adaptively subtracted from the input data. With this dataset we trained a supervised convolutional autoencoder and applied the trained neural network to seismic and GPR field measurements that were not part of the training data. The results show that the trained autoencoder is able to generalize and successfully separate the reflected and diffracted wavefields even for complex field data, resulting in an on-the-fly diffraction separation that requires no choice of parameters and is likewise applicable to both seismic and GPR data.

 
 

 

 

How to cite: Bauer, A., Schwarz, B., Walda, J., and Gajewski, D.: Deep learning diffraction separation for seismic and GPR data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9713, https://doi.org/10.5194/egusphere-egu23-9713, 2023.

Seismic methods
16:35–16:45
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EGU23-313
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SM5.2
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ECS
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On-site presentation
Saurabh Sharma, Anand Joshi, Jyoti Singh, Mohit Pandey, Richa Rastogi, and Abhishek Srivastava

Numerical modelling has been proved as an incomparable tool to understand the structure of the earth and the processes beneath the earth’s surface. Finite difference method (FDM) plays a dominant role among various numerical methods for the purpose of seismic modelling and exploration. FDM provides a comprehensible solution to the partial difference equations defining the propagations of seismic wave. These partial differential equations consist of derivatives in time and space domain. FDM can be applied by defining the elastic wave-field and model parameters at every position on a discrete mesh. Reverse-time migration (RTM) is based on exploding reflector model and it is better than other migration techniques for the interpretation of various seismic models. The present work shows the forward modelling and reverse time migration of point diffractor body placed in dipping layer of vertically transverse isotropic (VTI) medium. A 12th order space and second order time differentiation RTM scheme have been used to interpret the location and extent of a point diffractor placed in dipping layer of VTI medium. The earth model under study is of the size 1400 m x 600 m. A dipping layer and a diffractor of size 18 m x 18 m has been placed in the VTI model. The FORTRAN code developed for FDM scheme of VTI model performs various requisite studies like stability criteria, numerical dispersion and the boundary conditions within the code. The output from the FDM code are the synthetic records at surface which after processing fed as an input in the FORTRAN code developed for RTM scheme. The position and extent of the diffractor placed in the dipping VTI medium layer has been detected properly using RTM scheme. Another FORTRAN code is developed in which forward and reverse wave propagation snapshots has been cross-correlated using various cross-correlation imaging conditions. A Laplace filter is then designed to efficiently resolve the position and extent of the diffractor in the dipping VTI medium layer.

How to cite: Sharma, S., Joshi, A., Singh, J., Pandey, M., Rastogi, R., and Srivastava, A.: Identification of Point Diffractor body placed in dipping Vertically Transverse Isotropic medium using Reverse Time Migration, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-313, https://doi.org/10.5194/egusphere-egu23-313, 2023.

16:45–16:55
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EGU23-879
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SM5.2
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ECS
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On-site presentation
Mohit Pandey, Anand Joshi, Saurabh Sharma, and Jyoti Singh

The Horizontal-to-Vertical Spectral Ratio (HVSR) method and Multi-Channel Analysis of Surface Waves (MASW) method are commonly used as a joint fit technique to retrieve the 1-D shear wave velocity. The Kumaon Himalaya consists of major thrusts like MFT, MBT, SAT, NAT and MCT, from South to North) and other tectonic features. These geological structures are observed in the form of lineaments on the surface. In the present study, 2-D section of shallow shear wave velocity structure has been estimated along the transect crossing South Almora Thrust (SAT) in the Kumaon Himalaya to study the variation of shear wave velocity across the thrust. In the present work, the ambient noise survey and Multi-Channel Analysis of Surface Wave (MASW) survey has been conducted along the road profile crossing the South Almora Thrust (SAT) at equally spaced stations of 3 Km. The 1-D shear wave velocity has been used to prepare the 2-D section of shear wave velocity. The lineaments in this division have been identified by the variation in the two dimensional shear wave velocity section prepared from the so obtained 1-D shear wave velocity in this profile. The study shows that there is a good correlation between variation of shear wave velocity in the region and major tectonic features of the area. The geological sections in this area has been compared with the obtained 2D structure which give a fair amount of idea about dip of SAT in this area.

How to cite: Pandey, M., Joshi, A., Sharma, S., and Singh, J.: Shear wave velocity variation across the South Almora Thrust, Kumaon Himalaya using Joint Inversion of Horizontal-to-Vertical Spectral Ratio (HVSR) and Dispersion curve, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-879, https://doi.org/10.5194/egusphere-egu23-879, 2023.

16:55–17:05
|
EGU23-11541
|
SM5.2
|
On-site presentation
Myriam Lajaunie, Joachim Rimpot, Dimitri Zigone, Céleste Broucke, Jean-Philippe Malet, Elise Weiskopf, Clément Hibert, Joshua Ducasse, and Catherine Bertrand

The versatility, cost-efficiency and easy deployment of seismic sensor nodes facilitate geophysical monitoring in environments that were previously inaccessible for instrumentation, and among them landslides and unstable slopes, most of the time located in remote mountains. Using nodes allows for the setup of dense arrays with sensor inter-trace distances that become compatible with the geometries and dimensions of the geological structures to image. This becomes particularly true for landslides which have complex 3D architectures (hummocky bedrocks, layering, multi-dimensional fractures, diverse geotechnical material, deep and perched aquifers and water circulations) and are shallow processes with respect to the classical investigation depths and sensitivity of most geophysical survey techniques.

Here we develop a specific processing workflow to allow the computation of 3D shear-velocity models with Ambient-Noise-based tomography applied to dense arrays of seismic stations. The workflow is applied to a dataset acquired at the Viella shallow landslide (France) developed in altered schists and moraine deposits. We deployed 70 IGU-16HR-3C-5Hz SmartSolo sensors (EOST/PISE service) with inter station distances of 70 m for a period of 25 days.

The processing consists in several steps, all of them being tuned to the specific case of shallow depths of investigation. In areas where only few strong (ML>4) earthquakes are triggered, with a low azimuthal distribution, surface-waves velocity fields are complex to estimate with earthquakes. Ambient noise cross-correlation tomography has the advantage of using the ambient noise to model the surface waves velocities by retrieving the interstation Green’s functions. The main hypothesis for retrieving the Green’s functions is a homogeneous noise-source distribution, which is never achieved in a natural environment. Therefore, data filtering and daily stacking are crucial to reduce the effect of non-uniform noise distributions and lead to consistent velocity models. Due to the noisy environment of Viella (torrential flows, farming activity, anthropogenic noise), several procedures were implemented to optimize the processing (reduction of the coherent noises in the processed data, use of a pseudo-topography to estimate as accurately as possible the inter-station distances and travel times). We then computed the dispersion curve diagrams for the surface waves on which we applied a strict selection to only keep the consistent part of the surface waves dispersion curves. The selection parameters were optimized for the Rayleigh and Love waves. Then, we inverted the inter-station travel times to compute group velocities maps at several frequencies. Finally, we proceed to a Markov-Chain-Monte-Carlo inversion of each of the dispersion curves extracted from the group velocity maps. We finally obtained a 3D shear velocity model, which is further combined with geological and borehole information in order to document the 3D structure.

The objectives are to present the processing workflow developed specifically for shallow imaging and the retrieval of 3D heterogeneities; effects of the processing parameters will be discussed on the Viella dataset. The approach developed for Viella is generic and has been further applied to other geological processes (permafrost at the Chauvet rock glacier, Marie-sur-Tinée mudslide), and the models will be discussed.

How to cite: Lajaunie, M., Rimpot, J., Zigone, D., Broucke, C., Malet, J.-P., Weiskopf, E., Hibert, C., Ducasse, J., and Bertrand, C.: Ambient noise shear-wave tomography for shallow landslide structural models retrieval from dense 3D seismological arrays., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11541, https://doi.org/10.5194/egusphere-egu23-11541, 2023.

17:05–17:15
|
EGU23-1953
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SM5.2
|
On-site presentation
Jun Hyeon Jo and Wansoo Ha

Seismic inversion methods performed by a deep neural network trained in a supervised learning manner have shown successful inversion performance in synthetic data examples that target small areas. These deep-learning-based seismic inversions use time-domain wavefields as input data and subsurface velocity models as output data. Since the time-domain wavefields include both traveltimes and amplitudes of seismograms, the size of the input data is considerably large. Therefore, studies that apply deep-learning-based seismic inversions trained on large amounts of field-scale data have not yet been conducted. In this study, to apply the deep-learning-based seismic inversion technique to field-scale data, the velocity models are predicted using only traveltimes of seismic waves as the input data instead of the full time-domain wavefields. If the traveltime information is used as input data, the resolution of the inversion result is diminished, but the data size is significantly decreased, which can reduce GPU memory usage and speed up network training. We call this approach deep-learning traveltime tomography. The results obtained from this method can also be used as initial velocity models for full-waveform inversion. For network training, a large number of field-scale synthetic velocity models and corresponding first-arrival traveltimes with towed-streamer acquisition are created, and then the network is trained with the synthetic dataset. As a result of performing deep-learning traveltime tomography on an example of synthetic velocity models simulating the seafloor strata, inversion results similar to the labels were obtained. Therefore, it was confirmed that the deep-learning traveltime tomography method can immediately predict a field-scale velocity model, unlike the existing deep-learning-based seismic inversion.

How to cite: Jo, J. H. and Ha, W.: Sesimic Traveltime Tomography Using Deep Learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1953, https://doi.org/10.5194/egusphere-egu23-1953, 2023.

17:15–17:25
|
EGU23-3223
|
SM5.2
|
On-site presentation
Feiyi Wang, Xiaodong Song, and Jiangtao Li

Joint inversion of surface-waves and receiver functions has been widely used to image Earth structures to reduce the ambiguity of inversion results. We propose a deep learning method (DL) based on multi-label Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) with a spatial attention module, named SrfNet, for deriving the Vs models from Rayleigh-wave phase and group velocity dispersions and receiver functions (RFs). We use a spline-based parameterization to generate velocity models instead of directly using the existing models from real data to build the training dataset, which improves the generalization of the method. Unlike the traditional methods, which usually set a fixed Vp/Vs ratio, our new method takes advantage of the powerful data mining ability of CNN to simultaneously constrain the Vp model. A loss function is specially designed that focuses on key features of the model space (such as the Moho and the surface sedimentary layer). Tests using synthetic data demonstrate that our proposed method is accurate and fast. Application to southeast of Tibet shows a consistent result and comparable misfits to observation data with the previous study, indicating the proposed method is reliable and robust.

How to cite: Wang, F., Song, X., and Li, J.: Joint inversion of Surface-wave Dispersions and Receiver Functions based on Deep Learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3223, https://doi.org/10.5194/egusphere-egu23-3223, 2023.

17:25–17:35
|
EGU23-4987
|
SM5.2
|
ECS
|
On-site presentation
Amin Kahrizi, Maximilien Lehujeur, Odile Abraham, Antoine Lescoat, Loic Michel, Thomas Bardainne, Lilas Vivin, Christopher Boulay, Julien Blanchais, Thibaud Devie, Sérgio Palma Lopes, Olivier Durand, and Gautier Gugole

The use of ambient noise for passive seismic imaging has evolved into a cutting-edge, low-cost, and environmentally acceptable method of exploring the subsurface. This technique dispenses with active seismic sources, alternatively uses ambient seismic noise. Theoretical investigations have approved that an estimate of the empirical Green’s function between receivers could be obtained from the cross-correlation of ambient noise and/or dispersed coda waves. This Green’s function is mostly made up of fundamental Rayleigh waves, propagating between two receivers as if they would be caused at one of them. The applications of ambient noise surface wave tomography, from engineering and urban developments to regional and continental scales, have led to the mapping of the area's velocity model, which chiefly corresponds to the structural/geological units.

Because of numerous devastating catastrophes in recent years, several countries have made flood protection a priority. However, sea-dikes are considered remarkably heterogeneous and may fail due to their construction and/or reinforcing structures; they are potentially subject to stress by sea waves during the tidal cycle and seasonal heat variations, resulting in the water infiltration. Internal abnormalities cannot be recognised in the early stages of erosion, although visual assessments may often be relied on. In this study, we outline a passive seismic survey that was carried out to investigate technical and methodological aspects of passive seismic methods along with their application in a sea dike monitoring perspective. 

The SEEWALL project is a collaborative project, seeking to create innovative methodology to monitor the temporal evolution of sea dikes and detect early deterioration. We deployed 160 permanent 3-component MEMS accelerometers spaced 2 meters apart on top of a dike on the island of Noirmoutier (France), which was exhibiting moderate water infiltrations at its base. Despite the inhomogeneous distribution of the ambient noise sources, exploitable empirical Green's functions can be retrieved mostly from the cross-correlation of vertical component data. We estimate the surface wave phase velocity dispersion curves  using a time-frequency analysis; strictly speaking, after preconditioning the data, the cross-correlation is carried out in the frequency domain by carefully windowing data, from which each  empirical Green's functions is derived; their cross-correlations are stacked linearly by hours. The arrival times of the causal and anti-causal parts are often not fully symmetrical, indicating the diversity of major noise sources. The phase velocities measured on both positive and negative lag-times, as a function of the frequency, are computed using the phase-shift method. Interpretation of the phase velocity dispersion curves is challenging due to the geometry of the dike at the scale of the intended wavelength (a few tens to hundreds meters). But the pattern of the dispersion data appears to be relatively stable over time. It is also consistent with the dispersion curves we have obtained using active seismic hammer-shots, performed along the structure. For monitoring, we suggest using F-K spectra to highlight the variety of energy density over time, in order to advance a deeper understanding of data analysis; this enables us to discover any changes that might not be otherwise obvious.

How to cite: Kahrizi, A., Lehujeur, M., Abraham, O., Lescoat, A., Michel, L., Bardainne, T., Vivin, L., Boulay, C., Blanchais, J., Devie, T., Palma Lopes, S., Durand, O., and Gugole, G.: Ambient seismic noise processing to monitor sea dikes: the case of Noirmoutier, France, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4987, https://doi.org/10.5194/egusphere-egu23-4987, 2023.

17:35–17:45
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EGU23-8304
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SM5.2
|
ECS
|
On-site presentation
gheith alfakri

Gheith Alfakri Emmanouil Parastatidis1  Stella Pytharouli1

Abstract: Previous studies present evidence that microseismic monitoring could be a favourable potential technology for brownfield land site investigations, e.g. in the identification of buried objects in the shallow subsurface (< 3 m). More specifically, the presence of buried objects change the characteristics (amplitude and frequency) of a mechanical wave that propagates through a medium where this object lies. These changes have to-date only been observed at recordings from stations that are located directly above the buried object. To investigate whether a buried object can be ‘seen’ by more sensors located in the vicinity above the object, we carry out a series of numerical simulations. We examine the propagation of a sine wave emitted by a point source on the surface of a medium and study the frequency, amplitude and emitted energy from that sine wave and how these are affected by local changes in the mechanical properties of the model. For the duration of each simulation, we record the velocity history at a number of points on the free surface of the model. Numerical simulations are carried out in FLAC3D. First, we look on how the distance between the source and the monitoring points changes what we record. We examine two cases : In Case A, the monitoring stations and the buried object are at a distance less than 30 meters from the seismic wave source. In Case B, the monitoring stations and buried object are at a distance more than 30 meters from the seismic wave source. We apply spectral analysis to the resultant seismic velocity time histories as recorded at a number of monitoring stations at the free surface of the model. Our results for Case A show that an object can be detected at a monitoring station located directly above the object to a depth of 1-2 meters. Results for Case B show that an object can be detected at the monitoring station that is deployed directly above the object to a depth of up to 4-5 meters, and it can also be detected at neighbouring stations, at distances approximately equal to the depth of the object. In addition, we study factors having an impact on the amount of energy of the seismic wave emitted, i.e. depth of the object from the surface and its mechanical properties. Our analysis indicates that by increasing the depth of the object, the amount of reflected seismic energy decreases. The changes in the mechanical properties of the materials lead to a change in seismic wave propagation velocity and frequency. Results from our numerical simulations present evidence that microseismics can be used as a complementary, low-cost site investigation tool for applications where very shallow depths are of particular interest such as those at brownfield sites. This can have significant implications on the way site investigations on brownfield sites are carried out, with microseismics providing an alternative to sites where traditional non-intrusive methods such as GPR and/or resistivity tomography are limited due to ground properties.

 

How to cite: alfakri, G.: Microseismic monitoring of wave propagation through heterogeneous media: a tool for brownfield site investigation?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8304, https://doi.org/10.5194/egusphere-egu23-8304, 2023.

17:45–18:00

Posters on site: Mon, 24 Apr, 08:30–10:15 | Hall X2

Chairpersons: James Irving, Ellen Van De Vijver, Sonja Halina Wadas
X2.51
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EGU23-14455
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SM5.2
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ECS
ROBOMINERS multi-electrode arrays: a comparative test.  
(withdrawn)
Giorgia Stasi, Christian Burlet, Yves Vanbrabant, and Frederic Nguyen
X2.52
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EGU23-12280
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SM5.2
Yannick Forth, Anne-Sophie Mreyen, Andreas Kemna, Joost Hase, Florian Wellman, Nils Chudalla, and Frédéric Nguyen

The E-TEST project (Einstein Telescope EMR Site & Technology) investigates the feasibility of constructing a large Laser Interferometer (Einstein Telescope) in the Euregio Rhine Meuse. The aim of this instrument is to detect gravitational waves. To reach a sufficient noise attenuation the telescope will be built deep underground (more than 200 meters depth). The infrastructure consists of multiple tunnels and caverns spanning several kilometers. As with any large-scale infrastructure, the geological model, especially the existence and orientation of faults, is of large importance for hydrogeophysical and geotechnical characterization. At such depths, few near-surface geophysical methods are able to provide information with enough details. The application of large 3D Deep ERT surveys helps understanding the local geological settings and to identify important geological features but suffers from ambiguous interpretation. However, such imaging requires measuring dipoles independent of the injection system (such as the Fullwaver System by IRIS Instruments) in contrast to conventional ERT Systems, and due to the large covered area, coarsely spaced. This results in a drastic decrease in resolution when compared to classical ERT measurements.

Here, we present a sensitivity analysis on a dataset based on the geologic setting in Plombières, Belgium to identify the impact of geology and survey setup on Deep 3D ERT surveys. The utilized geologic model was created with the Open-Source 3D structural geomodelling software GemPy and used as an input for forward modelling using the Open-Source modelling and inversion library pyGIMLi.

How to cite: Forth, Y., Mreyen, A.-S., Kemna, A., Hase, J., Wellman, F., Chudalla, N., and Nguyen, F.: Sensitivity analysis on synthetic 3D Deep ERT data for the example of Plombières, Belgium, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12280, https://doi.org/10.5194/egusphere-egu23-12280, 2023.

X2.53
|
EGU23-12063
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SM5.2
|
ECS
Nathalie Roser, Anna Hettegger, Teresa Müller, Matthias Steiner, Lukas Aigner, Arno Cimadom, and Adrian Flores Orozco

The Lake Neusiedl-Seewinkel Basin is home to a system of unique soda lakes, which harbor a variety of rare and endangered flora and fauna. The existence of these lakes is facilitated by a complex equilibrium between climate and surface-groundwater interactions, where capillary forces pull soda (NaHCO3) and clay from the shallow aquifer to the surface. The accumulation of clay develops an impermeable layer that acts as a hydraulic barrier near the surface allowing rain water to form the eponymous lakes. Assessing lateral and temporal variations in porosity, clay and salt content, in particular within this impermeable layer, is important to understand the surface-groundwater dynamics at site and address the impact of climate change and artificial drainage of groundwater on the lakes leading to their on-going degradation. We investigate here the applicability of seismic methods to quantify near surface variations in porosity, while information on clay content and salinity are resolved through electric methods. In particular, we conduct measurements with the multichannel analysis of surface waves (MASW), the P-wave seismic refraction tomography (SRT) and the induced polarization (IP) methods during dry (summer) and wet (winter) periods in three adjacent soda lakes: one considered active, one degrading, one degraded. Quantitative estimates of porosity and water saturation are inverted from MASW and P-wave SRT data sets based on the Biot-Gassmann fluid substitution theory and an extension of the Hertz-Mindlin contact theory accounting for capillary suction effects taking place in the vadose zone. The complementary IP measurements aid in the identification of the salt-bearing clay rich impermeable layer, associated with higher electrical conductivity values, to sustain the porosity estimation based on the seismic methods and gain information on the clay content and pore fluid salinity at each site. In August 2022, we installed a permanent IP monitoring profile within the active lake to observe temporal changes in electrical conductivity related to variations in soil moisture due to seasonal variations such as precipitation. Our results reveal different geophysical signatures in the three lakes corresponding to their presumed ecological state of degradation. In general, we observe higher P- and S-wave velocity values and lower Poisson’s ratio and electrical conductivity values for the degrading/degraded lake than in case of the intact lake. The impermeable layer of the intact lake is clearly distinguishable from MASW and IP images, whereas it is less well resolved and exhibits a higher porosity in the degraded lake. The joint inversion of SRT and MASW overall improves the subsurface characterization as it solves for shallow porosity variations within the impermeable layer, which were not detectable through the independent inversions, clearly revealing differences in porosity between the three sites.

How to cite: Roser, N., Hettegger, A., Müller, T., Steiner, M., Aigner, L., Cimadom, A., and Flores Orozco, A.: Geophysical quantification of porosity in the soda lakes of the Lake Neusiedl-Seewinkel Basin, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12063, https://doi.org/10.5194/egusphere-egu23-12063, 2023.

X2.54
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EGU23-3185
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SM5.2
|
ECS
Nino Menzel, Norbert Klitzsch, Michael Altenbockum, Lisa Müller, and Florian Michael Wagner

As part of the Lower Rhein Embayment (LRE), the Southern Erft block is characterized by a complex tectonic setting that may influence hydrological and geological conditions on a local as well as regional level. The area presented in this study is located near Euskirchen in the south of North Rhine-Westphalia and traversed by several NW-SE-oriented fault structures. Past studies based on the lithological description of borehole cores and hydrological measurements stated that the present faults affect the local groundwater conditions throughout the targeted area. However, since the tectonic structures were located based on a sparse foundation of geological borehole data, the results include considerable uncertainties. Therefore, it was decided to re-evaluate and refine the assumed fault locations by conducting geophysical measurements.

Seismic Refraction Tomography (SRT) as well as Electrical Resistivity Tomography (ERT) was performed along seven measurement profiles with a length of up to 1.1 km. To allow a sufficient degree of model resolution, the electrode spacing was set to 5 m and halved for areas proximate to assumed fault locations. The geophone spacing was set to 2.5 m for all conducted seismic surveys. A large portion of data processing and inversion was performed with the open-source software package pyGIMLi (Rücker et al., 2017). In addition to compiling individual resistivity and velocity models for all deduced measurements, both ERT and SRT datasets were jointly inverted using the Structurally Coupled Cooperative Inversion (SCCI). This algorithm strengthens structural similarities between velocity and resistivity by adapting the individual regularizations after each model iteration.

This study emphasizes the benefit of multi-method geophysics to detect small-scale tectonic features. The surveys allowed to identify the fault locations throughout the area of interest, provided that the vertical displacements are large enough to be detected by the measurements. Previously assumed locations of the tectonic structures diverge from the new evidence based on ERT and SRT surveys. Especially in the western and eastern parts of the research area, differences between the survey results and formerly assumed locations are in the order of 100 m. Seismic and geoelectric measurements further indicate a fault structure in the southern part of the area, which remained undetected by past studies. The joint inversion provides minor improvements of the geophysical models, as most of the individually inverted datasets already provide results of good quality and resolution. Therefore, the effect of the SCCI algorithm is limited to underlining lithological and hydrological boundaries that are already present in the individually inverted ERT- and SRT-models.

 

References

Rücker, C., Günther, T., Wagner, F.M. (2017). pyGIMLi: An open-source library for modelling and inversion in geophysics, Computers and Geosciences, 109, 106-123, doi: 10.1016/j.cageo.2017.07.011.

How to cite: Menzel, N., Klitzsch, N., Altenbockum, M., Müller, L., and Wagner, F. M.: Prospection of faults in the Southern Erftscholle with Refraction Seismics and Electrical Resistivity Tomography, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3185, https://doi.org/10.5194/egusphere-egu23-3185, 2023.

X2.55
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EGU23-2183
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SM5.2
|
ECS
Wansoo Ha, Jun Hyeon Jo, and Lydie Uwibambe

We attenuated random noise in Laplace-domain seismic wavefields using a modified U-net. Laplace-domain wavefields can be obtained by Laplace-transforming time-domain wavefields. Due to the damping in the Laplace transform, small-amplitude noises near the first arrival signal can severely contaminate Laplace-domain wavefields. Therefore, time-domain denoising is not sufficient for seismic data processing in the Laplace domain. We trained a modified U-net in a supervised manner to generate clean wavefields from noisy wavefields. Since Laplace-domain wavefields show exponential decay with respect to offset, we used the logarithmic representation of the wavefields to train the network. Numerical examples show that the deep-learning approach can attenuate random noise better than denoising using singular value decomposition.

How to cite: Ha, W., Jo, J. H., and Uwibambe, L.: Seismic random noise attenuation in the Laplace domain using deep learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2183, https://doi.org/10.5194/egusphere-egu23-2183, 2023.

X2.56
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EGU23-2475
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SM5.2
Woohyun Son, Byoung-Yeop Kim, and Dong‑Geun Yoo

In this study, seismic data were acquired using Tamhae2 R/V to identify the subsurface fault structures in the Hupo basin. The seismic data were generated by the air-gun source (30 cu. in.). The source distance is 12.5 m, and the receiver distance is 6.25 m. The number of channels is 32. The offset range of the seismic data is 50 to 250 m. The data processing for short-offset seismic data is mainly applied with simple processing techniques such as frequency filter, trace editing, and velocity analysis in consideration of cost efficiency. However, these simple data processing techniques cannot accurately image complex subsurface structures because it is difficult to remove severe noise and water-bottom (WB) multiples effectively. Therefore, in order to accurately identify the geological structures, it is necessary to apply high-resolution signal processing techniques that can remove severe random noise and WB multiples included in raw seismic data. Severe noise was removed by applying data processing techniques such as a low-cut filter, trace editing, swell noise attenuation, and random noise attenuation. In addition, predictive deconvolution, SRME, and Radon filter were applied to effectively attenuate WB multiples that cause difficulties in geological interpretation. Finally, pre-stack Kirchhoff time migration was applied to more accurately image the subsurface structures. From the data processing results, we confirmed that the high-resolution signal processing techniques applied in this study greatly improved the signal-to-noise ratio of seismic data and effectively eliminated WB multiples.

How to cite: Son, W., Kim, B.-Y., and Yoo, D.: Application of the signal processing to a short-offset seismic data in the Hupo basin, offshore Korea, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2475, https://doi.org/10.5194/egusphere-egu23-2475, 2023.

X2.57
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EGU23-5390
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SM5.2
Seonghyung Jang, Donghoon Lee, and Byoung-Yeop Kim

After CO2 injection into a reservoir, the behavior of CO2 depends on permeability, porosity, cap rock, reservoir fluids, CO2 characteristics, pressure gradient, and buoyancy effects. Therefore, the thickness of the reservoir is an essential parameter for CO2 monitoring. In the case of reservoir thickness prediction, it is practical to consider a geological reservoir as a transition zone in which the physical properties linearly change. In the transition zone, the seismic reflections in the stack section are the normal incident reflection coefficient with continuously changing velocity. Since this is composed of a function of the velocity ratio of the upper and lower layers, frequency, and transition zone thickness, the seismic signals apply to predict the thickness of the reservoir layer. In this study, we use the frequency characteristics with time-varying to estimate the thickness of the transition zone. First, we prepare the time-frequency spectrum with various thicknesses and then analyze it through deep learning to determine an optimum reservoir thickness. We use a convolution neural network (CNN) for predicting the transition zone thickness, which has two more hidden layers in the feature extractions. Unlike the fully connected layer, CNN is composed of a convolutional layer and a pooling layer and requires many data to prevent overfitting. Since CNN can efficiently process nonlinear data, it is applied to image classification and argumentation. For the numerical modeling experiment, we prepared a geological model in which the velocity of the shale layer (3000 m/s), cap rock, is greater than the lower sandstone layer (2200 m/s). We verify variation of phase and amplitude according to various transition zone thicknesses. For example, when the thickness is 10 m, it shows the phase changes at 65 Hz, and the amplitude decrease with increasing frequency. For the thickness of 50 m, the phase changes at the cut-off frequency of 13 Hz, and the amplitudes decrease until 25 Hz, increasing and decreasing repeatedly. We suggest that CNN is one of the methods to predict the thicknesses of CO2- injected reservoir using a time-frequency spectrum with various thicknesses.

How to cite: Jang, S., Lee, D., and Kim, B.-Y.: Thickness estimation of CO2 transition layer using a deep learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5390, https://doi.org/10.5194/egusphere-egu23-5390, 2023.

X2.58
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EGU23-8048
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SM5.2
|
ECS
Giuseppe Provenzano, Stéphane Garambois, Jean Virieux, Romain Brossier, and Ludovic Métivier

Harmaliére, in southern France, is among the most active alpine landslides, posing a risk to the neighbouring settlements and infrastructures. A retrogressive slide in March 1981, that displaced a volume in the order of 100 m 3 , was followed in April 2016 by a landslide two order of magnitude larger, followed by minor reactivations in 2017 and 2018. Local bedrock paleo-topography and sedimentary structures within the glacio-lacustrine sediment layer are suspected to have a role in determining the dynamics of this slide, characterized by episodic large displacements as opposed to slow and continuous mass movements registered in neighbouring sites (e.g. Avignonet). However, current state of knowledge of the subsurface is limited to low-resolution volumes and local 1D layered S-wave profiles.

Within the RESOLVE project, in May-June 2021 a dense 3D array of 100 three-component geophones has been deployed to record continuously ambient seismic noise for a one-month period. This was complemented by the acquisition of an active dataset using 100 hammer-strike sources, with offsets ranging from 0 to 900 metres. The vertical component of the the active dataset has been used to obtain a 3D P-wave velocity model by first-arrival traveltime tomography. Particularly challenging field conditions, e.g. thick vegetation and surface water, along with the low-power of the hammer sources, required dedicated processing to enhance the signal-to-noise ratio and allow for confident first-arrival pickings.

Super-virtual interferometry (SVI) has been applied to improve the quality of offsets larger than 400 m, which contain head-waves key for the imaging of the sediment-bedrock interface. SVI enhances critically refracted arrivals by stacking the cross-correlations of traces pairs sharing a stationary-path in common-receiver gathers, and then convolving the resulting station-pair Green’s functions with the appropriate virtual sources in common-source gathers. An azimuth-varying approach has been developed to adapt SVI to the 3D problem, reducing the number of cross-correlations and mitigating artefacts resulting from non-stationary paths contributions. The dataset obtained by constrained automatic picking on the SVI dataset has been used for first-arrival traveltime tomography, yielding an improved-quality tomographic volume at depths larger than 50 m along with lower final data misfit, thanks to the greater number of reliable long-offsets picks compared to the pre-SVI dataset.

The P-wave velocities obtained within the sediment body, as well as the inferred bedrock topography, are sensible and appear to be consistent with independent geophysical data. In order to complete the elastic characterization of the site, a S-wave 3D model will be reconstructed from the empirical Green's functions obtained by interferometry on 1-month long noise recordings, opening the way towards a joint passive-active high-resolution 3D elastic remote characterization of the landslide volume, and thus an improved understanding of its controlling factors.

How to cite: Provenzano, G., Garambois, S., Virieux, J., Brossier, R., and Métivier, L.: 3D seismic tomography of the Harmaliére landslide (French Alps) by interferometry, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8048, https://doi.org/10.5194/egusphere-egu23-8048, 2023.

X2.59
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EGU23-6655
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SM5.2
ahmed saadi, Abdelouahab Issaadi, Fethi Semmane, Abdelkrim Yelles–Chaouche, Juan José Galiana-Merino, Khalissa Layadi, and Redouane Chimouni

Abstract

The city of Oran, which is located in the northwest of Algeria, in the Lower-Cheliff basin, has experienced several earthquakes in the past. Therefore, the characterization of its subsurface is crucial for a better assessment of the seismic hazard. Single-station ambient vibration measurements at 193 sites and array measurements at 15 sites have been analyzed with HVSR and F-K techniques, respectively, for the soil investigation.The HVSR curves showed a variation of the fundamental frequency peak between 0.3 and 7.4 Hz, increasing from east to west, and reaching a maximum amplitude of ~6. Rayleigh wave dispersion curves were obtained by F-K analysis. Joint-inversion of the dispersion and HVSR curves provided a shear wave velocity model and an estimate of the bedrock depth. The models showed 3 layers of sediments overlying the bedrock. The shear-wave velocity (Vs) of the softer sediments varies between 280 and 580 m/s, and at bedrock it varies between 1600 and 2500 m/s. The latter reached a maximum depth of 1050 m northeast of the city. In addition, these results were used to calculate the soil vulnerability factor (Kg), and the Vs30 in the entire area. Finally, a soil classification and a regression law between the fundamental frequency and the depth were proposed for the whole city.

How to cite: saadi, A., Issaadi, A., Semmane, F., Yelles–Chaouche, A., Galiana-Merino, J. J., Layadi, K., and Chimouni, R.: Vs model for Oran city, obtained by joint-inversion of dispersion and HVSR curves, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6655, https://doi.org/10.5194/egusphere-egu23-6655, 2023.

X2.60
|
EGU23-8768
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SM5.2
Maria Giulia Di Giuseppe, Sabatino Ciarcia, Claudio De Paola, Carmela Fabozzi, Roberto Isaia, Antonio Troiano, and Stefano Vitale

The southern Apennines are a fold-and-thrust belt characterized by the superpositions of different thrust sheets. The orogenic construction defined by thin- and thick-skinned tectonics occurred from the Paleocene to the early Pleistocene. In this orogen, evidence of nonvolcanic degassing is widely reported. The orogenic chain hosts the Mefite d'Ansanto (MdA) vent, the most significant nonvolcanic natural emission of low-temperature CO2 on Earth. Other degassing areas are located in the Sele River Valley, where several vents are aligned along major faults, including the thermal springs of Contursi and Oliveto Citra (COC).

Different investigations on these nonvolcanic emissive structures mark a close relation between degassing phenomena and tectonics, evidencing a likely dominant crustal gas origin for the COC vent. In any case, poor information is available about the characteristics of the CO2 reservoir (including the geometry and depth) and the fluid's rising pathways.

Different surveys have been performed, applying a multidisciplinary approach, including innovative methodologies, aiming to reconstruct the geometry of the shallow degassing pathways and investigate how the different geological and tectonic architecture influences the CO2 seeping and surficial degassing processes. The structures that convey and favour the upward gas migration, seeping and degassing have been imaged using geophysical and structural investigations.

Electrical Resistivity (ERT) and Induced Polarization (IP) tomographies, combined with Self-Potential (SP), Magnetic (Mag), and PH mapping have been performed in correspondence with the most degassing part of the COC area. The joint acquisition of such a multiparametric dataset ended in a better-constrained interpretation of the different detected anomalies. Furthermore, the obtained results allowed us to construct different geophysical maps and geological cross-sections of the investigated area and develop a model of the degassing vents area, highlighting the role of reconstructed lithological and structural settings in the shallow leaking processes.

How to cite: Di Giuseppe, M. G., Ciarcia, S., De Paola, C., Fabozzi, C., Isaia, R., Troiano, A., and Vitale, S.: Multidisciplinary approach to reconstruct the pathways of the CO2 nonvolcaning degassing in the thermal springs of Contursi and Oliveto Citra sector (southern Appennines, Italy), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8768, https://doi.org/10.5194/egusphere-egu23-8768, 2023.

X2.61
|
EGU23-9172
|
SM5.2
Imaging Oceanic Sedimentary Basins Using Machine Learning
(withdrawn)
Eoghan Totten, Chris Bean, and Gareth O'Brien
X2.62
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EGU23-9203
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SM5.2
|
ECS
Seismic events relocation and velocity models for the Atacama seismic gap at Central-Northern Chile (24.5-29°S)
(withdrawn)
Nicolás Hernádez-Soto, Matthew Miller, Diego González-Vidal, Marcos Moreno, and Dietrich Lange
X2.63
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EGU23-12582
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SM5.2
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ECS
Silvia Scolaro, Paolo Pino, Antonino Torre, Sebastiano D'Amico, Giancarlo Neri, and Debora Presti

Extensive ambient noise measurements have been carried out in the historical centre of Messina (Sicily, South Italy) and the related HVSR results showed a clear variation of the fundamental peak frequency in the range between 0.4 Hz and 1.6 Hz. This frequency variation is detected across a NW-SE segment, and it can be imputed to a strong lateral heterogeneity of the sediment cover going from southwest to northeast of the study area. Moreover, we carried out a detailed geological field survey and analysis of land surface morphology based on topographic maps and DTM data that allowed us to detect the NW-trending fault, never documented in literature, crossing the historical centre of Messina. Geologic observations indicate clearly normal faulting and activity of this fault is documented at least until Middle Pleistocene, with likely prosecution during Upper Pleistocene.

The detected NW-trending fault is roughly perpendicular to the strike of the main structural system of the Straits of Messina framework to which the major earthquake of 1908 (M 7.1) is imputed. Therefore, deeper future investigations for appropriate framing into the local geodynamic context and for evaluation of its eventual prosecution in the offshore area are necessary.

In this preliminary study we identify structural discontinuities and faults which may represent new sources of hazard in a town exposed to very high seismic risk in Italy.

How to cite: Scolaro, S., Pino, P., Torre, A., D'Amico, S., Neri, G., and Presti, D.: Geophysical and geological evidence of a previously undetected NW-trending fault crossing the historical centre of Messina (Sicily, south Italy), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12582, https://doi.org/10.5194/egusphere-egu23-12582, 2023.

X2.64
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EGU23-12610
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SM5.2
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Ki Baek Kwon, Byeong Seok Ahn, Dabeen Heo, June Baek, Suhee Park, and Tae-Seob Kang

The Heunghae area of the Cenozoic Pohang Basin, located in the southeastern part of the Korean Peninsula, is a small-scale sub-basin covered with alluvium. The Jurassic granite is overlaid by the Cretaceous sedimentary and volcanic rocks, which form the basement of the basin composed of the Miocene non-marine and marine sediments. Therefore, the vertical distribution of strata in the Heunghae Basin can be summarized as a sequence of Quaternary alluvium, Tertiary and Cretaceous sedimentary layers, and Jurassic granite. Depending on each layer's formation time, a distinct difference in the physical properties of each layer may occur, which mechanically results in the contrast of acoustic impedance of elastic wave energy. The resonant frequency measured from the horizontal-to-vertical spectral ratio (HVSR) curve of microtremor records at a seismograph station is known to be an effective value for determining the depth to the basement with strong contrast in acoustic impedance. Based on the assumption that the boundaries formed by each layer in the Heunghae Basin have a distinct difference in acoustic impedance, we tried to estimate the resonant frequencies corresponding to each boundary from the HVSR. A total of 114 three-component geophones with a natural frequency of 5 Hz were evenly installed to obtain microtremor records over the Heunghae Basin. The distance between geophones is approximately 500 meters. The installation period is from September 24 to November 24, 2021, and the recording time varies from a minimum of 2 hours to a maximum of about 26 hours, depending on the measurement site. The recording was made at a sampling rate of 500 samples per second. The HVSR analysis used two-hour long recordings for all sites. One or more peaks can be identified in the HVSR curve of most sites. Since the resonant frequency that can be confirmed through the HVSR curve is related to the depth of the boundary between the layers where strong impedance contrast occurs under each geophone, the boundary at various depths can be determined from these frequencies of peaks. The range of resonant frequencies was found to be approximately 0.3 – 26 Hz. To compare the resonant frequency with the known geological information, the HVSR curve near the borehole site was compared with the geological logging information. In the case of some measurement sites, it was difficult to specify other peaks because one resonance frequency peak was dominant over the HVSR curve. Multiple resonant frequencies can be assumed to correspond to major layer interfaces. Due to the uncertainty of the velocity structure model, it was difficult to accurately determine the depth to the interface from these resonant frequencies. Nevertheless, the results show that the multiple resonant frequencies of the HVSR curve indicates the layer boundaries with a strong impedance contrast, and thus it can contribute to reveal the sequence stratigraphy of a basin with multiple episodes of deposits.

How to cite: Kwon, K. B., Ahn, B. S., Heo, D., Baek, J., Park, S., and Kang, T.-S.: Stratigraphic characterization of the Heunghae Basin, Korea, using horizontal-to-vertical spectral ratio of microtremor records, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12610, https://doi.org/10.5194/egusphere-egu23-12610, 2023.

X2.65
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EGU23-13697
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SM5.2
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ECS
Sonja Halina Wadas and David Colin Tanner

Neotectonic movements can cause severe hazards and are scientifically and socially relevant, e.g. for seismic hazard assessment, and utilisation of the subsurface. In northern Germany, a presumed aseismic region, little is known about these processes and the associated structures, despite proven neotectonic activity, because many faults are hidden beneath sediments. To improve the knowledge of neotectonic activity, investigations of recently-active fault zones, like the Osning Lineament (OL) in North Rhine-Westphalia, are required.

To better understand the neotectonic evolution of the OL, we use near-surface geophysics, which have not been used at the OL so far. We used a combined approach using high-resolution 2D P- and SH-wave reflection seismics. P-wave seismic alone can often not properly image near-surface impressions of faults due to poor shallow resolution, but this gap can be closed using SH-wave reflection seismics, which offers very high resolution, even at shallow depth. Three P-wave profiles were measured with a hydraulically-driven vibrator vehicle (sweep frequency: 20 to 200 Hz) with a source point spacing of 10 m and plugged vertical geophones at 5 m intervals. Additionally, four SH-wave profiles were surveyed using an electro-dynamic micro-vibrator (sweep frequency: 20 to 160 Hz) with a source point spacing of 2 or 4 m and a landstreamer with horizontal geophones at 1 m intervals.

The seismic profiles show good results with respect to mapping the fault inventory. In the migrated depth sections of the P-wave profiles, several northward-dipping faults in the Cretaceous formations are recognizable, which are interpreted hitherto unknown extensions of the OL. The Quaternary, with a maximum thickness of 20 to 30 m, is only poorly imaged by the P-wave profiles, but there are nevertheless hints that the faults also extend into the Quaternary. The SH-wave profiles support this assumption, due to their higher resolution close to the surface, because of very-low wave velocities between 150 and 500 m/s. In the Quaternary sediments, further faulting and deformation features are recognizable, enabling a more comprehensive interpretation and understanding of the local fault geometry.

In the course of the project, we also carry out a full waveform inversion of the P- and S-wave data to improve the fault imaging. This will be accompanied by testing of different migration methods and seismic attribute analysis.

How to cite: Wadas, S. H. and Tanner, D. C.: P- and SH-wave reflection seismics of the reactivated intraplate Osning Thrust in northern Germany, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13697, https://doi.org/10.5194/egusphere-egu23-13697, 2023.

X2.66
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EGU23-15261
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SM5.2
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ECS
Chun Fei Chey, Tianjue Li, and Ping Tong

Long Valley Caldera is a depression located in eastern California, which is the Earth’s largest caldera. Geological structures beneath Long Valley Caldera are mapped by the novel adjoint-state traveltime tomography method. Adjoint-state traveltime tomography is an Eikonal equation-based seismic imaging method. It is computationally efficient as compared to wave equation-based adjoint tomography methods. Furthermore, the method avoids ray tracing in non-homogeneous media, which may fail using conventional ray tracing techniques. The data used in the method include P- and S-wave arrival times gathered from Northern California Earthquake Data Center (NCEDC). P-wave traveltimes are directly obtained from NCEDC, while high-quality S-wave arrivals are carefully picked on raw seismograms based on waveform similarity. With the abundant seismic traveltime data and adjoint-state traveltime tomography method, we can generate high-resolution P- and S-wave velocity models for the region of Long Valley Caldera. The relationship between velocity heterogeneity and seismic and magmatic activities will be investigated.   

How to cite: Chey, C. F., Li, T., and Tong, P.: Adjoint-State Traveltime Tomography of Long Valley Caldera in California, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15261, https://doi.org/10.5194/egusphere-egu23-15261, 2023.

X2.67
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EGU23-16673
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SM5.2
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ECS
Gongheng Zhang, Xuping Feng, Xiaofei Chen, Qi Liu, and Lina Gao

Ambient noise tomography has been a widely used method for imaging the structure of the lithosphere. A key step in this method is extracting the dispersion curve from ambient noise cross-correlation. Based on the single force displacement formula of Generalized Reflection and Transmission method, we obtained the type of Bessel function in different components of the cross-correlation function. Borrowing the idea of the S transformation and replacing the exponential function in which with the corresponding Bessel function to different components of cross-correlation function, we define a new transformation, named SJ transformation, to extract Rayleigh wave dispersion curve from ZZ, ZR, RZ, RR component and Love wave dispersion curve from TT component. Using synthetic test, the extracted dispersion curve fits the theoretical dispersion curve well, which’s error rate < 1%, and in field data test, the extracted dispersion curve of the Rayleigh wave from different component matches each other well. Although the SJ spectrum of ZZ component may be distorted by noise, there may be no influence in other components, which provide the possibility to extract Rayleigh wave dispersion curve with a wider frequency band.

How to cite: Zhang, G., Feng, X., Chen, X., Liu, Q., and Gao, L.: A Novel Transform For Extracting Dispersion Curve From Multiple Components of Ambient Noise Cross-correlation Function, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16673, https://doi.org/10.5194/egusphere-egu23-16673, 2023.

Posters virtual: Mon, 24 Apr, 08:30–10:15 | vHall GMPV/G/GD/SM

Chairpersons: Thomas Burschil, Frédéric Nguyen
vGGGS.8
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EGU23-3778
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SM5.2
|
ECS
Yunpeng Zheng, Fei Cheng, and Jiangping Liu

Tunnel advance detection technology is an important method for determining the structure of a complex geological body in front of the tunnel face. Among the tunnel advance detection technologies, the seismic method is one of the most accurate methods with long detection distances. In seismic tunnel advance detection, the cylindrical configuration aggravates the complexity of the wave field in the tunnel space and significantly influences the accuracy of the detection results. Thus, it is crucial to simulate an accurate seismic full-wave field of the tunnel space and to understand the propagation and wave-field characteristics of individual seismic waves for seismic tunnel advance detection. Usually, in 3D Cartesian coordinates, the tunnel wall is approximated with a staircase boundary, but it is not sufficiently accurate in shape and generates numerical dispersion in the simulation, especially in the presence of surface waves. Therefore, we developed a variable staggered-grid finite-difference method in cylindrical coordinates to simulate the elastic full-wave field in a 3D tunnel space. Setting free-surface boundary conditions solves the propagation of surface waves on the tunnel wall and face. The free-surface boundary condition was validated by comparing the simulated seismic records with the finite element method. The interference of the instability and discontinuity of the pole axis in the seismic wave field simulation was eliminated using our method. Using this scheme, we simulated the elastic full-wave field of three geological bodies in front of the tunnel face, including the vertical interface, inclined interface, and karst cave. The results of the three models show that the excitation near the tunnel face is more conducive to the detection of geological bodies. Compared with the simulation results in Cartesian coordinates, the results in cylindrical coordinates show that numerical dispersion is negligible and conclude that a higher signal-to-noise ratio and more accurate seismic wave field can be simulated with cylindrical coordinates in the tunnel space. The new method can also be used as an accurate elastic wave propagator for reverse-time migration and full-waveform inversion under tunnel-observing geometries. Our simulation method provides theoretical and practical guidance for analyzing and interpreting seismic wave fields in tunnel advance detection.

How to cite: Zheng, Y., Cheng, F., and Liu, J.: Elastic full-wave field simulation in 3D tunnel space using a variable staggered-grid finite-difference method in cylindrical coordinates, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3778, https://doi.org/10.5194/egusphere-egu23-3778, 2023.

vGGGS.9
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EGU23-28
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SM5.2
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ECS
Yonatan Garkebo Doyoro and Ping-Yu Chang

We examine the measurement noise of electrical resistivity tomography and assess its effect on the inverted results. The observed and numerically simulated resistivity datasets are analyzed regarding noise distributions. We evaluate and present the contact resistance, reciprocal and repeating errors, potential noise, artificial effect on 2D resistivity measurement, inversion misfit, and model accuracy. The result shows considerable measurement noise variation for dry and wet conditions. This study uses a 3% repeatability error cut-off, and about 3.2% of the dry season and 0.83% of the wet season datasets are above cut-off values.  The result also exhibits an inverse relationship between the precipitation and reciprocal error. The resistivity measurement in dry conditions generally indicates high contact resistance, repeatability error, and reciprocal errors, resulting in significant data discarding. We also reveal the misfit between observed and model-predicted resistivity data; a high discrepancy is exhibited for noisy data, leading to substantial model error. The depth of investigation (DOI) threshold depth decrease with increasing measurement noise. This study will give insight into measurement noise evaluation, allow cut-off value, assess data noise propagation and its effects on the data misfits and inverted models, and reduce model misinterpretation.

How to cite: Doyoro, Y. G. and Chang, P.-Y.: The 2D resistivity measurement eror and its effect on the model accuracy, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-28, https://doi.org/10.5194/egusphere-egu23-28, 2023.

vGGGS.10
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EGU23-14363
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SM5.2
Qingjie Yang, Marcus Engsig, Meixia Geng, and Chaouki Kasmi

Full waveform inversion (FWI) is a challenging data-fitting procedure based on full wavefields to abstract quantitative information from seismograms. During the process, the wave propagation equation is solved for different sources and frequencies. Therefore, an efficient and effective wave propagation engine plays a critical role for FWI. The modelled data, theoretically, can be seen as the composites of Green’s function and source wavelet, convolution in the time domain, or multiplication in the frequency domain. So, the accurate source wavelet is essential for successfully applying full waveform inversion on a real dataset.  Yet, it remains a challenging task for data sets with sparse acquisition and noisy field datasets. In the traditional inversion procedure, the source signal can be estimated from observed seismograms as a part of FWI, which is time consuming and may result in inversion divergence. A source wavelet can also be extracted from the direct arrivals in the streamer dataset. However, the quality of the direct arrivals can be diminished by reflections and near-surface noise for land acquisition, vertical seismic profiling (VSP), and ocean bottom cable (OBC) datasets.

To avoid inaccurate source wavelet estimation, various source-independent methods are applied to FWI. Firstly, the deconvolution-based source-independent algorithm is proposed to mitigate the uncertainty of source wavelet estimation by normalizing the seismic data with a reference trace in the frequency domain. Then, the convolution-based source-independent algorithm is presented in the time domain to eliminate the source wavelet influence by convolving the observed data with a reference trace selected from a modelled wavefield, and the modelled data with a reference trace selected from an observed wavefield. To avoid an arbitrary or manual selection of the reference trace, we present a convolution-based source-free method implemented in the frequency domain. Thus, the convolution process becomes a multiplication in our source-free misfit function, achieving a significantly simpler implementation than in the time domain and requiring no artificial interposition.

How to cite: Yang, Q., Engsig, M., Geng, M., and Kasmi, C.: A novel source-free frequency-domain full waveform inversion, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14363, https://doi.org/10.5194/egusphere-egu23-14363, 2023.

vGGGS.11
|
EGU23-14984
|
SM5.2
|
ECS
|
Alina Coman and Laura Petrescu

Bucharest is a densely populated urban region affected by strong earthquakes generated in the Vrancea seismogenic area. Studying and understanding its underground structure can help constrain seismic risk and improve estimates of seismic hazard and resilience. To obtain a 3D image of the near - surface structure beneath Bucharest, we analyse ambient noise records from 34 broadband seismic stations that operated throughout the city in 2004 ( the URS – URban Seismology network). We cross - correlate daily vertical component seismograms to obtain virtual Rayleigh waveforms and extract the phase velocity dispersion curves between pairs of stations using an automated Bessel - function analogue algorithm for periods between 2s and 10s. Dispersion curves are then combined in a fast marching seismic tomography (FMST) to estimate the lateral distribution of phase velocities at discrete periods. These are then jointly inverted with horizontal-to-vertical spectral ratios using Simulated Annealing methods under the assumption of a diffuse field to obtain shear wave velocity profiles with depth beneath each station. Preliminary results reveal seismic heterogeneities beneath Bucharest and offer fundamental constraints on the anomalous ground motion amplification and its relationship with complex geological structures from the uppermost crust.

How to cite: Coman, A. and Petrescu, L.: Near-surface ambient-noise seismic tomography of Bucharest, Romania, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14984, https://doi.org/10.5194/egusphere-egu23-14984, 2023.