SM5.2

Critical groundwater resources and hidden seismic hazards underly much of the Mississippi Alluvial Plain. Spanning nearly 100,000 square kilometers across seven states, this region hosts one of the most prolific shallow aquifer systems in the United States that supports a $12 billion agricultural economy amidst chronic groundwater decline. Further, underlying fault structures of the Reelfoot Rift and New Madrid Seismic Zone represent an important and poorly understood hazard with a complex pattern of historical impacts. Despite its societal and economic importance, mapping of shallow subsurface architecture with spatial resolution needed for effective management is insufficient. Here, we report the results of 40,000 flight-line-kilometers of electromagnetic, magnetic, and radiometric data collectively providing a system-scale snapshot of an entire aquifer system, the first such effort in the United States. This survey enables new understanding of the regional hydrogeology while also revealing previously unseen large vertical displacements (exceeding 50 m) in the uppermost Tertiary units within the New Madrid Seismic Zone.

How to cite: Minsley, B., Rigby, J., James, S., Burton, B., Knierim, K., Pace, M., Bedrosian, P., and Kress, W.: Closing a scale-gap in Earth observation using regional-scale airborne geophysics in the lower Mississippi Valley, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3508, https://doi.org/10.5194/egusphere-egu21-3508, 2021.

Ground magnetic surveys are commonly used for imaging near-surface structures in archaeological studies. Usually, surveys are conducted using vertical component gradiometers or scalar gradiometers to produce a vertical pseudo-gradient map. Scalar magnetometers can also be used, albeit less frequently, to produce maps of the total magnetic anomaly. In all these cases, the equipment is pushed or pulled by an operator or carried behind a vehicle. Here we present a third approach made available by the use of three-component fluxgate magnetometers: fast surveys over large areas using a compact lightweight drone flying automatically 1 to 2 m above the ground and high precision surveys acquired by an operator 0,2 to 1 m above the ground. A case study on the gallo-roman site of Oedenburg, located along the Rhine River in its upper valley, illustrates the results that can be obtained with the approach. A comparison with previously acquired pseudo-gradient surveys shows that the presented method allows a faster coverage, a greater resolution for the imaging of short wavelength structures (such as walls) and a better capacity of imaging large wavelength structures (such as pathways, palaeochannels or soil composition variations). As the site is crossed by a high voltage electric power line, a method to suppress the high-amplitude 50 Hz frequency magnetic field is presented.

How to cite: Gavazzi, B., Reiller, H., Munschy, M., Pierrevelcin, G., Basoge, F., Mercier de Lépinay, J., and Fréville, T.: A multi-scale ground and drone-borne magnetic survey approach for the detection and investigation of archaeological structures, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7372, https://doi.org/10.5194/egusphere-egu21-7372, 2021.

Over the past 1-2 decades, seismological measurements have provided new and unique insights into glacier and ice sheet dynamics. At the same time, sensor coverage is typically limited in harsh glacial environments with littile or no access. Turning kilometer-long fiber optic cables placed on the Earth’s surface into thousands of seismic sensors, Distributed Acoustic Sensing (DAS) may overcome the limitation of sensor coverage in the cryosphere.

First DAS applications on the Greenland and Antarctic ice sheets and on Alpine glacier ice have highlighted the technique’s superiority. Signals of natural and man-made seismic sources can be resolved with an unrivaled level of detail. This offers glaciologists new perspectives to interpret their seismograms in terms of ice structure, basal boundary conditions and source locations. However, previous studies employed only relatively small network scales with a point-like borehole deployment or < 1 km cable aperture at the ice surface.

Here we present a DAS installation, which aims to cover the majority of an Alpine glacier catchment: For one month in summer 2020 we deployed a 9 km long fiber optic cable on Rhonegletscher, Switzerland, and gathered continuous DAS data. The cable followed the glacier’s central flow line starting in the lowest kilometer of the ablation zone and extending well into the accumulation area. Even for a relatively small mountain glacier such as Rhonegletscher, cable deployment was a considerable logistical challenge. However, initial data analysis illustrates the benefit compared to conventional cryoseismological instrumentation: DAS measurements capture ground deformation over many octaves, including typical high-frequency englacial sources (10s to 100s of Hz) related to crevasse formation and basal sliding as well as long period signals (10s to 100s of seconds) of ice deformation. Depending on the presence of a snow cover, DAS records contain strong environmental noise (wind, meltwater flow, precipitation) and thus exhibit lower signal-to-noise ratios compared to conventional on-ice seismic installations. This is nevertheless outweighed by the advantage of monitoring ground unrest and ice deformation of nearly an entire glacier. We present a first compilation of signal and noise records and discuss future directions to leverage DAS data sets in glaciological research.

How to cite: Walter, F., Paitz, P., Fichtner, A., Edme, P., Gajek, W., Lipovsky, B. P., and Martin, E.: Capturing Glacier-Wide Cryoseismicity With Distributed Acoustic Sensing, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5809, https://doi.org/10.5194/egusphere-egu21-5809, 2021.

Development of Low Cost Autonomous Electrical Resistivity Monitoring Systems for continuous active-layer monitoring in harsh environment 

Fernando A. Monteiro Santos ⁽¹⁾, Mohammad Farzamian ⁽¹⁾, Miguel Esteves ⁽¹⁾, Gonçalo Vieira ⁽²⁾, Christian Hauck ⁽⁴⁾

(1) Universidade de Lisboa, IDL, Portugal

(2) Centre for Geographical Studies, IGOT, Universidade de Lisboa, Portugal

(3) Department of Geosciences, University of Fribourg, Switzerland

The last overview of the thermal state in the Western Antarctic Peninsula shows that permafrost is close to 0^oC in the region. This fact reinforces the importance to study the evolution of permafrost and active layer in the region. However, monitoring of the active layer and permafrost dynamics in Antarctica is generally conducted using only 1-dimensional borehole and meteorological data, which restricts the analysis to point information that often lack representatives at the field scale. In addition, being an invasive technique, the drilling of boreholes disturbs the subsurface and is not feasible to conduct over large areas, especially in environmentally sensitive ecosystems such as the Antarctic.

In this context, we developed automated electrical resistivity tomography (A-ERT) systems using a 4POINTLIGHT_10W (Lippmann) instrument with a solar panel-driven battery and multi-electrode configuration for autonomous and non-invasive monitoring of active layer and permafrost in Antarctica. The A-ERT measurements are sensitive to the electrical conductivity of materials, allowing to distinguish between frozen and unfrozen soil and thus to monitor the active layer dynamics including freezing, thawing, water infiltration and refreezing processes in a spatial context. We deployed the system in two monitoring sites at Deception and Livingstone Islands (South Shetland Islands, Maritime Antarctica) for quasi-continuous measurements at 6h interval from early 2019 and 2020 respectively.

Detailed investigation of the A-ERT data and obtained models reveals that the A-ERT system can detect the seasonal active-layer freezing and thawing events with very high resolution. In addition, the brief surficial refreezing and thawing of the active layer during summer and winter respectively were well resolved by A-ERT data, highlighting the significance of the continuous A-ERT monitoring setup which enables detecting fast changes in the active layer during short-lived extreme meteorological event. This suggests that the A-ERT measurements can provide valuable subsurface information to improve the spatio-temporal understanding of active layer and permafrost dynamics with very high resolution and minimal environmental disturbance in Antarctica. The set-up is very flexible and can be used with different configurations to investigate different depth ranges for site-specific detailed investigation.

Publication supported by FCT- project UID/GEO/50019/2020 - Instituto Dom Luiz

How to cite: Monteiro dos Santos, F. A., Farzamian, M., Esteves, M., Vieira, G., and Hauck, C.: Development of Low Cost Autonomous Electrical Resistivity Monitoring Systems for continuous active-layer monitoring in harsh environment , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12883, https://doi.org/10.5194/egusphere-egu21-12883, 2021.

Quantitative estimation of pore fractions filled with liquid water, ice and air is one of the prerequisites in many permafrost studies and forms the basis for a process-based understanding of permafrost and the hazard potential of its degradation in the context of global warming. The volumetric ice content is however difficult to retrieve, since standard borehole temperature monitoring is unable to provide any ice content estimation. Geophysical methods offer opportunities to image distributions of permafrost constituents in a non-invasive manner. A petrophysical joint inversion was recently developed to determine volumetric water, ice, air and rock contents from seismic refraction and electrical resistivity data. This approach benefits from the complementary sensitivities of seismic and electrical data to the phase change between ice and liquid water. A remaining weak point was the unresolved petrophysical ambiguity between ice and rock matrix. Within this study, the petrophysical joint inversion approach is extended along the time axis and respective temporal constraints are introduced. If the porosity (and other time-invariant properties like pore water resistivity or Archie exponents) can be assumed invariant over the considered time period, water, ice and air contents can be estimated together with a temporally constant (but spatially variable) porosity distribution. It is hypothesized that including multiple time steps in the inverse problem increases the ratio of data and parameters and leads to a more accurate distinction between ice and rock content. Based on a synthetic example and a field data set from an Alpine permafrost site (Schilthorn, Swiss Alps) it is demonstrated that the developed time-lapse petrophysical joint inversion provides physically plausible solutions, in particular improved estimates for the volumetric fractions of ice and rock. The field application is evaluated with independent validation data including thaw depths derived from borehole temperature measurements and shows generally good agreement. As opposed to the conventional petrophysical joint inversion, its time-lapse extension succeeds in providing reasonable estimates of permafrost degradation at the Schilthorn monitoring site without a priori constraints on the porosity model.

How to cite: Klahold, J., Hauck, C., and Wagner, F.: Ice or rock matrix? Improved quantitative imaging of Alpine permafrost evolution through time-lapse petrophysical joint inversion, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4509, https://doi.org/10.5194/egusphere-egu21-4509, 2021.

In frequency domain Electromagnetic Induction (EMI) surveys, an image of the electrical conductivity of the subsurface is obtained non-invasively. The electrical conductivity can be related to important subsurface properties such as the porosity, saturation or water conductivity via Archie’s law. The advantage of geophysical EMI surveys is its cost-effectiveness because it is a non-contacting method, one can easily walk with the device or mount in on a vehicle or a helicopter (AEM).

The process of finding the conductivity profile from the collected field data is an ill-posed inverse problem. Regularization improves the stability of the inversion and, based on Occam’s razor principle, a smoothing constraint is typically used with a very large number of thin layers. However, the conductivity profiles are not always expected to be smooth. Another alternative is to use a predefined number of layers and to invert for their conductivity and thickness. This can yield sharp contrasts in conductivity. In practice however, the real underground might be either blocky or smooth, or somewhere in between. Those standard constraints are thus not always appropriate.

We develop a new minimum-structure inversion scheme in which we transform the model into the wavelet space and impose a sparsity constraint. This sparsity constrained inversion scheme minimizes an objective function with a least-squares data misfit and a sparsity measure of the model in the wavelet domain. With a solid understanding of wavelet theory, a novel and intuitive model misfit term was developed, allowing for both smooth and blocky models, depending on the chosen wavelet basis. A model in the wavelet domain has both temporal (i.e. low and high frequencies) and spatial resolution, and penalizing small-scale coefficients effectively reduces the complexity of the model.

Comparing the novel scale-dependent wavelet-based regularization scheme with wavelet-based regularization with no scale-dependence, revealed significantly better results (Figure A and B) w.r.t. the true model. Comparing with standard Tikhonov regularization (Figure C and D) shows that our scheme can recover high amplitude anomalies in combination with globally smooth profiles. Furthermore, the adaptive nature of the inversion method  (due to the choice of wavelet) allows for high flexibility because the shape of the wavelet can be exploited to generate multiple representations (smooth, blocky or intermediate) of the inverse model.

We have introduced an alternative inversion scheme for EMI surveys that can be extended to any other 1D geophysical method. It involves a new model misfit or regularization term based on the wavelet transform and scale-dependent weighting which can easily be combined with the existing framework of deterministic inversion (gradient-based optimization methods, L-curve criterion for optimal regularization parameter). A challenge remains to select the optimal wavelet, however, the ensemble of inversion results with different wavelets can also be used to qualitatively assess uncertainty.

How to cite: Deleersnyder, W., Maveau, B., Dudal, D., and Hermans, T.: Inversion of electromagnetic induction data using a novel wavelet-based and scale-dependent regularization term, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-974, https://doi.org/10.5194/egusphere-egu21-974, 2021.

The near surface is a complex and often highly heterogeneous system as its current status results from interacting processes of both natural and anthropogenic origin. Effective sustainable management and land use planning, especially in urban environments, demands high-resolution subsurface property models enabling to capture small-scale processes of interest. The modelling methods based only on discrete direct observations from conventional invasive sampling techniques have limitations with respect to capturing the spatial variability of these systems. Near-surface geophysical surveys are emerging as powerful techniques to provide indirect measurements of subsurface properties. Their integration with direct observations has the potential for better predicting the spatial distribution of the subsurface physical properties of interest and capture the heterogeneities of the near-surface systems.

Within the most common geophysical techniques, frequency-domain electromagnetic (FDEM) induction methods have demonstrated their potential and efficiency to characterize heterogeneous deposits due to their simultaneous sensitivity to electrical conductivity (EC) and magnetic susceptibility (MS). The inverse modelling of FDEM data based on geostatistical techniques allows to go beyond conventional analyses of FDEM data. This geostatistical FDEM inversion method uses stochastic sequential simulation and co-simulation to perturbate the model parameter space and the corresponding FDEM forward model solutions, including both the synthetic FDEM responses and their sensitivity to changes on the physical properties of interest. A stochastic optimization driven by the misfit between true and synthetic FDEM data is applied to iterative towards a final subsurface model. This method not only improve the confidence of the obtained EC and MS inverted models but also allows to quantify the uncertainty related to them. Furthermore, taking into account spatial correlations enables more accurate prediction of the spatial distribution of subsurface properties and a more realistic reconstruction of small-scale spatial variations, even when considering highly heterogeneous near surface systems. Moreover, a main advantage of this iterative geostatistical FDEM inversion method is its ability to flexibly integrate data with different resolution in the same framework.

In this work, we apply this iterative geostatistical FDEM inversion technique, which has already been successfully demonstrated for one- and two-dimensional applications, to invert a real case FDEM data set in three dimensions. The FDEM survey data set was collected on a site located near Knowlton (Dorset, UK), which is geologically characterized by Cretaceous chalk overlain by Quaternary siliciclastic sand deposits. The subsurface at the site is known to contain several archaeological features, which produces strong local in-phase anomalies in the FDEM survey data. We discuss the particular challenges involved in the three-dimensional application of the inversion method to a real case data set and compare our results against previously obtained ones for one- and two-dimensional approximations.

How to cite: Azevedo, L., Narciso, J., and Van De Vijver, E.: Geostatistical FDEM inversion: a three-dimensional real case application, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16130, https://doi.org/10.5194/egusphere-egu21-16130, 2021.

Frequency domain loop-loop electromagnetic induction (FDEM) soundings using decametric coil-separations and multi-frequency sources have been used for decades to investigate the electrical conductivity of top 100 m of the subsurface. The most common coil configurations include horizontal and vertical co-planar (HCP and VCP) setups, and the data recorded with a rather large station spacing are typically processed assuming 1D layered media. In many geological situations, the subsurface shows significant lateral contrasts in the electrical material properties, especially, in regoliths close to earth’s surface. Here, the HCP and VCP 2D/3D sensitivity functions show complex and rather extended lateral sensitivity patterns. Therefore, in presence of high lateral variations in the uppermost layers, assuming 1D layered media for interpreting HCP and VCP profiles is often not valid. Furthermore, using rather large lateral station spacings often hinders the identification (and removal) of 2D/3D effects. In consequence, the overall 1D FDEM profiling procedure is often considered to be less robust than other electrical imaging techniques (e.g., DC tomography) to depict near-surface horizontal variations of the subsurface.

In shallower FDEM applications focusing on the characterization of the uppermost soil layers, portable loop-loop FDEM sensors (e.g. rigid boom systems with coil separations < 6 m) are used to explore the subsurface electrical properties. Here, it is commonly known that the PERP configuration shows better lateral resolution and apparent conductivity maps closer to the actual conductivity distribution. The latter feature is in fact crucial for the validity and applicability of the 1D approximation. The robustness of the PERP configuration regarding the 1D assumption can be explained by its sensitivity pattern showing a preponderant sign and a rather focused pattern, centered approximately below the receiver.

In order to evaluate the benefit of the PERP configuration for systems with decametric coil separation, we present two case studies, where densely sampled profiles of 1D inversions of multi-frequency FDEM HCP and PERP data are compared to 2D ERT inverted models and additional independent borehole and rigid-boom FDEM sensor data. In the first case study, we explore a coastal environment near Bourbourg, France, where only minor lateral variations in the subsurface are expected. Here, our results demonstrate that a 1D inversion of HCP and PERP data result in similar models. In the second case study, we explore debris flow deposits close to Braunsbach, Germany, which are characterized by significant near-surface lateral variability. In this case, only the 1D inversion of our PERP data results in a pseudo 2D model being in agreement with the inverted 2D ERT data. These two case studies confirm that the 1D inversion of PERP data (only) yields results that are more robust regarding 2D/3D artifacts than the 1D inversion of HCP data, or a joint inversion of HCP/PERP data. In conclusion, we propose that the 1D inversion of spatially densely sampled multi-frequency PERP data should be further evaluated in view of characterizing the lateral variations within the first 20 m of the subsurface because it could represent an efficient alternative to ERT methods in selected applications.  

How to cite: Guillemoteau, J., Arboleda Zapata, M., Simon, F.-X., Hulin, G., Deschodt, L., and Tronicke, J.: Vertical multi-frequency FDEM loop-loop soundings for sub-surface conductivity imaging: comparison of HCP and PERP configurations for different environments, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6149, https://doi.org/10.5194/egusphere-egu21-6149, 2021.

Accurate subsurface imaging through geophysics is of prime importance for many geological and hydrogeological applications. Recently, airborne electromagnetic methods have become more popular because of their potential to quickly acquire large data sets at relevant depths for hydrogeological applications. However, the solution of inversion of airborne EM data is not unique, so that many electrical conductivity models can explain the data. Two families of methods can be applied for inversion: deterministic and stochastic methods. Deterministic (or regularized) approaches are limited in terms of uncertainty quantification as they propose one unique solution according to the chosen regularization term. In contrast, stochastic methods are able to generate many models fitting the data. The most common approach is to use Markov chain Monte Carlo (McMC) Methods. However, the application of stochastic methods, even though more informative than deterministic ones, is rare due to a quite high computational cost.

In this research, the newly developed approach named Bayesian Evidential Learning 1D imaging (BEL1D) is used to efficiently and stochastically solve the inverse problem. BEL1D is combined to SimPEG: an open source python package, for solving the electromagnetic forward problem. BEL1D bypasses the inversion step, by generating random samples from the prior distribution with defined ranges for the thickness and electrical conductivity of the different layers, simulating the corresponding data and learning a direct statistical relationship between data and model parameters. From this relationship, BEL1D can generate posterior models fitting the field observed data, without additional forward model computations. The output of BEL1D shows the range of uncertainty for subsurface models. It enables to identify which model parameters are the most sensitive and can be accurately estimated from the electromagnetic data.

The application of BEL1D together with SimPEG for stochastic transient electromagnetic inversion is a very efficient approach, as it allows to estimate the uncertainty at a limited cost. Indeed, only a limited number of training models (typically a few thousands) is required for an accurate prediction. Moreover, the computed training models can be reused for other predictions, considerably reducing the computation cost when dealing with similar data sets. It is thus a promising approach for the inversion of dense data set (such as those collected in airborne surveys). In the future, we plan on relaxing constraints on the model parameters to go towards interpretation of EM data in coastal environment, where transition can be smooth due to salinity variations.

Keywords : EM, Uncertainty, 1D imaging, BEL1D, SimPEG

How to cite: Ahmed, A., Michel, H., Deleersnyder, W., Dudal, D., and Hermans, T.: Applying BEL1D for transient electromagnetic sounding inversion, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1131, https://doi.org/10.5194/egusphere-egu21-1131, 2021.

A common challenge in reflection GPR data processing and analysis is the reconstruction of missing traces. Gap filling, for example, may be needed to fill-in data where they could not be recorded in the field in order to produce a uniform trace spacing that is important for Fourier- or finite-difference-based migration methods. Similarly, field GPR data recorded in continuous mode with an uneven trace spacing are usually needed at a regular spacing for subsequent visualization and imaging. Finally, we may wish to increase the spatial resolution of a GPR dataset through “super-resolution”, whereby new traces are simulated between the existing ones in order to improve the interpretability of the data. A common challenge in these various applications is the need to interpolate a variable that has a complex, non-smooth behavior.

A number of interpolation methods have been proposed for filling in missing GPR traces over the past decades. The majority of these, however, tend to produce overly smooth and unrealistic results. Here, we present a data reconstruction strategy based on the QuickSampling (QS) multiple-point geostatistical method. With this approach, GPR traces are simulated via sequential conditional simulation based on patterns that are observed in nearby high-resolution data (training images). To evaluate the potential of our approach, we apply it to a variety of field 2D GPR datasets. Results indicate that the QS method provides an effective means of simulating missing GPR traces in a highly realistic manner.

How to cite: Zhang, C., Gravey, M., Irving, J., and Mariethoz, G.: Multiple-point geostatistical reconstruction of GPR reflection data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13373, https://doi.org/10.5194/egusphere-egu21-13373, 2021.

The ground-penetrating radar (GPR) datasets obtained in archaeological environments have substantial problems related the presence of clutter noise. These noisy reflections are generated by the heterogeneities of the ground and by the collapses of structures buried in the ground, that can prevent a good assessment of the subsurface with this method. The classic filtering operations available can fail to remove it effectively. This work presents an approach to filtering the GPR data in the 2D spectral domain through the singular value decomposition (SVD) factorization technique. The spectral domain present advantages such as the circular symmetry of the transformed data that turns easy the filter parametrisation and the constant computational effort whatever the amount of data considered. SVD allows the decreasing of the user dependency to parametrize the filter. The main propose of this method is to classify automatically the datasets into useful information, corresponding to buried structures, and noise, to remove the last. This approach was conceived based on the study of the GPR signal in the 2D spectral domain and the manual filter design. The tests were performed with different datasets, one from a laboratory experiment (controlled environment) and the other from a field acquisition in an archaeological site (uncontrolled environment) with subsequent excavation to proof the results. The proposed approach is effective to remove the clutter noise in the GPR datasets and constitutes a complementary operation to those already existing in the commercial software.

Acknowledgment: The work was supported by the Portuguese Foundation for Science and Technology (FCT) project UIDB/04683/2020 - ICT (Institute of Earth Sciences) and by the INTERREG 2014-2020 Program, through the "Innovación abierta e inteligente en la EUROACE" Project, with the reference 0049_INNOACE_4_E.

How to cite: Oliveira, R. J., Caldeira, B., Teixidó, T., and Borges, J. F.: Enhancement of 3D GPR datasets using singular value decomposition applied in 2D the spectral domain for clutter noise removal, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9492, https://doi.org/10.5194/egusphere-egu21-9492, 2021.

A detailed characterization of the landfill geometry, the waste volume and composition, and the water saturation within and outside the landfill body is critical for an adequate environmental management. To overcome the limited spatial resolution of direct investigations into landfills, geophysical methods have proven to resolve subsurface properties with high spatial resolution in a non-invasive and cost-efficient manner. The joint inversion of different geophysical datasets became increasingly popular in various fields of application since it solves quantitatively for the parameters of interest. Built upon a recently developed framework considering Archie’s law and a time-averaging equation for the seismic slowness, we present here the petrophysical joint inversion (PJI) of electrical and seismic data collected along three profiles at the “Heferlbach” landfill located close to Vienna (Austria). We use the PJI framework to simultaneously invert apparent electrical resistivities and seismic traveltimes to solve for quantitative estimates for porosity, water saturation and air content. Our results show that the shallow geometry of the landfill with an average thickness of 3.5 m is clearly resolved by subsurface areas characterized by an air content of approximately 40 %. Based on the resolved saturation, we were able to identify the known aquifer underneath the landfill (average saturation of 25 %) that is lying on top of an aquiclude formed by tertiary sands. Within the landfill body, the saturation is approximately 10 to 15 %, which is in agreement with available data from the site. The resolved porosity model shows significant lateral variations (between 40 and 60 %) at shallow depths (< 3 m) suggesting a varying degree of compaction of the waste and different types of waste. Our results demonstrate the potential of the proposed PJI to enhance geophysical investigations of landfills by providing plausible quantitative estimates for parameters of interest with an adequate spatial resolution.

How to cite: Steiner, M., Katona, T., Fellner, J., and Flores Orozco, A.: Characterization of a solid waste landfill through geophysical data fusion, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12073, https://doi.org/10.5194/egusphere-egu21-12073, 2021.

Large scale slope instabilities are complex objects controlled by multiple parameters. The underground and superficial structure of the slope plays a major role as it often controls water circulations, potentially causing weathering and damaging processes, and permits the local storage of water masses, causing temporary overload. In addition, the structure of the subsurface often delineates rock-volumes with variable mechanical properties, whose spatial distribution greatly influences the behavior of the slope. This work illustrates how Dense 3D Electrical Resistivity Tomography can provide relevant constraints on these parameters.

The village of Viella, in France (Hautes-Pyrénées), is affected by strong slope movement since 2018, when a massive rockslide above the village modified the stress conditions of the entire slope and, potentially, the hydrogeological context. As a consequence, some houses and infrastructures are progressively damaged, leading to heavy measures (houses evacuation). This complex, deep-seated (> 80 m), slope instability covers an area of ca. 650 000 m², is primarily composed of altered shists, colluviums, and non-consolidated alluvial deposits, forming several kinematic units with surface velocities in the range [0.5 – 5] mm.month^-1.

A 3D dense electrical resistivity tomography was realized using the FullWaver system, to characterize the structure and the forcing factors of this unstable slope. 55 V-FullWavers receivers (3 -electrodes, 2 channels sensors) were quasi-evenly distributed over a surface area of 400 x 500 m² with an interval of 90 m, apart from the village area, where no electrode could be grounded. Each V-FullWaver recorded signals through two orthogonal dipoles of 25 m length. Current injections were realized with a high-power transmitter (6 kW, 16 A, 3000 V). 235 injection dipoles were used. The system injected current between a fixed remote electrode (more than 1 km away from the site to increase the investigation depth) and a local mobile electrode, moved all over the investigated area in between the V-Fullwaver receivers, with an interval of approximately 40 m, except in the village area.

The resulting 3D resistivity model presents a high spatial variability until 100 to 150 m depth approximately, that highly relates to the complex strain dynamics of the slope and the hydrogeological observations. It highlights the relation between the most active kinematic compartments and the large-scale structure of the slope.

It provides a first understanding of the role of local compacted rocks in the buildup of surface deformation but also on the localization of heterogeneities (fissures, scarps) which may relate to water circulation paths.

. This 3D image of the slope is the first structural reference model for future hydrogeological and geomechanical studies aiming at deducing the possible evolution of the slope.

How to cite: Gance, J., Leite, O., Lajaunie, M., Susanto, K., Truffert, C., Maillard, O., Bertrand, C., Ferhat, G., and Malet, J.-P.: Dense 3D electrical resistivity tomography to understand complex deep landslide structures, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14522, https://doi.org/10.5194/egusphere-egu21-14522, 2021.

Geophysical imaging is subject to inherent non-uniqueness due to the ill-posed nature of the inverse problem. To mitigate this, the solution is commonly subjected to regularization. Smoothing regularization is widely used in practice, but produces high-dimensional images without sharp contrasts between geological units. These tomograms stand in contrast to current implicit geological models, which are able to produce sharp subsurface interfaces with complex geometries using low-dimensional parametrizations. This work aims to bring together modelling concepts from geophysics and geology using the example of electrical resistivity tomography (ERT).

An implicit geological model is used as the centerpiece of a 2D ERT inversion within a deterministic Gauss-Newton framework. The points that define the surfaces of the geological model are included into the model vector of the inverse problem along with a low-dimensional pilot point parametrization of the subsurface electrical resistivity. The point-based parameterization is translated to a triangular finite-element mesh to solve the geoelectrical forward problem. Sensitivities for the geological interfaces and resistivity parameters are efficiently calculated based on finite-differences and the reciprocity theorem, respectively. Each iteration step produces an update of both the geological interface as well as the parameter fields.

The approach converges to an updated geological model and a distribution of subsurface resistivity, which are in accordance with the measured data. The tomograms show sharply localized and realistic subsurface interfaces that are described by only a few parameters. While the imaging of small-scale heterogeneities is challenging and would require a locally increased number of pilot points, the current approach allows for the estimation of smoothly distributed heterogeneities. Further advantages of the approach lie in the improved integration of a-priori geological knowledge, the straightforward extension to 3D, and the applicability to other geophysical methods as well as joint inversion.

How to cite: Förderer, A., Wellmann, F., and Wagner, F.: Geoelectrical imaging of subsurface discontinuities and heterogeneities using low-dimensional parameterizations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10012, https://doi.org/10.5194/egusphere-egu21-10012, 2021.

Electrical resistivity tomography (ERT) is widely used to image the Earth's subsurface and has proven to be an extremely useful tool in application to hydrological problems. Conventional smoothness-constrained inversion of ERT data is efficient and robust, and consequently very popular. However, it does not resolve well sharp interfaces of a resistivity field and tends to reduce and smooth resistivity variations. These issues can be problematic in a range of hydrological or near-surface studies, e.g. mapping regolith-bedrock interfaces. While fully Bayesian approaches, such as those employing Markov chain Monte Carlo sampling, can address the above issues, their very high computation cost makes them impractical for many applications. Ensemble Kalman Inversion (EKI) offers a computationally efficient alternative by approximating the Bayesian posterior distribution in a derivative-free manner, which means only a relatively small number of 'black-box' model runs are required. Although common limitations for ensemble Kalman filter-type methods apply to EKI, it is both efficient and generally captures uncertainty patterns correctly. We propose the use of a new EKI-based framework for ERT which estimates a resistivity model and its uncertainty at a modest computational cost. Our EKI framework uses a level-set parameterization of the unknown resistivity to allow efficient estimation of discontinuous resistivity fields. Instead of estimating level-set parameters directly, we introduce a second step to characterize the spatial variability of the resistivity field and infer length scale hyper-parameters directly. We demonstrate these features by applying the method to a series of synthetic and field examples. We also benchmark our results by comparing them to those obtained from standard smoothness-constrained inversion. Resultant resistivity images from EKI successfully capture arbitrarily shaped interfaces between resistivity zones and the inverted resistivities are close to the true values in synthetic cases. We highlight its readiness and applicability to similar problems in geophysics.

How to cite: Binley, A., Tso, M., Iglesias, M., Wilkinson, P., Kuras, O., and Chambers, J.: Efficient multi-scale imaging of subsurface resistivity with uncertainty quantification using ensemble Kalman inversion, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16358, https://doi.org/10.5194/egusphere-egu21-16358, 2021.

Zagros continental collision zone (S-SW Iran) is tectonically active and extends over 1800 km contained most part of hydrocarbon reservoirs worldwide. The DehDasht region is located in the southeast of the Dezful embayment in the Zagros fold-and-thrust belt. The existence of an evaporation layer with high velocity features is the main challenge to apply classical seismic exploration in this region. However, ambient seismic noise carries valuable information about the propagation path; hence it could be a useful tool for studying crustal structure in the DehDasht region. For this purpose, we used up to 9 months of continuous data recorded by 107 stations in the area with ~16 × ~24 km². All stations are equipped with broadband (120s) sensors recording at 100 sps. The standard ambient seismic noise processing was done as outlined by Bensen et al. (2007), and optimize empirical Green’s function (EGF) was retrieved based on the WRMS stacking method. Afterward, Rayleigh wave dispersion measurements were calculated using the FTAN approach in the period range of 0.1-5.0 s, then the inversion procedure was performed by the Fast-Marching Method with an inversion cell size of 2×2 km. Our group velocity tomographic maps show a high velocity anomaly in the Khaviz Mountain belt (west part of the study area) is generally linked to the older, consolidated bodies while two low velocity anomalies are related to the presence of fluids and or younger structures.

How to cite: Shakeri, N., Shirzad, T., Ashkpour Motlagh, S., and Norouzi, S.: Shallow Crustal Structure in the DehDasht Region (SW Iran) from Ambient Seismic Noise Tomography, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9354, https://doi.org/10.5194/egusphere-egu21-9354, 2021.

In Spring 2011 (11^th of May), the vicinity of Lorca city (Murcia, SE Iberian Peninsula) was hit by two main seismic shocks that reach a maximum magnitude of 5.2 Mw. The earthquake caused serious widespread damage in the city and its surroundings. Similar events have affected the area regularly in the past (for example: on May 6,1977, 4.2 mg). These events are distributed along a relatively broad band (roughly NE-SW oriented) parallel to the coast, associated to the activation of the Alhama de Murcia Fault (AMF), an oblique-slip (reverse-strike-slip) fault system located in the Eastern Betics Shear Zone. The current study aims to characterize the shallow subsurface across some of the surface outcrop of a few of the main faults that lie within this seismogenic strike-slip fault system. Six normal-incidence seismic reflection profiles were acquired in the area crossing the AMF and the Carrascoy fault, among others). This study focuses on the determination of the shear-wave velocity depth model by applying Multichannel Analysis of Surface Waves (MASW), using the shot records of the seismic reflection profiles. The 1D velocity-depth functions acquired were pasted together to obtain the final 2D velocity models. The hand-picked dispersion curves were inverted using two different approaches to address the consistency of the inversion schemes. The final models reveal relevant differences across the different fault zones, reflecting the heterogeneity and lateral variability that characterizes a complex seismogenic zone, a most probably, diffuse plate boundary.

This research is supported by: Generalitat de Catalunya (AGAUR) grant 2017SGR1022 (GREG); EU (H2020) 871121 (EPOS-SP); EIT-RawMaterials 17024 (SIT4ME), CGL2013-47412-C2-1-P.

How to cite: Handoyo, H., Palomeras, I., Alcalde, J., de Felipe, I., Martí, D., García-Mayordomo, J., Martínez-Díaz, J. J., Teixidor, T., Insúa-Arevalo, J. M., and Carbonell, R.: ​Near-Surface High Resolution Characterization of the Seismogenic Alhama de Murcia Strike-slip Fault, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9185, https://doi.org/10.5194/egusphere-egu21-9185, 2021.

Abundant noise sources in urban area has been widely utilized for subsurface investigations based on the seiemic interferometry. Reliable dispersion extraction between two seismic stations is an essential basis of surface wave imaging. Noise source directivity has become an inescapable obstacle and a main concern for passive seismic surveys: it basically breaks the physics of Green’s function retrieval in travel-time tomography; Moreover, the azimuthal effect of ambient noise sources would inherently cause different levels of early arrival on cross-correlation functions, so that the apparent velocity of surface wave could be overestimated in multichannel slant stackings.

Instead of the conventional frequency-time analysis, which aims to extract the apparent dispersions of phase/group velocity between seismic stations, we proposed a method to jointly invert noise source distributions and the corresponding unbiased surface wave velocities based on the theoretical framework of full waveform ambient noise inversion. Waveform itself could intrinsically contains the features of travel-time, energy and asymmetry of ambient noise cross correlation functions (NCF). And they could in return map the resulted NCF into the noise source distributions and velocity structures. The L2 norm of cross-correlating waveform misfits was taken as the objective function to conduct gradient based inversion (i.e. the L-BFGS algorithm). We parametrized the noise source distributions as a temporally ensemble averaged model, which was discretized as a spatially plane grid of normalized source strength. The surface wave velocity model was approximated as the straight-ray interstation velocity. The two kinds of variants were decoupled in waveform misfit function by their corresponding partial derivatives to iteratively update the model space.

The effectiveness of source-velocity joint imaging using above full waveform inversion work flow was qualified by both the synthetic test and the applied research in Hangzhou urban area. The inverted noise source model was comparable with the urban traffic- and construction- noise distributions. And the truthful surface wave velocities were achieved considering the constraint of noise source distributions, they were also prior constrained and later verified by local borehole datasets.

How to cite: Zhou, C., Xia, J., Cheng, F., Pang, J., and Chen, X.: Modeling ambient noise distributions for surface wave imaging based on full waveform inversion, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3573, https://doi.org/10.5194/egusphere-egu21-3573, 2021.