Al-Hassa area features Saudi Arabia's largest oasis and one of the world's largest naturally irrigated land. Moreover, Al-Hassa area is very close to Ghawar, known as the largest conventional oil-field in the world. Additionally, more than 280 natural springs used in the past to water the farmland where the water in some of the springs, is used to be warm. The quality of water also exhibits spatial variations, hinting at a complex subsurface that must be characterized. Finally, the available geological information from outcrops, are very limited, since the majority of the study area is covered by a sand-layer. Based on the above, it seems that this important for its natural resources (oil & gas, groundwater, low-enthalpy geothermy) area is partly unexplored or the data are not available. The purpose of this work is to reconstruct the 3D subsurface geophysical structure of the study area by combining different geophysical electromagnetic (EM) methods. Thus, three EM geophysical methods to construct a detailed 3D model of the subsurface were applied. Specifically, 46 magnetotelluric (MT) stations, 6 audio magnetotelluric (AMT) stations, and 35 transient electromagnetic (TEM) stations were acquired within Al-Hassa National Park. The data from all these EM soundings were processed and integrated to achieve the highest resolution from the surface to the maximum depth of investigation. 2D and 3D processing, and joint interpretation was applied to all EM data. The EM findings were confirmed by gravity measurement conducted in the same area. The integration of various geophysical data sets, including TEM, MT, AMT and gravity data, uncovers lateral discontinuities in resistivity, a complex structure, and fracture zones acting as pathways or barriers to groundwater flow. This comprehensive modelling approach offers invaluable insights into the subsurface dynamics, enhancing our understanding for the complexity of the study area.

How to cite: Khogali, A., Kirmizakis, P., Chavanidis, K., Ashadi, A. L., Hanstein, T., Stampolidis, A., Candansayar, E., and Soupios, P.: Electromagnetic Insights: 3D Subsurface Modeling of Al-Hassa, Saudi Arabia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2343, https://doi.org/10.5194/egusphere-egu24-2343, 2024.

The Ganos Fault, in the westernmost part of the North Anatolian Fault Zone (NAFZ), stayed quiet for approximately 146 years before two catastrophic events shook the area in the summer of 1912. The historical records point out that two devastating activities shook both sides of the Marmara Sea in 1766, too. Following the 1766 events, on August 9^th, 1912, two blocks of the dextral Ganos Fault shifted one more time to create an Mw = 7.4 event near Mürefte in Tekirdağ. Nearly a month later, further to the west, the fault zone moved on September 13^th, forcing another disastrous Mw= 6.8 earthquake. Almost 112 years have passed since then (leaving approximately 34 more years to complete the recurrence), and the Ganos Fault is again acting as a seismic gap. In brief, the Ganos Fault tends to generate another series of earthquakes in the region, and the fault zone characteristics of this locked segment are poorly known. In this study, magnetotellurics (MT) method is utilized to image the crustal electrical resistivity structure for deciphering the fault zone geometry. With this object in mind, simultaneous electric and magnetic observations were made at nearly 40 sites in two campaigns. For each observation point, the collected data were transferred to the frequency domain where the electromagnetic impedance tensor elements were calculated with robust processing algorithms (Birrp) for wideband frequencies. Following the dimensionality analyses performed with various tools such as Groom and Bailey decomposition, phase tensor analysis, etc., which eventually pointed out a geo-electric strike angle of nearly ~N60^oE, numerical models based on two- and three-dimensional algorithms (such as MT2D and ModEM) were developed to image the fault zone properties of the Ganos Fault. Several numerical models were calculated to realize the influence of the coast-effect caused by the Marmara Sea. Pre-modeling analysis results and the final models suggest that (i) the geo-electric strike angle of ~N60^oE agrees well with the earlier results, the geology and the fault’s geometry, (ii) the Ganos Fault acts as a geological boundary between the Eocene-aged Keşan Formation in the north and Miocene-aged Çengelli Formation in the south, (iii) while the Keşan Formation defines a continuous tubular highly resistive zone (~500- 800 Ωm) that may be acting as the seismogenic zone along the Ganos Fault, the Çengelli Formation appears to be less resistive in coherent with the ages of the formations highlighted earlier, (iv) the aforementioned resistive block reaches to a depth of nearly 15 - 18 km and this feature may mark the bottom of the seismogenic zone.          

How to cite: Tank, B., Yazıcı, R., Doğukan, E., Demir, T., Taşseten, G., Duran, P., and Assar, T.: Imaging the Electrical Resistivity Structure of a Locked Fault Segment: The Ganos Fault example, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15344, https://doi.org/10.5194/egusphere-egu24-15344, 2024.

Magnetotellurics is often used as a tool in general geologic research. At the Supervisory Authority for Regulatory Affairs (SARA), Hungary, one of our aims is to form large-scale magnetotelluric (MT) key sections in order to be able to gain new insights into the diverse geologic and tectonic settings of our area through mapping the distribution of rocks’ electrical properties. For this purpose, MT sections have been created which cross Hungary from one country border to the other. Mainly archived MT data is used but, in some cases, new field measurements are needed to fill data gaps in the created profiles.

One of the magnetotelluric key sections is the MTOA-02 which crosses the western part of Hungary in a NW-SE direction. It is composed of three archived sub-sections and new supplementary MT stations which were measured during the past year in order to fill a 20 km long part of the profile without data. All these data were integrated into the dataset, analyzed and processed together which resulted in an approximately 210 km long magnetotelluric section. The processing of the data in this way is considered a novelty in the institute’s activity since previously, archived sub-sections were not handled together, they were only processed separately at the period of their acquisition.

As a part of data processing filtering, smoothing, the correction of static shift, 1D, and 2D inversions were carried out. For the interpretation both inversion results and observations from raw magnetotelluric data were used accompanied with information from other geological-geophysical methods (gravity, magnetism, geologic maps and cross sections) to confirm our conclusions from the resistivity profiles. As a result, main tectonic elements and geologic units were identified. Main structural lines include the Rába line, Balaton line, Kapos line and Mecsekalja line. Identified geologic units are as follows: the Penninic Unit and the subducted European passive margin, the Austroalpine Unit, the Transdanubian Range Unit, the Mid-Hungarian Megaunit, the Tisza Megaunit and Miocene sedimentary rocks filling the Pannonian back-arc basin. During data analyzes, we have noticed that phase-depth sections may be used to identify the depth of the Pre-Cenozoic basement in certain areas where low resistivity sedimentary rocks filling the basins do not show significant resistivity contrast with the underlying strata and hence the basement cannot be distinguished accurately on resistivity sections. This may help interpretations in the area of the Little Hungarian Plain.

How to cite: Szebenyi, R. and Kiss, J.: Regional-scale magnetotelluric data processing and interpretation in Transdanubia, Hungary, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8201, https://doi.org/10.5194/egusphere-egu24-8201, 2024.

Geomagnetically Induced Currents (GICs) flowing along electrically conductive infrastructure, such as power transmission lines, are produced by a naturally induced geoelectric field during geomagnetic disturbances, such as magnetic storms. GICs can cause widespread blackouts across power grids, resulting in the loss of electric power. Although GIC intensity is greater in high latitudes, recent studies highlight the importance of considering GIC risks for countries located in the low and middle latitudes, including the Mediterranean region. GIC index is a proxy of the geoelectric field calculated entirely from geomagnetic field variations. Following a recent study where we investigated the GIC index levels for the Mediterranean (i.e., Greece, Italy, France, Spain, Algeria, and Turkey) for the most intense magnetic storms of solar cycle 24 (2008–2019), here we expand the analysis to encompass solar cycle 25. From the beginning of solar cycle 25 six major magnetic storms occurred with Dst index ≤ -100 nT. The three most intense magnetic storms (-163 nT < Dst < -212 nT) occurred in March, April and November 2023. We focus on those to compare with previous results showing that GIC index increases are well correlated with Storm Sudden Commencements (SSCs) and shed more light upon the expected GIC activity levels in the Mediterranean region during extreme events.

How to cite: Boutsi, A. Z., Balasis, G., Dimitrakoudis, S., Daglis, I. A., Tsinganos, K., Papadimitriou, C., and Giannakis, O.: Investigating the GIC index levels in the Mediterranean region during the strongest magnetic storms of solar cycle 25, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12696, https://doi.org/10.5194/egusphere-egu24-12696, 2024.

The rapid development of geophysical systems utilizing drones facilitates mineral exploration with more efficient and economical data collection. To align the progress of hardware advancement and meet the model complexity needs for exploration, we aim to develop an efficient and robust 3D inversion code to interpret the drone-based EM data. Here, we present the framework of the implementation and show some preliminary results of the development.

We use a total electric field formulation with curl-conforming Nédélec elements to solve Maxwell equations in the frequency domain. Octree grids are used to accommodate the meshing of even large models at adequate resolution, separately in forward and inverse domains. A direct solver (MUMPS) is applied to solve the linear system of equations of the forward problem. The code is implemented in C++ and allows for easy adaptation for various sources and data types.

Currently, to solve the inverse problem, we minimize the misfit using a Gauss-Newton scheme with explicit computation of the Jacobian. The implementation was built on the deal.II library, where the interface wrappers allow to use MUMPS and PETSc for numerically intensive computations, such as system equation solving (MUMPS) and inversion model update (PETSC Conjugate Gradient solver). Currently, the code is parallelized using MPI throughout for both forward and inverse modeling and additionally OpenMP for MUMPS only.

The code is planned to be a reliable and competent imaging tool, that can be applied for both commercial and educational use. Currently, the code is under the development and testing stage. The preliminary results will be shown on-site.

How to cite: Xiao, L., Patzer, C., and Kamm, J.: Parallel inversion of drone-based electromagnetic data for near-surface geophysical prospecting, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15298, https://doi.org/10.5194/egusphere-egu24-15298, 2024.

Airborne electromagnetics made its first successful entry into mineral exploration applications, mainly by interpreting anomalies in the data. Airborne EM methods will increasingly be used for more advanced large-scale applications, such as mapping the fresh saltwater interface. Every step in the data interpretation is crucial in this regard. We believe the state-of-the-art (such as in [1]) is a good starting point for an initial data interpretation, though not the endpoint. We propose a workflow for airborne EM processing, where the current state-of-the-art may be too limited for your advanced applications. It consists out of four steps:

1. First processing with Quasi(or pseudo)-2D/3D inversion (with your favourite or typical smooth inversion scheme, such as in [1])

2. If the sharpness of the obtained inversion model is inappropriate, perform a tunable regularization with the inversion model from step 1 as the initial model. This can be achieved with wavelet-based regularization schemes [2], where prior information can be incorporated by the choice of the wavelet or, in the absence of prior information, the non-uniqueness can be assessed with the ensemble approach.

3. Where is multidimensionality a possible issue? In steps 1 and 2, we have used a 1D forward approximation, potentially yielding multidimensionality issues. An appraisal method, such as [3], enables the assessment of an inversion model obtained with 1D forward approximation for areas that do not fit the true multidimensionality of the observed data because it deviates from the 1D assumption.

3. Re-interpret zones with significant multidimensionality with either full 3D simulations or a surrogate model [4], replacing the computationally demanding 3D full simulations.

[1] Siemon, B., Auken, E., & Christiansen, A. V. (2009). Laterally constrained inversion of helicopter-borne frequency-domain electromagnetic data. Journal of Applied Geophysics, https://doi.org/10.1016/j.jappgeo.2007.11.003.

[2] Deleersnyder, W., Maveau, B., Hermans, T., & Dudal, D. (2023). Flexible quasi-2D inversion of time-domain AEM data, using a wavelet-based complexity measure. Geophysical Journal International,  https://doi.org/10.1093/gji/ggad032.

[3] Deleersnyder, W., Dudal, D., & Hermans, T. (2022). Novel airborne em image appraisal tool for imperfect forward modeling. Remote Sensing, https://doi.org/10.3390/rs14225757.

[4] Deleersnyder, W., Dudal, D., & Hermans, T. (2023, February). Machine learning assisted fast forward 3D modelling for time-domain electromagnetic induction data–lessons from a simplified case. In EGU General Assembly 2023, Location: Vienna, Austria, https://doi.org/10.5194/egusphere-egu23-12015.

How to cite: Deleersnyder, W., Hermans, T., and Dudal, D.: An efficient workflow for airborne electromagnetic data processing for advanced applications., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16111, https://doi.org/10.5194/egusphere-egu24-16111, 2024.

Geothermal heat production might pose the risk of degrading groundwater quality due to temperature changes, which may lead to ecological and economic impacts. Monitoring and quantifying spatial and temporal changes in the groundwater temperature are challenging but necessary for reliable environmental evaluations. Electromagnetic Induction (EMI) measurements have been extensively used in environmental monitoring, since Electrical Conductivity (EC) is very sensitive to changes in the groundwater properties, such as the presence of contaminants or changes in fluid temperature. Subsurface EC estimates can be obtained through the inversion of EMI measurements in a non-invasive and cost-effective way. However, these estimates are inherently ambiguous because of the ill-posed nature of the inverse problem.

In this project, we investigate how to provide an accurate EC estimation of the subsurface using EMI measurements, with the aim of understanding the near-surface groundwater temperature evolution. We test two different inversion methodologies to estimate horizontally layered EC models. The first, using a search in a pre-computed and stored database containing a discrete version of the full solution space finds the model that fits the data best in the least squares sense. The second, using a gradient descent optimization. We tested the method using different numerical scenarios. We show that for a horizontally 3-layered model using a gradient descent optimization might be insufficient to obtain an accurate estimate. Moreover, we demonstrate that the in-phase part of the measurement contains information about the electrical conductivity that might be useful to include in the estimation. Finally, our results give insight into the challenges and limitations of the estimation of EC horizontally layered models using frequency domain EMI data in the context of geothermal operations in the Netherlands.

How to cite: Carrizo, M., Werthmüller, D., and Slob, E.: The potential of the electromagnetic induction method to monitor temperature changes in the near-surface, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9665, https://doi.org/10.5194/egusphere-egu24-9665, 2024.

Recent years have witnessed an alarming increase in quick-clay landslides in Nordic countries, such as in Alta (Norway), Gjerdrum (Norway), and Stenungsund (Sweden), resulting in substantial damages and loss of lives. This study focuses on the application of geophysical methods, particularly the Controlled-source Radio-magnetotelluric (CSRMT) technique, to understand the characteristics and model the geometry and possibly dynamics of quick clay in these regions. The CSRMT method, combines Radio-magnetotelluric (RMT) and Controlled-source Magnetotelluric (CSMT) techniques and offers an innovative approach for investigating the electrical resistivity of subterranean structures, crucial for identifying quick clay zones. The Rissa region in Norway provides a unique opportunity for this research due to its historical context and existing infrastructure for geophysical studies. The catastrophic Rissa landslide of 1978 led to an extensive national quick clay mapping initiative, forming the basis for this study. We have also collected Distributed Acoustic Sensing (DAS) data at Rissa, intending to integrate it with CSRMT data for comprehensive analysis.

Borehole analyses at the Rissa site reveal a relatively simple stratigraphy with a flat terrain and a marine clay stratum about 20 meters thick. Quick clay layers, identifiable due to their higher electrical resistivity compared to marine clays, are sandwiched in borehole samples. Our study utilizes (CS)RMT to model these layers and assess the impact of seasonal variations on their characteristics. Data collection involved a 250-meter long CSRMT profile with a 10-meter station spacing conducted in both summer and winter seasons. The EnviroMT instrument from Uppsala University was used for data acquisition. This time-lapse approach was critical to study the resistivity differences due to seasonal variation at the quick clay site.

Results from modelling the CSRMT data show a four-layer model including L1-L4. L2 appeared thicker in winter, possibly due to reduced freshwater. Conversely, in some locations, L2 appeared thicker. These findings show that CSRMT data can distinguish resistivity differences at a quick-clay site due to seasonal variations. This research offers significant insights into the modelling of seasonal variations of the resistivity related to changes in the water content which in turn might lead to development of areas with quick clays. The integration of CSRMT and DAS data presents a novel approach to studying these phenomena, potentially aiding in better understanding and predicting quick-clay landslide triggering. The findings are not only crucial for academic research but also have profound implications for infrastructure planning and disaster management in regions prone to quick-clay landslides.

How to cite: Wang, S., Landrø, M., Bastani, M., Duffaut, K., Johansen, S. E., and Rørstadbotnen, R. A.: Investigating Quick Clays in Norway Using CSRMT Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20520, https://doi.org/10.5194/egusphere-egu24-20520, 2024.