We invite contributions focused on imaging and characterising the continental lithosphere at scales ranging from regional to local, using geophysical array and profile data. Studies that integrate multiple datasets—such as electromagnetic surveys, magnetotellurics, seismic tomography and reflection, distributed acoustic sensing, gravity, magnetics, geoid, and heat flow—are particularly encouraged. We also welcome research that combines geophysical data with geological, geochemical, mineralogical, and petrophysical approaches to provide a holistic understanding of lithospheric processes.
This session will highlight advances that inform the discovery and sustainable development of critical minerals, natural hydrogen, and geothermal resources, ultimately contributing to a secure and low-carbon energy future.
Understanding the whole system from the mantle to the surface is required to produce accurate subsurface models for geothermal resource assessment, resource exploration, hazard assessment and the understanding of tectonic processes. Variations in lithology and the associated thermal parameters will influence the subsurface thermal structure, which is one of the key parameters for geothermal exploration. This information can be difficult to obtain in areas with limited deep boreholes that directly sample subsurface lithology and physical properties (e.g. temperature). Furthermore, subsurface temperature signals are intertwined with other variables, requiring approaches to separate the individual contributions within overlapping datasets. One way to achieve this is by utilising complementary datasets such as laterally continuous geophysical datasets (primarily passive seismic), thermal conductivity and heat production, and inverting directly for subsurface temperature with a joint geophysical-petrological inversion.
We use Ireland to test the methodology within the crust and lithospheric mantle, both for the full island and local scale. Ireland has 32 deep (>1 km) boreholes, which are unevenly distributed across the island and have variable quality temperature measurements. In contrast, Ireland has abundant indirect geophysical measurements from seismic, magnetotelluric and gravity data. The output from the inversion includes the lithospheric geotherm, lithospheric thickness and Moho depth, as well as crustal structure parameters such as seismic velocity, density and radiogenic heat production. The resulting temperature models agree well with the existing borehole temperature data and provide information for areas with fewer direct measurements. In addition, the inversion outputs offer insights into the lithological and compositional variations within the crust. We further develop the workflow by incrporating lithological boundaries from detailed 3D subsurface models.
How to cite: Chambers, E. L., Fullea, J., Kiyan, D., Owusu, B., and Bean, C. J.: Investigating geothermal potential with limited direct measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5714, https://doi.org/10.5194/egusphere-egu26-5714, 2026.
The tungsten and copper deposits in the northern Jiangxi, China are formed in an intraplate environment, with obvious structural mineralization zoning and prominent coexistence and separation enrichment patterns. It is a “natural laboratory” for understanding the intraplate mineralization. However, the understanding of deep crust mantle interactions regulate shallow tectonic-magmatic-mineralization responses in the study area is still insufficient. An array with 144 broad-band magnetotelluric data this important metallogenic region has been completed to find some possible clues to the metallogenesis of copper and tungsten in northern Jiangxi. The inverted resistivity model from 3-D inversion refines that the lithosphere beneath northern Jiangxi is mainly characterized by high-resistivity, but with an approximately, southeast trending high-conductivity zone that occurs beneath the Ganjiang fault. This high-conductivity zone delineates a lithospheric delamination zone, which is localized on a multiply reactivated ancient plate boundary. There is a southeast trending trans-crustal high-conductivity anomaly beneath the Dahutang tungsten deposits, which is connected to the delamination zone. However, the Dexing copper deposits on the east side is mainly characterized by high-resistivity and lacks a high-conductivity channel similar to that connecting the deep mantle. We speculate that this structural difference is likely a deep-seated controlling factor for the zoning of tungsten and copper deposits. This work provides electrical constraints for the deep processes of massive copper and tungsten mineralization in an intraplate environment.
This work was funded by the China Magnetotelluric Array National Science and Technology Major Project (2024ZD1000204), National Natural Science Foundation of China (42130811, 42304090 and 42374097), the Science and Technology Project of Jiangxi Province (DHSQT42023001 and 20242BAB2014) and by Autonomous Deployment Project of the National Key Laboratory of Uranium Resources Exploration-Mining and Nuclear Remote Sensing (2024QZ-TD-15, 2025QZ-YZZ-03).
How to cite: Yu, H., Deng, J., Wen, Y., Chen, H., and Du, D.: Lithosphere electrical structure and its implications for the metallogenesis of copper and tungsten in northern Jiangxi, China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8928, https://doi.org/10.5194/egusphere-egu26-8928, 2026.
In geophysical inversion problems, the model–data misfit between the theoretical responses f(m) and observational data d is quantified by a data misfit function: Φ(m) =Cd1/2(d- f(m))22, and the inverse problems are inherently non-unique.
To reduce the non-unique, regularization is commonly introduced by adding structural constraint terms that favor smooth models consistent with the data and a prescribed error tolerance. This leads to the minimization of an augmented objective function, Φ(m) =Cd-1/2(d- f(m))22+λCm-1/2(m-m0)22. However, such approaches may suppress legitimate model variability and fail to adequately characterize the inherent non-uniqueness of geophysical inverse problems. Bayesian inversion provides a probabilistic framework to address these challenges by characterizing the posterior probability distribution p(md) through the combination of data likelihood p(dm) and prior information p(m) , p(md)∝ p(dm) p(m), with p(dm) ∝exp[-Φ(m)]. The posterior distribution can be efficiently explored using reversible-jump Markov chain Monte Carlo (rj-MCMC) methods, which allow both model parameters and model dimensionality to be inferred from the data.
This study examines the impact of smoothing-based structural constraints on two-dimensional magnetotelluric (MT) inversion through a comparison of conventional regularized and Bayesian approaches, using a wavelet-domain, tree-based trans-dimensional MCMC sampling. Two numerical examples are designed to systematically examine the effects of smoothing-based regularization. In the first example, a synthetic model with anomalies of varying sizes and burial depths is used to compare a Bayesian inversion constrained only by model parameterization and weakly informative priors, without smoothness-based regularization, with a conventional nonlinear conjugate gradient (NLCG) inversion that enforces structural constraints through regularization. In the second example, a single high-conductivity anomaly is inverted to directly compare Bayesian inversions without and with regularization-based structural prior information, where the structural prior is explicitly introduced through smoothness constraints. The structural prior can be expressed as :pstructure(m)=(1/2πλ2)-Mexp[-λ(Cm-1/2(m-m0)22)].
Results from the first example show that the NLCG inversion produces a smooth conductivity model in which the recovered anomalies are larger than the true anomalies, reflecting the strong influence of smoothness regularization. In contrast, the Bayesian inversion recovers the main anomaly locations while yielding rougher boundaries and a background field that is no longer uniformly smooth, indicating that multiple model realizations are consistent with the observed data. While the NLCG solution provides a stable and easily interpretable model, it may underestimate uncertainty, whereas the Bayesian inversion without regularization-based structural priors offers a more complete characterization of model non-uniqueness through marginal probability density distributions. In the second example, introducing smoothness-based structural priors within the rj-MCMC framework produces smoother posterior samples with reduced uncertainty and improved convergence stability, but at the cost of diminishing the relative contribution of the data in constraining the solution.
Overall, our results demonstrate that prior information plays a critical role in Bayesian MT inversion. While structural priors can reduce non-uniqueness and improve convergence in high-dimensional problems, they must be selected with caution to avoid excessive prior-driven bias when interpreting real data.
How to cite: Tian, S., Guo, R., Liu, J., Wang, Y., Li, J., and Bodin, T.: Regularization-Based Structural Constraints in Two-Dimensional Magnetotelluric Inversion: Implications for Non-Uniqueness and Uncertainty, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7932, https://doi.org/10.5194/egusphere-egu26-7932, 2026.
The Xiangshan volcanic basin in South China hosts the world’s third-largest volcanic-type uranium deposit. However, the deep structural framework of the caldera-collapse system and its coupling with mineralization remain poorly constrained. We perform resistivity-model–constrained 3D joint inversion of gravity and magnetic data and apply derivative-based edge detection to enhance imaging of shallow structural boundaries. The recovered density and magnetic-susceptibility models reveal two steep, deeply rooted collapse columns that coincide with volcanic conduits, with a dominant eastern column and a smaller western one. Collapse-related low-density zones extend to depths exceeding 2 km, indicating that magma withdrawal caused depressurization and roof instability that drove multi-center, piecemeal subsidence. Segments of the ring-fault belt closely coincide with belt-like granitic-porphyry emplacement, suggesting that the collapse framework remained permeable after collapse and was repeatedly exploited by subvolcanic magma and hydrothermal fluids. In the northern basin, tight conduit–ring-fault coupling aligns with intense alteration and uranium occurrences, implying more efficient ascent and local focusing of mineralizing fluids, whereas weak shallow alteration above large southern intrusions suggests that prospective targets in the south may lie deeper, within granitic-porphyry bodies, along deeper ring-fault continuations, and at intersections with basement faults.
This work was funded by the National Natural Science Foundation of China (42130811, 42304090, and 42374097), the Autonomous Deployment Project of the National Key Laboratory of Uranium Resources Exploration-Mining and Nuclear Remote Sensing (2025QZ-YZZ-03 and 2024QZ-TD-15), and the Science and Technology Project of Jiangxi Province (20242BAB20143).
How to cite: Deng, J., Feng, M., Yu, H., Chen, H., and Yuan, C.: 3D imaging of the caldera-collapse system: implications for uranium mineralization in the Xiangshan volcanic basin, South China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9436, https://doi.org/10.5194/egusphere-egu26-9436, 2026.
The Yidun Island Arc, located along the eastern margin of the Tibetan Plateau, represents a Late Triassic active continental margin arc closely associated with the subduction–collision evolution of the Paleo-Tethys Ocean (Fig. 1). Although previous geochemical and geophysical studies have revealed the presence of deep-seated thermal and material anomalies beneath this region, significant controversies remain regarding the geometry of the subducting slab, the pathways of mantle upwelling, and their coupling with mineralization processes. Gravity anomalies are highly sensitive to subsurface density variations and therefore provide direct constraints on deep structures. In this study, we construct a lithospheric density model of the Yidun Island Arc based on three-dimensional gravity inversion, with a focus on resolving the deep structural characteristics of the suture zone and the associated mantle flow patterns.
Fig. 1: Tectonic–geomorphological features and regional setting of the Yidun Island Arc. (a) Topography and distribution of major tectonic units in the Yidun Island Arc. LCJF, Lancangjiang Fault; NJF, Nujiang Fault; JSJFZ, Jinshajiang Fault Zone; JQF, Jinhe–Qinghe Fault; MYF, Mopan Mountain–Yuanmou Fault; ANHF, Anninghe Fault; LTF, Litang Fault; DLSF, Daliangshan Fault. (b) Geographic location of the Yidun Island Arc within the Tibetan Plateau and surrounding tectonic framework.
This study utilizes high-precision gravity data to construct a lithospheric density model for depths of 0–150 km beneath the study area by removing the Moho effect and performing three-dimensional gravity inversion. The results indicate that: (1) pronounced high-density anomalies (Δρ ≈ +0.02–0.03 g/cm³) occur beneath the Jinsha River Suture (~99°E) and the Ganzi–Litang Suture (100–100.5°E), extending to depths of ~120 km. These anomalies are interpreted as remnants of eclogitized slabs formed during westward subduction of the Paleo-Tethys Ocean; (2) a near-vertical low-density channel (Δρ ≈ −0.08–0.12 g/cm³; width ~50–100 km) is developed between the two sutures, extending continuously from the asthenosphere to the lower crust. This channel spatially coincides with low-velocity zones revealed by seismic tomography and high-conductivity anomalies identified by magnetotelluric data (Fig. 2), suggesting a mantle channel flow induced by blockage from the rigid Yangtze Block; (3) the low-density channel shows strong spatial overlap with the porphyry–skarn Cu–Mo polymetallic mineralization belt in the southern Yidun Island Arc, indicating that deep mantle upwelling provided essential thermal input and fluid sources for shallow ore-forming systems.
This study provides the first direct geophysical evidence, from the perspective of three-dimensional density structure, for westward subduction polarity and a mantle channel flow-controlled metallogenic model in the Yidun Island Arc, thereby advancing our understanding of the coupling between deep geodynamic processes and shallow mineralization in the Tethyan collisional belt.
Fig. 2: Vertical slices of three-dimensional density structure derived from gravity inversion. (a) Locations of three vertical profiles in different orientations; (a.1)–(a.3) Density anomaly sections along profiles AA′, BB′, and CC′ obtained from gravity inversion; (b.1)–(b.3) Density anomaly sections along profiles AA′, BB′, and CC′ converted from seismic velocity models.
Finally, we would like to express our special gratitude to the National Natural Science Foundation of China (Grant No. 4223031) for the financial support of this paper.
How to cite: Zhang, Y., Yang, J., Wang, X., Xu, Z., and Jiang, P.: Deep Lithospheric Density Structure and Tectonic Significance of the Yidun Island Arc in the Tethyan Tectonic Domain: Evidence from 3D Gravity Inversion, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11291, https://doi.org/10.5194/egusphere-egu26-11291, 2026.
The Geological Survey of Norway has collected frequency-domain electromagnetic and gamma-ray spectrometric data through airborne mapping since 1972 on the mainland of Norway. These data were acquired and processed using the technologies available at the time of the campaigns. Consequently, the resolution and quality of individual surveys vary across the country.
Over the years, helicopter-borne frequency-domain electromagnetic data were acquired using various instruments with up to five different frequencies. While today, these data are inverted to determine apparent resistivity using a half-space earth model, inversion has not been consistently carried in the past. To build a homogeneous compilation, we are re-processing and inverting all existing data using modern levelling and noise-reduction tools. By limiting instrumental and environmental noise, we create a country-scale map of conductors. For each frequency, apparent resistivity data from all surveys are merged into a single, seamless compilation.
The primary objective of reprocessing and compilation is to recover the maximum amount of legacy airborne data and produce a uniform coverage map. This unique compilation serves as a crucial tool for identifying conductors in evaluating mineral resources and for general bedrock mapping. The location and continuity of conductive structures are interpreted, across survey boundaries, providing critical insights into the deeper sources of mineral systems at a regional scale.
Similarly, gamma-ray spectrometric data were collected using different instruments with varying detector volumes, leading to discrepancies in large-scale resolution. Presently, these data are corrected for live time, cosmic and aircraft background removal, radon removal, Compton stripping, and height attenuation following the International Atomic Energy Agency (IAEA) recommendations. Since 2002, the final products are the ground concentration for potassium, uranium and thorium. Prior to this, window counts of gamma rays for each respective radioelement were reported. A homogeneous compilation is obtained after a careful data re-processing including noise reduction, levelling and calibration. For surveys where original calibration parameters are missing, data from neighbouring overlapping surveys allow to derive the ground concentration. The final compiled ground concentration maps provide geochemical insight about the top half meter of the ground. Combined with electromagnetic data, links between surface lithology and deeper sources can be studied.
Airborne geophysical operations are capital-intensive. A standardized and homogeneous re-processing of frequency-domain electromagnetic and gamma-ray spectrometric data maximizes the value of Norway’s existing geophysical assets. These new regional datasets will contribute to mineral exploration, effective bedrock mapping, and societal safety by identifying natural and anthropogenic radioactivity, quick clay and rock instabilities, and other environmental hazards.
How to cite: Dumais, M.-A., Baranwal, V., Kristiansen, T., Ofstad, F., Stampolidis, A., and Brönner, M.: Country-scale airborne electromagnetic and gamma-ray spectrometric data for mapping sustainable resources in Norway, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11599, https://doi.org/10.5194/egusphere-egu26-11599, 2026.
To contribute to a secure and low energy carbon future, the InGEO project (Innovation in GEOthermal resources and reserves potential assessment for the decarbonization of power/thermal sectors) seeks to develop an innovative exploration workflow for combining muti-parameter datasets that will help reduce the risks associated with geothermal energy exploitation. The chosen area for the application is the Northern Apennine buried - structures belonging to the Romagna and Ferrara Folds (RFF), Eastern Po Plain (Italy). There, a mapped thermal anomaly was interpreted to be the effect of deep fluids circulation within the deep-seated Mesozoic carbonate sequences (e.g., Pasquale et al., 2013). As part of the workflow, we first developed a consistent geological/geophysical model of the RFF region. The model integrated data from over 200 seismic surveys from the VIDEPI database (www.videpi.com), 700 deep (>1500 m) boreholes (CNR database, www.geothopica.igg.cnr.it), 160 sonic and lithological logs (Livani et al. 2023), recent seismic tomography models (e.g., Brazus et al. 2025; Kästle et al., 2025), and new density models, obtained from the inversion of the the first pan-Alpine surface-gravity database (Zahorec et al., 2021). The Kingdom Suite was used to interpret the 2D seismic lines and well log data, while clustering algorithms (K-means and Fuzzy c-means) were chosen to classify the seismic tomography and density dataset. The results consist of a 3D architecture of shallow and deep geological features of the study region. Shallow features (up to a depth of ~15 km) included eight horizons, ranging in age from the Quaternary to the Permian. Deep features (between ~15 and 50 km depth) included the basement, the upper crust and the Moho depths. The geological/geophysical model was further validated by utilizing thermo-physical measurements on rocks, also obtained as part of the InGEO project (Sulpski, 2025), high temperature and pressure laboratory data on rocks, complied from the literature (Burke and Fountain, 1990; Christensen and Mooney, 1995), and sonic log data, obtained from oil and gas wells, drilled in the RFF region (Livani et al. 2023). Furthermore, a comparison with the temperature data on wells provided a preliminary evaluation of the resource potential of the RFF region. The workflow will further entail a more rigorous assessment of the geothermal energy potential of the region, by implementing a numerical simulation, which uses as main input the consistent geological/geophysical model. The workflow of InGEO project will be also used as a decision support system for developing future geothermal projects in Italy.
Acknowledgments
InGEO is a PRIN 2022 PNRR Project and has received funding from the European Union, Next Generation EU.
References
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Slupski et al., 2025. 43° National Conference GNGTS, Bologna, 11-14 February 2025.
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How to cite: Basant, R. A., Cortassa, V., Tesauro, M., Gola, G., Nanni, T., Slupski, P. M., Galgaro, A., and Manzella, A.: Towards a multiscale geophysical approach for the evaluation of the geothermal energy potential of the Eastern Po Plain (Italy), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13255, https://doi.org/10.5194/egusphere-egu26-13255, 2026.
Thermal conductivity, heat capacity, and thermal diffusivity control subsurface temperature and heat-flow estimates and are key inputs for geothermal exploration and basin-scale thermal modelling. In practice, these properties are rarely available as continuous depth profiles because laboratory measurements require core material and are typically sparse. We present an extended thermo-profiler workflow that predicts continuous thermal property profiles directly from standard wireline logs and provides uncertainty-aware outputs for downstream geothermal and heat-flow applications. Thermo-profiler uses multivariate statistics or machine-learning models trained on physically modelled synthetic datasets representing realistic mineralogical and porosity variations in common sedimentary lithologies. The workflow learns relationships between thermal properties and routinely available logs (e.g., sonic velocity, density, neutron porosity, gamma ray). Multiple prediction models and log combinations are evaluated, enabling robust predictions even when only a subset of logs is available and allowing automated model choice based on the input data of a given borehole. Validation with independent laboratory core measurements shows that prediction performance improves with log coverage and with formation-scale averaging. For thermal conductivity, uncertainties are commonly within the ~10–30% range at sample scale, while interval means can be constrained substantially better for larger stratigraphic units. Heat capacity is predicted with higher accuracy in the best-performing models, and thermal diffusivity uncertainties follow are derived from the combined conductivity and heat-capacity predictions. We illustrate application examples where thermo-profiler outputs are used to generate thermal property profiles for wells in sedimentary settings and to provide consistent inputs for conductive 1D temperature and heat-flow modelling, including geothermal screening in data-limited settings. The workflow is implemented as an automated, FOSS Python package (thermo-profiler) to support reproducible thermal characterization from legacy and modern wireline datasets.
How to cite: Fuchs, S., Dergunova, V., Salis Gross, E., Frick, M., and Norden, B.: Thermo-profiler: Automated Thermal Property Prediction from Routine Wireline Logs in Sedimentary Basins, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14537, https://doi.org/10.5194/egusphere-egu26-14537, 2026.
India’s energy demand is increasing rapidly due to its urbanization and economic growth, which necessitates a multi-source energy adaptation, as outlined in the first rule of India’s energy governance. At the same time, India has set a target to achieve net-zero emissions by 2070, which has already led to a shift in policy toward renewable energy resources. The recent launch of the National Policy on Geothermal Energy has transitioned India's nascent geothermal market from an exploratory stage to a structured framework ready for tapping its estimated potential. Despite a long-standing exploratory study of Indian geothermal resources, there is less agreement on the definitive estimate of the current reported potential that requires further research. The current study provides an up-to-date assessment of the country's geothermal surface manifestations and subsurface heat flow. The latest data from the Geological Survey of India reports 381 surface manifestations, including hot springs and geysers. The spatial distribution of these surface features has been mapped within 10 geothermal provinces of India to provide the latest map of India’s geothermal provinces. We have generated the latest Heat-Flow map of peninsular India with the latest borehole data available from the International Heat Flow Commission. The results of this study reveal that most surface geothermal manifestations in India are located along the tectonically active zones. Most of the elevated heat flow regions also follow the same pattern. In addition to tectonically active zones with deep extended faults (i.e., Himalayan Province), radiogenic heat sources (i.e., Ladakh Batholits), hot sedimentary basins (i.e., Cambay and Assam basins), and shallow magma chambers (i.e., Andaman Island) are the major sources of India’s geothermal resources. This study further suggests an exploratory investigation into the enhanced geothermal system, which is expected to be more promising, with an approximate potential of 14 terawatt-hours of electricity.
How to cite: Daqiq, M. T. and Sharma, R.: Geothermal resources of India: A country update from surface manifestation to subsurface heat flow, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19203, https://doi.org/10.5194/egusphere-egu26-19203, 2026.
Understanding the thermal and compositional structure of the Antarctic lithosphere is fundamental for assessing its tectonic stability, geodynamic evolution, and mantle processes beneath East and West Antarctica. However, interpretations based on single geophysical observables remain highly non-unique due to the coupled effects of temperature and composition on seismic velocity and density. Here we present a multi-physics framework that integrates gravity, seismic velocity, heat flow, and thermodynamic modeling to derive high-resolution density, temperature, and compositional models of the Antarctic lithosphere and lithospheric mantle.
We first perform a three-dimensional parallel gravity inversion constrained by seismic shear-wave velocity structure, using a structurally coupled objective function that combines data misfit, model regularization, and Gramian-based structural consistency. Structural similarity between density and velocity is enforced in the mantle, where seismic constraints are strongest, while thermally corrected density relationships are incorporated within the crust. The inversion is accelerated through a matrix-free implementation with CUDA-enabled forward and adjoint modeling and MPI–GPU parallelization, enabling continental-scale imaging at a resolution of 5 km × 5 km.
The resulting absolute density model reproduces observed Bouguer gravity anomalies with low residuals and reveals pronounced lateral heterogeneity across Antarctica. Building on these results, we further decouple temperature (T) and composition (Mg#) in the upper mantle through joint simulation of seismic velocity and density. Forward models are constructed using Gibbs free energy minimization with Perple_X, incorporating phase equilibria, anelastic attenuation, and rheological effects. A probabilistic grid-search approach with Monte Carlo uncertainty analysis enables robust estimation of T and Mg# and identification of regions where standard solid-state physics fails to explain observations.
Our results indicate a cold, thick, and chemically depleted lithospheric root beneath East Antarctica, consistent with a stable cratonic mantle, while West Antarctica is characterized by elevated temperatures, fertile compositions, and widespread regions exceeding solid-state limits, suggesting active asthenospheric upwelling and possible decompression melting beneath the West Antarctic Rift System. This study demonstrates the power of integrated geophysical–thermodynamic approaches for resolving the thermo-compositional state of continental lithosphere.
How to cite: Zuo, Z., Tu, X., Ji, F., and Di, Q.: Thermal and Compositional Architecture of the Antarctic Lithosphere Revealed by Integrated Gravity–Seismic Imaging, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23041, https://doi.org/10.5194/egusphere-egu26-23041, 2026.
The central–southern Greater Khingan Range (GKAR) is a key polymetallic metallogenic region in China, hosting major deposits such as the Baiyinchagan and Weilasituo deposits (Fig. 1). Tectonically, it lies in the eastern Central Asian Orogenic Belt and has been shaped by the closure of the Paleo-Asian Ocean and Paleo-Pacific Ocean and the ongoing subduction of the Pacific Plate. The region is transected by major deep faults, including the northern boundary of the North China Craton, and the Solonker–Xar Moron fault.
In July 2025, a joint team from the Inner Mongolia Geologic Survey and Research Institute and Zhejiang University acquired 77 broadband magnetotelluric (MT) sites, Each site observed for more than 40 hours, yielding high-quality responses with periods up to 5000 s. A 3D inversion using ModEM produced a preliminary lithospheric resistivity model.
The model reveals high-resistivity bodies beneath the GKAR axial fault down to ~60 km, likely reflecting intense east–west compressional metamorphism. Two dominant low-resistivity anomalies are identified: C1 is situated in the southeastern part of the study area and at depths exceeding 80 km, whereas C2 is located in the northwestern part at a shallower depth but exhibits good connectivity with C1 (shown in Figs. 2 and 3). These features are spatially consistent with localized low-velocity upwellings and regions of moderate-to-high heat flow. We infer that mantle-derived melts and fluids, possibly sourced from Pacific Plate subduction, underlie the region’s metallogeny. The spatial linkage between deep fluid migration and shallow ore systems requires further investigation.
This study was supported by the National Science and Technology Major Project for Deep Earth Exploration and Mineral Resources Exploration(2024ZD1000200) and the National Natural Science Foundation of China (42474103).
Figure 1: Overview map of the study area. White inverted triangles denote MT sites, large orange circles represent major mineral deposits, labeled with numbers as follows, 1: Weilasituo Polymetallic Deposit (WLST), 2: Hegerao La (HGL), 3: Hegen Shan (HGS), 4: Baiyinchagan Pb-Zn-Ag Deposit (BYCG), 5: Zhalageamu Cu Deposit (ZLGM), 6: Daolundaba Cu-W-Sn Deposit (DLDB), 7: Shuangjianzi Shan Ag-Pb-Zn Deposit (SJZ), 8: Baiyinnuo Pb-Zn Deposit (BYN), 9: Haobugao Polymetallic Deposit (HBG), 10: Maodeng-Xiaogushan North Sn-Cu-Zn Deposit (MD-XGSN), 11: Baiyinchagan Dongshan Ag-Sn Polymetallic Deposit (BYCG-DS). Small yellow circles indicate minor mineral occurrences. Black dashed lines show the locations of resistivity profiles, with endpoints of profiles P1 to P4 marked accordingly. Red solid lines represent faults. GKAR: Greater Khingan Range, XMF: Xar-Moron Fault.
Figure 2: Resistivity profiles, red inverted triangles denote MT sites near profiles.
Figure 3: 3D Resistivity model, green surface is 70 Ωm isosurface.
How to cite: Wu, X., Yang, B., Meng, X., Wen, G., and Jiang, L.: Preliminary lithospheric electrical structure of Southern Greater Khingan Range, North China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2507, https://doi.org/10.5194/egusphere-egu26-2507, 2026.
How to cite: Yun, Z., An, Z., Di, Q., and Ren, Z.: Study on the Deep Electrical Structure and Metallogenic Coupling Mechanism of the Metallogenic Belt in the Eastern South China Block: Evidence from Aeromagnetic Data and Magnetotelluric Sounding, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8589, https://doi.org/10.5194/egusphere-egu26-8589, 2026.
Critical and strategic raw materials have emerged as a focal point of global interest and are increasingly embedded in geopolitical competition among major powers. The European Union (EU) has been identified as reliant on external suppliers for these minerals and has undertaken a range of measures to mitigate this dependency.
As part of this initiative, Siikalatva graphite as a raw material for the green transition project has been launched in Central-Finland by the municipality of Siikalatva, the University of Oulu, and the Geological Survey of Finland (GTK). The project is funded by the EU’s Just Transition Fund (JTF) for 2024–2026, with the objective of mitigating the adverse impacts associated with the transition to a low-carbon economy. The project aims to achieve this goal by supporting the regions and employees most affected by the transition and by promoting a balanced socioeconomic transformation.
The main goal of this project is to investigate the flake graphite potential in a small municipality (Siikalatva) in Central Finland. Flake graphite is, after all, a critical raw material e.g., in battery production. Flake graphite occurs in a Paleoproterozoic metasedimentary environment that has undergone high-grade metamorphism, which increases the size of the graphite flakes. Graphite exhibits strong geophysical conductivity and is frequently associated with iron sulfides, including pyrrhotite and pyrite.
In this presentation, we will examine the geophysical research opportunities offered by the GTK’s geophysical and geological data sets for assessing graphite potential in the municipality of Siikalatva. This also serves as a good example of the possibilities and long-term usefulness of geodata for various purposes.
By processing geophysical data from a high-grade metamorphic area, we can delineate graphite-rich zones and further classify them into sulfide-poor and sulfide-rich types. These zones can then be presented as potential areas and integrated with other land-use planning, existing infrastructure, settlements, and tourism. Similarly, integrating this information with, for example, groundwater resource data provides valuable insights for the potential utilization of flake graphite.
This study provides the municipality of Siikalatva an opportunity to assess the perspectives and potential that the graphite in its area may offer. At the same time, it supports land-use planning and decision-making. The results of the project will be published in the final report in 2026.
How to cite: Konnunaho, J., Lahti, I., Rauhala, A., and Tuomela, A.: Assessment of Graphite Potential in the Siikalatva Area, Central Finland, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3275, https://doi.org/10.5194/egusphere-egu26-3275, 2026.
The eastern part of the Sofia Basin hosts proven geothermal occurrences documented by borehole data and elevated temperature gradients; however, the geometry, depth extent, and structural controls of the geothermal system remain poorly constrained. Although geological information and drilling provide important local constraints, an integrated understanding of the geothermal system in the eastern Sofia Basin and its relationship to major structural elements is still lacking.
In 2025, a geophysical investigation was carried out in the eastern Sofia Basin within the tasks of the Geotherm Pro project, funded by the Bulgarian National Recovery and Resilience Plan, complementing existing geological and borehole data. These investigations include ground magnetic surveys, seismic (H/V) measurements, electrical resistivity methods, and the first modern magnetotelluric (MT) survey targeting geothermal systems in this part of the basin.
Magnetic and electrical resistivity methods are primarily used to constrain the layered subsurface structure and resistivity contrasts, contributing to the definition of major lithological units. Seismic H/V measurements further constrain sediment thickness and basin geometry. Magnetotellurics is therefore applied as the key method to investigate deep conductivity variations, the spatial extent and geometry of the geothermal system in the eastern Sofia Basin, and the potential role of faults as fluid pathways. MT data acquisition has been completed, and processing and inversion are currently ongoing. Urban electromagnetic noise represents a significant challenge in the study area and is explicitly addressed during data processing and interpretation.
This contribution presents the conceptual framework, survey design, and integration strategy for an exploratory geological and geophysical procedure, aiming to advance the understanding of geothermal systems in the eastern Sofia Basin and to support future geothermal assessment and utilisation.
How to cite: Hristova, D. and Trifonova, P.: Geophysical investigation of geothermal systems in the eastern Sofia Basin, Bulgaria: integrating existing knowledge with new geophysical data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5162, https://doi.org/10.5194/egusphere-egu26-5162, 2026.
The southeastern margin of the Tibetan Plateau serves as a key pathway for the outward extrusion of plateau material. The deep crust–upper mantle structure and associated material transport processes in this region are therefore crucial for understanding the mechanisms of tectonic deformation of the plateau. The southern part of the Dianzhong secondary block is located at the junction of the Red River Fault Zone and the Xiaojiang Fault Zone, where tectonic activity is particularly intense. However, existing magnetotelluric (MT) studies in the Dianzhong block have mainly focused on its central and northern sectors, while the three-dimensional lithospheric electrical structure of the southern part and its implications for deep material transport remain poorly constrained.
In this study, a three-dimensional MT investigation was carried out in the southern Dianzhong secondary block to image the electrical structure of the crust and upper mantle and to explore its tectonic significance. A total of 105 MT sites were deployed across the study area. Impedance tensor decomposition and phase tensor analysis were first applied to assess the dominant dimensionality and structural strike of the subsurface. The results indicate that the middle to deep crust is characterized by strong three-dimensional features, supporting the application of three-dimensional inversion.
Three-dimensional MT inversion based on a nonlinear conjugate gradient algorithm was subsequently performed, yielding a resistivity model down to a depth of approximately 80 km. The reliability of the major low-resistivity anomalies was further evaluated through sensitivity tests. The inversion results reveal a complex electrical structure in the upper and middle crust, with high- and low-resistivity bodies distributed in an interlaced pattern. Shallow low-resistivity anomalies show a clear spatial correlation with major active faults in the region.
At greater depths, a prominent low-resistivity anomaly extends from the lower crust into the upper mantle and exhibits a noticeable change in geometry near the Moho. Beneath the Xiaojiang Fault Zone, a low-resistivity channel that crosses the Moho is identified. In combination with regional geological and tectonic information, this deep low-resistivity structure is interpreted to represent a pathway for the ascent of thermal material or fluids controlled by deep-seated fault systems. These results provide new electrical constraints on deep material transport processes beneath the southeastern margin of the Tibetan Plateau and the Dianzhong region.
How to cite: Ren, Z., An, Z., and Yun, Z.: Three-Dimensional Electrical Structure of the Southern Dianzhong Secondary Block, Southeastern Tibetan Plateau, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6333, https://doi.org/10.5194/egusphere-egu26-6333, 2026.
Rapid imaging of subsurface electrical structures is highly challenging, especially for complex geological formations. Conventional inversion algorithms require repeated solutions of large-scale forward problems, which constitute the main computational expense. To address this limitation, we have developed an underground resistivity imaging method based on the Pix2Pix Generative Adversarial Network (GAN) architecture. Our approach integrates impedance phase information with conventional apparent resistivity observations, significantly improving imaging accuracy. For training data generation, we employ Gaussian random fields to synthesize resistivity models. This practice not only enhances the geological representativeness of the data but also introduces meaningful variability that benefits the generalization capability of the GAN. By systematically comparing the prediction accuracy under different loss functions, we determined the optimal form of the loss function.
Detailed qualitative and quantitative evaluations demonstrate that our multi-parameter joint inversion strategy outperforms methods relying on only a single parameter, such as apparent resistivity or impedance phase alone. To improve the method’s robustness in practical applications, we incorporate the objective function from conventional inversion into the GAN’s loss function to handle noisy data. This geophysically constrained loss function greatly enhances the model’s noise resistance. In synthetic data experiments, compared with the Nonlinear Conjugate Gradient (NLCG) inversion method, our approach not only achieves faster prediction but also exhibits superior capability in resolving high-resistivity bodies beneath low-resistivity layers. Validation using real-world data further confirms the practical applicability and generalization potential of the proposed method.
How to cite: Gao, Y., Di, Q., Fu, C., and Zhang, Y.: Rapid imaging of subsurface media with magnetotellurics based on Pix2Pix GAN, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22622, https://doi.org/10.5194/egusphere-egu26-22622, 2026.
Deep learning methods are currently being effectively used by several geophysicists to achieve direct data-to-model mapping in magnetotelluric (MT) inversion. This method enables extremely quick inversion speeds in addition to removing the need on initial models. However, the MT method covers a broad frequency band range, and conventional deep learning inversion requires training separate networks for different frequency bands, leading to inefficiency. Here, we present a trans-scale MT inversion framework guided by the principle of physical similarity, which enables a network trained on a single frequency band to be applied across the entire MT spectrum. We first construct practical 2D smooth geoelectric models as network outputs. Using forward modeling, the apparent resistivities for the TE and TM polarization modes are calculated and used as network inputs. In order to improve network robustness, training samples also take data loss scenarios into account and incorporate random noise. A U-Net architecture based on PyTorch is developed to perform high-precision nonlinear mapping from MT data to resistivity models. Crucially, the principle of physical similarity is then applied to extend the trained network to other frequencies without retraining. Furthermore, using the network's predictions as the initial model for deterministic inversion effectively reduces the reliance on initial model selection, decreases the number of iterations, and enhances the final inversion resolution. Ultimately, by means of numerical model tests and the inversion of MT data from the Tamusu region in Inner Mongolia, we verify the efficacy of this inversion technique, offering useful perspectives and pointers for the implementation of intelligent MT inversion.
This work was funded by the National Natural Science Foundation of China (42130811, 42374097 and 42304090), Autonomous deployment project of National Key Laboratory of Uranium Resources Exploration-Mining and Nuclear Remote Sensing (2025QZ-YZZ-03 and 2024QZ-TD-15) of East China University of Technology, and by the Science and Technology Project of Jiangxi Province (DHSQT42023001, 20242BAB20143 and 20204BCJL23058).
How to cite: Chen, H., Yuan, C., Deng, J., Yu, H., Gui, T., and Yin, M.: Trans-Scale Magnetotelluric Inversion via Deep Learning Guided by the Principle of Physical Similarity and Application, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9602, https://doi.org/10.5194/egusphere-egu26-9602, 2026.
