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) =C_d^1/2(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) =C_d^-1/2(d- f(m))²₂+λC_m^-1/2(m-m₀)²_2. 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 :p_structure(m)=(1/2πλ²)^-Mexp[-λ(C_m^-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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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.