The Scandinavian Mountains span c. 1500 km and broadly follow the trace of the Scandinavian Caledonides, formed during the collision between Baltica and Laurentia. While the formation of this orogen is well established, the origin and timing of the present-day topography remain debated. This study investigates the structure and evolution of the deep-seated masses supporting the present-day topography. A comprehensive review of geological and geophysical data facilitates new integrated gravity–isostatic and thermal modelling, enabling a quantitative understanding of the interplay between the crust–Moho system (CMS) and the lithosphere–asthenosphere system (LAS). Crustal thickening from the Norwegian coast toward the Baltic Shield initially raises topography, while farther inland a thicker lithosphere subdues topography despite a thick crust. Offshore, crustal thinning persisted from the Late Mesozoic through the Paleogene. However, inland areas show no evidence of significant post-Permian crustal thinning or magmatism. These observations indicate that the underlying isostatic structure was established primarily during and in the aftermath of the Caledonian orogeny, with the modern topography representing a long-lived remnant sculpted by slow erosion and passive isostatic rebound.

We conducted comparative measurements of thermal properties of samples from nine cores of the ICDP COSC-1 borehole and four widely used rock references, using a steady-state and a transient divided-bar device, a transient plane source device, a modified & Aring;ngstr & ouml;m device, as well as two optical thermal conductivity scanners. In addition, a caloric method provided benchmark values for specific heat capacity. A complementary thin-section analysis of the COSC-1 samples allowed us to calculate specific heat capacity according to Kopp's law and thermal conductivity according to commonly used mixing models. Our results demonstrate agreement between the various test methods within $\pm 10$ per cent for about one half of the investigated samples. Furthermore, almost all results for specific heat capacity agree with the predictions of Kopp's law, though the significance of this correspondence is limited owing to large uncertainties in the experimental and theoretical values. The results for thermal conductivity fall within the most extreme theoretical bounds that account for anisotropy but for an amphibolite. Thermal anisotropy seems to contribute significantly to the deviations between results of the different transient methods that, however, cannot be reconciled by the available theoretical relations for apparent thermal conductivity of transversely isotropic materials. The combination of characteristic investigation volume of the individual methods and sample heterogeneity has to be considered responsible for variability of results, too, an issue whose clarification is calling for dedicated numerical modelling in the future, with the prospect to characterize thermal heterogeneity from observed differences.

Heat-flow data from deep boreholes are of particular value as a number of factors, including climatic surface temperature variations, may disturb subsurface thermal conditions, especially at shallow depths. This study provides thermal results from two boreholes, Gravberg-1 and Stenberg-1, drilled to large depths in the Siljan Ring impact structure, Baltic Shield, Central Sweden. Taking several high-resolution temperature logs to a depth of more than 5000 m in Gravberg-1 and close to 2000 m in Stenberg-1, and up to a long time after drilling, ensures high-quality deep temperature data. A good-quality thermal conductivity profile was determined from mineralogical composition and radiogenic heat production from a spectral gamma-ray log. The observations show overall thermal conductive profiles with local non-conductive temperature variations. Observed present-day near-surface heat flow (depth range 200-500 m) in Gravberg-1 is 47 mW/m(2) (std. dev. 1.5 mW/m(2)). From observed deep background heat flow, considering heat production, the equivalent steady-state, unperturbed heat flow is estimated at 66-68 mW/m(2). Data from Stenberg-1 provide similar results. The observed reduction of heat flow at near-surface level by about 20 mW/m(2) is interpreted as originating from long-term palaeoclimatic temperature variations. During the last glacial period, the area was, to varying degree, both ice-free and covered by the Fennoscandian ice sheet. Thermal modelling, with a ground-surface mean-temperature increase by 12 +/- 2 degrees C from glacial to post-glacial time, shows consistency between observed and modelled heat flow perturbations for the full depth range of observations. Heat flow perturbations may be significant to depths of about 2000 m. These results show that, uncorrected for palaeoclimate, depending on depths of measurements, observed continental heat flow values might be markedly underestimated. This applies to Northern Europe and, likely, to many other areas as well.

The scientific drilling project “Collisional Orogeny in the Scandinavian Caledonides” (COSC), supported by ICDP and the Swedish Research Council, involved the drilling of two vertical boreholes through carefully selected sections of the Paleozoic Caledonian orogen in Central Sweden. The main objectives of the COSC geothermal team are: a) to determine the vertical variation of the geothermal gradient, heat flow and thermal properties, and to determine the required corrections for shallow (< 1 km) heat flow data; b) to advance basic knowledge about the thermal regime of Palaeozoic orogenic belts, ancient shield areas and high heat-producing plutons; c) to improve understanding of climate change at high latitudes (i.e. Scandinavia), including historical global changes and recent palaeoclimate development (since last ice age); d) to explore the geothermal potential of the Åre-Järpen area; e) to assess to what degree the conductive heat transfer is affected by groundwater flow in the uppermost crust, and f) to determine the heat generation input and impact from the basement and the alum shales.The present contribution focuses on themes “b” and “f” and evaluates the likely paleothermal state of the lithosphere of Baltica, in the region of the COSC boreholes, at the onset of the Caledonian orogeny. We concentrated on the results obtained from COSC-1, which was drilled, fully cored and repeatedly logged for temperature down to ~2.5 km depth. Average heat generation of the penetrated Caledonian metamorphic rocks was derived from the spectral gamma ray logs. The analysis yields a low average value of 0.8 µW/m3. Thermal conductivities were determined from 105 core samples. On average, thermal conductivity equals 2.8±0.4 W/(m K), down to ~2 km depth, and increases to 4.1±1 W/(m K) in the lowermost section of the borehole. The thermal gradient shows obvious paleoclimatic disturbances but seems largely unaffected below ~2 km depth and no advective signal is detected. The calculated heat flow for the deepest section of the well amounts to ~82 mW/m2. This unusually high heat flow value for cratonic lithosphere reflects, most likely, dominant input from the underlying highly radioactive Transscandinavian Igneous Belt (TIB), which is Late Proterozoic in age. We therefore propose that the lithosphere of Baltica involving the TIB was relatively warm at the time of the Caledonian orogeny. We anticipate that the relatively high temperatures of the margin of Baltica strongly influenced deformation style.

The present contribution introduces new heat flow data from northern Norway, a region of the Baltic Shied and Scandinavian Caledonides that has been poorly covered until now. We computed heat flow values based on data gathered in ten boreholes reaching total depths ranging between -390 m and 960 m below ground level. Abundant core material for five of the studied boreholes allowed for precise determination of thermal conductivity profiles. The new determinations represent significant improvement with respect to the few pre-existing heat flow values that were based on shallow drillholes and lake measurements. The obtained heat flow values range between -40 and 70 mW/m2, after corrections, and suggest significant heat flow decrease (i.e. -10-20 mW/m2) from chiefly Proterozoic terrains to the Archean nucleus in NE Norway. The results suggest also sharp decrease in heat flow from the NE Atlantic to the Lofoten-Vesteralen margin but rather smooth gradients from the Barents Shelf to the continent. The former may be associated with abrupt deepening of the base of the lithosphere below the Lofoten-Vesteralen margin, as already suggested by previous seismic tomography studies and consistent with drastic strain focusing and the formation of a narrow margin. In contrast, gradual deepening of the base lithosphere towards the continent appears to be in agreement with the observed diffuse deformation that took place in the comparatively wide Western Barents Shelf.

As sediment accumulation indicates basin subsidence, erosion often is understood as tectonic uplift, but the amplitude and timing may be difficult to determine because the sedimentary record is missing. Quantification of erosion therefore requires indirect evidence, for example thermal indicators such as temperature, vitrinite reflectance and fission tracks in apatite. However, as always, the types and quality of data and the choice of models are important to the results. For example, considering only the thermal evolution of the sedimentary section discards the thermal time constant of the lithosphere and essentially ignores the temporal continuity of the thermal structure. Furthermore, the types and density of thermal indicators determine the solution space of deposition and erosion, the quantification of which calls for the use of inverse methods, which can only be successful when all models are mutually consistent. Here, we use integrated basin modelling and Markov Chain Monte Carlo inversion of four deep boreholes to show that the erosional pattern along the Sorgenfrei–Tornquist Zone (STZ) in the eastern North Sea is consistent with a tectonic model of tectonic inversion based on compression and relaxation of an elastic plate. Three wells in close proximity SW of the STZ have different data and exhibit characteristic differences in erosion estimates but are consistent with the formation of a thick chalk sequence, followed by minor Cenozoic erosion during relaxation inversion. The well on the inversion ridge requires ca. 1.7 km Jurassic-Early Cretaceous sedimentation followed by Late Cretaceous–Palaeocene erosion during inversion. No well demands thick Cenozoic sedimentation followed by equivalent significant Neogene exhumation. When data are of high quality and models are consistent, the thermal indicator method yields significant results with important tectonic and geodynamic implications.

Svalbard is a High Arctic Archipelago at 74-81 degrees N and 15-35 degrees E under the sovereignty of Norway. All settlements in Svalbard, including the capital of Longyearbyen (population 2400), currently have isolated energy systems with coal or diesel as the main energy source. Geothermal energy is considered as a possible alternative for electricity production, as a heat source in district heating systems or harnessed for heating and cooling using geothermal heat pump installations. In this contribution we present the until now fragmented data sets relevant to characterize and assess the geothermal potential of Svalbard. Data sets include petroleum and deep research boreholes drilled onshore Svalbard, 14 of which have recorded subsurface temperature data at depths below 200 m. Geothermal gradients on Spitsbergen vary from 24 degrees C/km in the west to 55 degrees C/km in the south-east, with an average of 33 degrees C/km. Four deep research boreholes were fully cored and analyzed for thermal conductivity. These analyses were complemented by thermal conductivity calculated from wireline logs in selected boreholes and four measurements on outcrop samples. 1D heat flow modelling on five boreholes calibrated with the measured thermal conductivities offers insights into heat transfer through the heterogeneous sedimentary suc-cession. Offshore petroleum boreholes in the south-western Barents Sea and marine heat flow stations around Svalbard provide a regional framework for discussing spatial variation in heat flow onshore Svalbard, with emphasis on the effects of erosion and deposition on the thermal regime. We conclude that Svalbard's geology is well suited for geothermal exploration and potential production, though challenges related to permafrost, the presence of natural gas, heterogeneous reservoir quality and strongly lateral varying heat flow need to be adequately addressed prior to geothermal energy production. Specifically for Longyearbyen, high geothermal gradients of 40-43 degrees C/km in the nearest borehole (DH4) suggest promising sub-surface thermal conditions for further exploration of deep geothermal potential near the settlement.

Since 1963, the International Heat Flow Commission has been fostering the compilation of the Global Heat Flow Database to provide reliable heat-flow data. Over time, techniques and methodologies evolved, calling for a reorganization of the database structure and for a reassessment of stored heat-flow data. Here, we provide the results of a collaborative, community-driven approach to set-up a new, quality-approved global heat-flow database. We present background information on how heat-flow is determined and how this important thermal parameter could be systematically evaluated. The latter requires appropriate documentation of metadata to allow the application of a consistent evaluation scheme. The knowledge of basic data (name and coordinates of the site, depth range of temperature measurements, etc.), details on temperature and thermal-conductivity data and possible perturbing effects need to be given. The proposed heat-flow quality evaluation scheme can discriminate between different quality aspects affecting heat flow: numerical uncertainties, methodological uncertainties, and environmental effects. The resulting quality codes allow the evaluation of every stored heat-flow data entry. If mandatory basic data are missing, the entry is marked accordingly. In cases where more than one heat-flow determination is presented for one specific site, and all of them are considered for the site, the poorest evaluation score is inherited to the site level. The required data and the proposed scheme are presented in this paper. Due to the requirements of the newly developed evaluation scheme, the database structure as presented in 2021 has been updated and is available in the appendix of this paper. The new quality scheme will allow a comprehensible evaluation of the stored heat-flow data for the first time.

Abstract. We compile, analyse and map all available geothermal heat flow measurements collected in and around Greenland into a new database of 419 sites and generate an accompanying spatial map. This database includes 290 sites previously reported by the International Heat Flow Commission (IHFC), for which we now standardize measurement and metadata quality. This database also includes 129 new sites, which have not been previously reported by the IHFC. These new sites consist of 88 offshore measurements and 41 onshore measurements, of which 24 are subglacial. We employ machine learning to synthesize these in situ measurements into a gridded geothermal heat flow model that is consistent across both continental and marine areas in and around Greenland. This model has a native horizontal resolution of 55 km. In comparison to five existing Greenland geothermal heat flow models, our model has the lowest mean geothermal heat flow for Greenland onshore areas (44 mW m–2). Our model’s most distinctive spatial feature is pronounced low geothermal heat flow (< 40 mW m–2) across the North Atlantic Craton of southern Greenland. Crucially, our model does not show an area of elevated heat flow that might be interpreted as remnant from the Icelandic Plume track. Finally, we discuss the substantial influence of paleoclimatic and other corrections on geothermal heat flow measurements in Greenland. The in-situ measurement database and gridded heat flow model, as well as other supporting materials, are freely available from the GEUS DataVerse (https://doi.org/10.22008/FK2/F9P03L; Colgan and Wansing, 2021).

The Baltic Shield is located in northern Europe. It was formed by amalgamation of a series of terranes and microcontinents during the Archean to the Paleoproterozoic, followed by significant modification in Neoproterozoic to Paleozoic time. The Baltic Shield includes a high mountain range, the Scandes, along its western North Atlantic coast, despite being a stable craton located far from any active plate boundary. The ScanArray international collaborative program has acquired broad band seismological data at 192 locations in the Baltic Shield during the period between 2012 and 2017. The main objective of the program is to provide seismological constraints on the structure of the lithospheric crust and mantle as well as the sublithospheric upper mantle. The new information will be applied to studies of how the lithospheric and deep structure affects observed fast topographic change and geological-tectonic evolution of the region. The recordings are of very high quality and are used for analysis by suite of methods, including P- and S-wave receiver functions for the crust and upper mantle, surface wave and ambient noise inversion for seismic velocity, body wave P- and S- wave tomography for upper mantle velocity structure, and shear-wave splitting measurements for obtaining bulk anisotropy of the upper and lower mantle. Here we provide a short overview of the data acquisition and initial analysis of the new data with focus on parameters that constrain the fast topographic change in the Scandes.

Understanding the thermal behavior of nonsteady state subsurface geosystems, when temperature changes over time, requires knowledge on the speed of heat propagation and, thus, of the rock's thermal diffusivity as essential thermo‐physical parameter. Mixing models are commonly used to describe thermo‐physical properties of polymineralic rocks. A thermal diffusivity‐porosity relation is known from literature that incorporates common mixing models into the heat equation and properly works for unconsolidated, clastic clayey, and sandy marine sediments of high porosity (35% – 80%). We have proofed the relation's applicability for consolidated, isotropic sedimentary rocks of low porosity (<35%). The performance of this approach was evaluated for consolidated quartz‐dominated sandstones containing air, water, and heptane as pore‐ and/or fracture‐filling medium. For these rocks, the reliability of the relation was confirmed for the entire range of porosity and all three media, with water‐saturated rocks displaying an almost perfect fit between measured and modeled thermal diffusivity. Additional measurements conducted on a larger suite of low‐porous siliciclastic and carbonate rocks imply that it is also suitable to acceptably good to infer the thermal diffusivity of mineralogically more diverse sedimentary rocks. In contrast to other common mixing models, this relation is appropriate to convert thermal‐diffusivity data obtained on air‐saturated samples into such reflecting water‐saturated conditions. This is of particular importance for handling data acquired from methods limited to the measurement of dry samples, for example, laser‐flash analysis. In conclusion, the applied relation is suited to model thermal‐diffusivity data of isotropic sedimentary rocks of different porosity and independent of the pore fluid.

The ScanArray international collaborative program acquired broadband seismological data at 192 locations in the Baltic Shield during the period between 2012 and 2017. The main objective of the program is to provide seismological constraints on the structure of the lithospheric crust and mantle as well as the sublithospheric upper mantle. The new information will be applied to studies of how the lithospheric and deep structure affect observed fast topographic change and geological-tectonic evolution of the region. The program also provides new information on local seismicity, focal mechanisms, and seismic noise. The recordings are generally of very high quality and are used for analysis by various seismological methods, including P- and S-wave receiver functions for the crust and upper mantle, surface wave and ambient noise inversion for seismic velocity, body-wave P- and S-wave tomography for upper mantle velocity structure using ray and finite frequency methods, and shear-wave splitting measurements for obtaining bulk anisotropy of the upper and lowermost mantle. Here, we provide a short overview of the data acquisition and initial analysis of the new data, together with an example of integrated seismological results obtained by the project group along a representative ∼1800-km-long profile across most of the tectonic provinces in the Baltic Shield between Denmark and the North Cape. The first models support a subdivision of the Paleoproterozoic Svecofennian province into three domains, where the highest topography of the Scandes mountain range in Norway along the Atlantic Coast has developed solely in the southern and northern domains, whereas the topography is more subdued in the central domain.

Well logs are commonly used by geoscientists to infer and extrapolate physical properties of subsurface rocks. However, at some depth intervals, well log values might be missing due to operational issues in the logging process. To overcome this problem, an innovative approach to reconstruct well logs is proposed using machine learning methods. Based on other complete logging features, the missing well log values are predicted by data-driven machine learning algorithms, namely random forest. A grid-searching scheme is applied to find a combination of hyper-parameters for the best cross-validation score. During the training process, the relative importance of different input features is analysed to remove weakly sensitive measurements and prioritize data with strong correlation with the target variables. Principal component analysis is applied to explore the multicollinearity in the input features, such that only few principal components in the new data vector are used to represent a large fraction of the variance in the original data. To quantify the uncertainty in the predictions, a quantile regression tree is used for determining prediction intervals. Well log data from the Volve Field are used for validation of the prediction obtained by random forest, in which a high correlation coefficient between prediction and reference is achieved. The prediction intervals of different percentiles are estimated, and show more accurate results at depth points where a small range of the prediction intervals exists.

Segmentation of faults based on seismic images is an important step in reservoir characterization. With the recent developments of deep-learning methods and the availability of massive computing power, automatic interpretation of seismic faults has become possible. The likelihood of occurrence for a fault can be quantified using a sigmoid function. Our goal is to quantify the fault model uncertainty that is generally not captured by deep-learning tools. We have used the dropout approach, a regularization technique to prevent overfitting and coadaptation in hidden units, to approximate the Bayesian inference and estimate the principled uncertainty over functions. Particularly, the variance of the learned model has been decomposed into aleatoric and epistemic parts. Our method is applied to a real data set from the Netherlands F3 block with two different dropout ratios in convolutional neural networks. The aleatoric uncertainty is irreducible because it relates to the stochastic dependency within the input observations. As the number of Monte Carlo realizations increases, the epistemic uncertainty asymptotically converges and the model standard deviation decreases because the variability of the model parameters is better simulated or explained with a larger sample size. This analysis can quantify the confidence to use fault predictions with less uncertainty. In addition, the analysis suggests where more training data are needed to reduce the uncertainty in low-confidence regions.

We have introduced a Bayesian neural network in quantitative log prediction studies with the goal of improving the petrophysical characterization and quantifying the uncertainty of model predictions. Neural network (NN) methods are gaining popularity in the petrophysics and geophysics communities; however, uncertainty quantification in model predictions is often neglected in the available literature, where the prediction is frequently performed in a deterministic setting. Determination of the uncertainty of the petrophysical model requires the estimation of the posterior distribution of the neural parameters that is generally mathematically intractable; for this reason, we adopt a variational approach to approximate the posterior model of the Bayesian network. To represent the uncertainty, we randomly draw samples from the posterior distribution of neural parameters to predict the model variables given the input data, leading to a learned-ensemble predictor. The proposed approach combines the ability of the NN of extracting hidden relations within the data set that physical relations cannot describe and the probabilistic framework for uncertainty quantification. We apply the proposed method to a well-log data set from the Volve Field, offshore Norway, to predict well logs in intervals where the data are incomplete or missing due to operational issues in the drilling procedure. The proposed approach is validated in intervals where the true data are available but not included in the training process. In the proposed application, the correlation coefficient between predictions and true data is greater than 0.9. In terms of accuracy, the results are comparable to those obtained using a traditional NN approach; however, the proposed method also provides a quantification of the uncertainty in the results, which offers additional information on the confidence in the predictions.

The seismic response of geological reservoirs is a function of the elastic properties of porous rocks, which depends on rock types, petrophysical features, and geological environments. Such rock characteristics are generally classified into geological facies. We propose to use the convolutional neural networks in a Bayesian framework to predict facies based on seismic data and quantify the uncertainty in the classification. A variational approach is adopted to approximate the posterior distribution of neural parameters that is mathematically intractable. The network is trained on labeled examples. The mean and the standard deviation of the distribution of neural parameters are randomly drawn from predefined Gaussian functions for the initialization, and are updated by minimizing the negative evidence lower bound. The facies classification is applied to seismic sections not included in the training data set. We draw multiple random samples from the trained variational posterior distribution to simulate an ensemble predictor and classify the most probable seismic facies. We implement the proposed network in the open-source library of TensorFlow Probability, for its convenience and flexibility. The applications show that the internal regions of the seismic sections are generally classified with higher confidence than their boundaries, as measured by the predictive entropy that is calculated based on a multiclass probability across the possible facies. A plain neural network is also applied for comparison, by assigning fixed values to the neural parameters using a classical backpropagation technique. The comparison shows consistent results; however, the proposed approach is able to assess the uncertainty in the predictions.

A 2D seismic AVO inversion and well log analysis was performed to characterise a geothermal reservoir in the northern Zealand of Denmark in 2019. This case study shows how from the seismic inversion results it is possible to interpret different lithologies and estimate porosities through links established with well logs. The results revealed several layers of porous and clean sandstone as potential high-quality reservoirs for geothermal energy development within the Lower Jurassic unit and the Gassum formation. Even though the limited data available for the study caused some challenges, the obtained predictions seem generally reasonable when compared to existing regional well data, seismic interpretations and geological expectations. Ultimately, this study demonstrates the applicability of seismic AVO inversion for reservoir characterisation as a tool for de-risking geothermal resources.