Three-dimensional structural modelling of complex metamorphic settings is an extremely challenging task. In these settings, rocks sequences record multiple ductile and brittle events, leading for instance to refolded fold structures, isoclinal folds and dense network of faults. In this contribution, we build a 3D structural model of a portion of the highly deformed core of the Alpine orogeny in the North-Western Italian Alps, by using field data (1:10,000 geological map and a dense database of structural stations) as unique input source. Our model area has an extension of ca. 1,300 km² and a vertical elevation difference between the highest mountains (e.g., Cervino-Matterhorn) and the valley floors of ca. 4,000 meters, reflecting a truly three-dimensional dataset.

Our workflow expects a first phase of orientation statistics study of the structural field database, followed by structural interpretation in vertical cross-sections and 3D interpolation using implicit surfaces and structural constraints. The implicit approach allows us to propagate field data and geological interpretation through mathematical constraints and to obtain structural interfaces reflecting observations.

After introducing the new 3D structural model of the portion of the North-Western Alps, we discuss the difficulties related to geomodelling using input surface data only, by qualitatively addressing the uncertainty aspects of our workflow. We also focus on the range of geological and structural constraints that fieldwork allows us, reasoning on the distinction between observed and interpreted geological information.

How to cite: Arienti, G., Bistacchi, A., Caumon, G., Bonneau, F., Dal Piaz, G. V., Dal Piaz, G., Monopoli, B., and Bertolo, D.: Three-dimensional modelling of a complex metamorphic nappe stack from field survey only: the case study of the Aosta Valley (Italian NW-Alps), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8856, https://doi.org/10.5194/egusphere-egu23-8856, 2023.

In this work, we conduct 3-D geologic modelling of the Bjerkreim-Sokndal (BKS) layered intrusion's Bjerkreim lobe in southern Norway. The BKS is a folded, double-plunging syncline with an areal extent of 230km². There are five rhythmic megacyclic lithological units (MCU, I - V) that are divided into zones (a - f) based on the presence or absence of index minerals. The BKS is often used as an analogue for Martian studies due to the presence of strong magnetic remanence, 20 000 nT below background. It has also undergone significant exploration for critical minerals.

Although the BKS is well-studied, there are limitations that have hindered geophysical mapping. The layered units reside along a topographic low, limiting the lowest altitude for airborne surveys. Land use is classified for agriculture and the presence of lakes restrict ground data collection to roads and pathways. Seismic and gravity surveys have been collected over the study area; however, the gravity data is sparse, and the seismic data is restricted to a single profile. These limited studies suggest a BKS depth to base of 4 - 5 km. Drillcore has been collected, however these extend on average to a depth of 30 m. In 2021, a small-scale drone magnetic survey was collected. This survey was to complete low-altitude, high resolution magnetic sampling to complement previous ground magnetic surveys and as a segue for multiscale analysis with regional airborne surveys. Since no single geophysical dataset is sufficient for a complete geologic interpretation, joint modelling is required to better understand subsurface distribution.

A master ground sample database was compiled of over 3000 samples and 11 petrophysical properties. This database consisted of in-situ and in-lab measurements. Principal Component Analysis was conducted to reduce dimensionality and identify which properties should be incorporated into the model. K-means clustering was conducted to identify natural groupings in the data, where average values for these properties were calculated from the dominant cluster. 2-D profiles orthogonal to strike were constructed at 2 km increments along the Bjerkreim lobe with additional intermediate profiles to minimize truncation of 3-D volumes. A combination of 2-D forward and joint inversion modelling was implemented using compiled aeromagnetics and gravity as the observed values. Each MCU zone was modelled as a block with average properties from the cluster analysis and constrained at surface by mapped contacts. Depth estimation routines, including Euler deconvolution, were also executed. The modelled blocks were then wireframed into volumes to create a 3-D representation of the Bjerkreim lobe.

How to cite: Lee, M., McEnroe, S., Pastore, Z., and Church, N.: 3-D Geologic Modelling of the Bjerkreim Lobe, Norway, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9058, https://doi.org/10.5194/egusphere-egu23-9058, 2023.

Modern workflows for construction of 3-D data-constrained Earth’s subsurface models in complex geological environments require sophisticated research software tools capable of handling interdisciplinary data and analysis in both visual and quantitative context. Integration of potential field data – gravity and magnetics – into the model building process is a key component that helps to bridge the gaps in the sparse input data by fitting the modelled response to the measurements. On the basis of IGMAS+ – a free cross-platform potential field modelling software – we show how 3-D model building can be complemented by interactive optimisation (inversion) of the triangulated subsurface model geometry. The optimisation is done by means of Covariance-Matrix-Adaptation Evolution Strategy (CMAES) which proved to be efficient for strongly non-linear problems with high-dimensional parameter space. In order to avoid topology distortions of the triangulated model domain, we use a concept of warping the space containing a model, rather than operating on the model vertices. The space warping implies an elegant solution using a system of virtual elastic springs connecting the lattice nodes. The optimisation workflow is demonstrated on synthetic and real case studies. We also show how an interpreter can interact with the process: visually control and influence the quality of the optimisation on a timeline. The proposed workflow is an efficient tool for automated quick model construction, validation and rebuilding, as well as for testing of multiple modelling hypotheses.

How to cite: Anikiev, D., Götze, H.-J., Plonka, C., Schmidt, S., Bott, J., and Scheck-Wenderoth, M.: Interactive optimisation of 3-D subsurface models using potential fields, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1860, https://doi.org/10.5194/egusphere-egu23-1860, 2023.

High-Temperature Aquifer Thermal Energy Storage (HT-ATES) manages the temporal mismatch between heat supply and demand periods. Up to 50 % of consumed energy by residents of metropolitans can be provided through this huge underground battery systems. This study evaluates risks and effects of geological structures for two HT-ATES candidates designed close to populated areas in central Europe. DeepStor, as the first example, is designed to store surplus heat in Meletta beds beneath the campus of the Karlsruhe Institute of Technology (KIT). A synthetic sealing fault is embedded in real topology of the Meletta beds to numerically simulate the temperature and pressure under such structural feature. The synthetic fault is relocated 16 times in the geological model and proved to only increase the pressure value up to 7 % in comparison to fault-free (base case) realization. The real tilted morphology of Meletta beds revealed that hot temperature tends to accumulate in the western side of the model while pressure increase is more notable in the reverse side, i.e. down dip. A simple function fitted to the pressure change and fault to well distance shows acceptable levels of reliability. Another showcase designed for the Greater Geneva Basin confirmed the insensitivity of the temperature and pressure to surfaces morphology of the Malm reservoir with 100 m thickness. Despite modulating the top and bottom contacts of the Malm from flat planes to randomly rugged surfaces, the results remain the same. The upper and lower surfaces are moved ± 8 and ± 10 m, respectively. This insensitivity indicates the local natures of the induced thermal regime in thick reservoirs and dispensability of some expensive exploration campaigns like 3D seismic.

How to cite: Dashti, A., Grimmer, J. C., Geuzaine, C., and Kohl, T.: Modeling two high-temperature aquifer thermal energy storage cases under uncertain geological frameworks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5416, https://doi.org/10.5194/egusphere-egu23-5416, 2023.

Gravity inversion methods are able to recover density distributions in the Earth but they need to be strongly constrained using a variety of prior information. Here, we aim at inverting gravity data anomalies constrained by existing geological and density information on orogenic areas such as the Pyrenees where many geological and geophysical studies have been conducted for geophysical exploration purposes and fluid resources recovering of economic interest.

To perform such inversion, we aim at constraining gravity inversions using covariance matrix defined as an interval distribution of possible density values. This covariance-like matrix is obtained by computing the probability of impact of lithological density variations on gravity residuals. Instead of using a Monte Carlo-like approach to sample density values in each rock unit, which may be too computationally expensive (in terms of number of forward calculations, memory and disk storage of all data needed for the probabilistic analysis), we calculate a series of probabilistic metrics associated to different combinations of density variations. For this, we select representative model variations and use partial plane experiment-based probabilistic method approach to estimate the impact of density variations on gravity data misfit. This drastically reduces the number of calculations and requires only a few tens of forward problems evaluations (instead of hundreds or thousands with Monte Carlo-like approach). Based on the impact of each prior lithological density variation, intervals of density variations can thus be estimated for each rock unit. This approach allows to define at low cost all these intervals, which can be interpreted as a reduced covariance matrix. For inversion using these intervals as constraints, we use an initial a priori density model obtained from a prior Vp model obtained by seismic teleseismic time-arrival inversion. To reconcile the so-obtained density model with gravity data, we perform gravity inversion constrained by bounded density intervals estimated from the probabilistic approach we propose. A dynamic Alternate Direction Multipliers Method regularization approach is used to constrain the inversion over such variation intervals. This allows us to obtain inverted models consistent with the geological structures modelled in the area and gravity data.

We apply this inversion technique to the whole Pyrenees chain (southwest Europe) at a 2 km resolution and on a smaller zoomed 1 km resolution area constrained by outer information (density, ADMM variation intervals, …) provided by the 2km coarser inverted model. This way, new geological features can be inferred in the collisional intraplate Iberian-Eurasian region, in the axial zone and basement, and also at depth until the upper mantle. Besides, strong excesses of mass in the northern part and strong negative density contrasts in the south of the Pyrenees are appearing and increasing with depth when compared to previous prior models.

How to cite: Martin, R., Ogarko, V., Giraud, J., Plazolles, B., Rousse, S., and Angrand, P.: 3D gravity data inversion constrained by bounding interval ADMM regularization : Application to density distribution reconstruction of the Pyrenees chain lithosphere., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2380, https://doi.org/10.5194/egusphere-egu23-2380, 2023.

Understanding the sealing capabilities of faults provide vital information for underground carbon dioxide storage to hydrocarbon exploration and production. The sealing properties of faults are dependent on several parameters controlled by lithologies, overlap, throw, fault width, burial history, thermal regime and diagenesis in the sedimentary basin. All these input parameters hold large uncertainties, and different processes will influence the fault permeabilities.

In this work we are using a Monte-Carlo approach, varying the input parameters with a certain distribution, and simulate the fault permeabilities for a North Sea case study. The 3D simulated mean geo-pressures are compared with measured overpressures in sandstone units from wells. To carry out the 3D simulation, the in-house Pressim2.0 software has been used to simulate pressure generation and dissipation over geological time scale. The fluid flow dynamics can be represented and described by pressure compartments laterally delineated by mapped faults from seismic. Lateral flow is modelled between the reservoir units and the vertical fluid flow in the overburden is modelled below, in between and above the reservoir units. Depth-converted maps of the overlying sediments are used to reconstruct the burial history that is adjusted for decompaction.

In this work we will present stochastic probability distribution of key input parameters defining the fault permeability and transmissibility for a study area. The simulated fault permeabilities will be compared with published data. We will also use misfit analysis, to evaluate what fault permeabilities that will be give the lowest misfit/deviation compared to measured overpressures from wells.

How to cite: Lothe, A. E., Grøver, A., and Roli, O.-A.: Innovative stochastic probability distribution of fault permeabilities in 3D geo-pressure modelling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14866, https://doi.org/10.5194/egusphere-egu23-14866, 2023.

How to cite: Lu, X., Lelièvre, P., and Farquharson, C.: Uncertainty quantification of geophysical models constructed by surface geometry inversion using Markov chain Monte Carlo sampling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8434, https://doi.org/10.5194/egusphere-egu23-8434, 2023.

How to cite: Farquharson, C. and Kangazian, M.: A comprehensive study of gradient and smoothness regularization operators on unstructured tetrahedral meshes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10084, https://doi.org/10.5194/egusphere-egu23-10084, 2023.

Thermokinematic modeling often relies on prescribed geometric and kinematic models at depth without considering their uncertainty. This does not allow for the proper quantification of the relative contributions of different drivers to the exhumation signal. Considering uncertainty of structural data in thermokinematic models would help understanding how much shortening associated with the observed cooling signal occurred. 

The aim of this work is to combine probabilistic structural modeling with thermokinematic forward simulations to investigate the related uncertainties. For this purpose, the Bavarian Subalpine Molasse is particularly suited as a test case, as it connects the Alpine orogen with its foreland, and should shed light on the strain distributions during the latest stages of Alpine mountain building. 3D implicit geological modeling of the Bavarian Subalpine Molasse triangle zone was carried out and combined with a systematic random sampling approach to automatically generate an ensemble of geometric models in the range of assigned uncertainties. In addition, a probabilistic 3D kinematic forward model is constructed. A link can then be obtained between kinematic model parameters and present-day geometry in comparison with field observations at the surface and also in comparison to the range of geometric uncertainties in the 3D geological model. Results show that the uncertainty (quantified as information entropy) is distributed as a function of structural complexity, depth, and data density throughout the geometric model, and additionally where fault slip ranges are large in the kinematic model.

In a next step, these models are combined with a thermokinematic forward model to integrate thermochronological measurements from previous campaigns, and eventually own measurements, to obtain an integrated picture of foreland evolution and associated uncertainties over space and time.

How to cite: Brisson, S., Ziegler, J., Chudalla, N., Wellmann, F., and von Hagke, C.: Combining thermochronological data with 3D probabilistic kinematic modeling of the Bavarian Subalpine Molasse for uncertainty estimation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13340, https://doi.org/10.5194/egusphere-egu23-13340, 2023.