3-D Geological Models as Scientific Tools for Joint Inversion, Uncertainty Quantification, and Machine Learning

Geological models are key to our understanding of the subsurface by providing both visual and quantitative context. But accurately modeling the significant heterogeneities, discontinuities and the uncertainties of geological systems from often sparse data remains challenging. Substantial developments in geomodeling over the past years has helped bridge the gap between input data and resulting geomodel, allowing for the (semi-)automated construction of geomodels, a quicker model validation and rebuilding when new data arrives, as well as an efficient testing of multiple hypotheses. Increasing computing power now also allows for effective stochastic simulation of uncertainties in geomodelling, as well as the integration of probabilistic inference frameworks and geophysical inversions. Machine learning approaches can be used in every step of the geomodeling pipeline to enhance the process: from automated input data extraction and classification to probabilistic model selection.

We seek here contributions from all geoscientists using 3-D geological modelling methods, as well as novel developments to construct these models, to quantify and communicate uncertainties, and to integrate geological modelling into geophysical inversions. Of special interest are also approaches to combine and enhance geomodelling with machine learning methods. Applications can be in any field of solid earth sciences to address scientific questions throughout the lithosphere/anthroposphere.

Convener: Florian Wellmann | Co-conveners: Clare Bond, Zhouji LiangECSECS, Mohammad MoulaeifardECSECS, Jan von Harten
vPICO presentations
| Wed, 28 Apr, 11:45–12:30 (CEST)

vPICO presentations: Wed, 28 Apr

Chairperson: Florian Wellmann
Mark Jessell

In geological settings characterised by folded and faulted strata, and where good field data exist, we have been able to automate a large part of the 3D modelling process directly from the raw geological database (maps, bedding orientations and drillhole data). The automation is based upon the deconstruction of the geological maps and databases into positional, gradient and spatial and temporal topology information, and the combination of deconstructed data into augmented inputs for 3D geological modelling systems, notably LoopStructural and GemPy.

When we try to apply this approach to more complex terranes, such as greenstone belts, we come across two types of problem:

  • 1) Insufficient structural data, since the more complexly deformed the geology, the more we need to rely on secondary structural information, such as fold axial traces and vergence to ‘solve’ the structures. Unfortunately these types of data are not always stored in national geological databases. One approach to overcoming this is to analyse the simpler (i.e. bedding) data to try and estimate the secondary information automatically.


  • 2) The available information is unsuited to the logic of the modelling system. Most modern modelling platforms assume the knowledge of a chronostratigraphic hierarchy, however, especially in more complexly deformed regions, a lithostratigraphy may be all that is available. Again a pre-processing of the map and stratigraphic information may be possible to overcome this problem.

This presentation will highlight the progress that has been made, as well as the road-blocks to universal automated 3D geological model construction.


We acknowledge the support of the MinEx CRC and the Loop: Enabling Stochastic 3D Geological Modelling (LP170100985) consortia. The work has been supported by the Mineral Exploration Cooperative Research Centre whose activities are funded by the Australian Government's Cooperative Research Centre Programme. This is MinEx CRC Document 2020/xxx.


How to cite: Jessell, M.: Current and future limits to automated 3D geological model construction, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-632, https://doi.org/10.5194/egusphere-egu21-632, 2021.

Matthis Frey, Sebastian Weinert, Kristian Bär, Jeroen van der Vaart, Chrystel Dezayes, Philippe Calcagno, and Ingo Sass

The crystalline basement of the Upper Rhine Graben presents an attractive target for deep geothermal projects due to its favourable temperatures and its high potential as a fractured and faulted reservoir system. It is already exploited at several sites, e.g. Soultz-sous-Forêts or Landau, and further projects are currently planned or under development. The crystalline units are furthermore the main source of radiogenic heat production and thus, together with the shallow Moho depth and convective heat transport along large fault zones, significantly contributing to the crustal temperature field. For these reasons, we developed the most detailed 3D geological model of the basement in the northern Upper Rhine Graben to date within the Interreg NWE DGE-ROLLOUT and Hesse 3D 2.0 projects. Due to the small number of very deep boreholes as well as seismic profiles reaching the basement beneath the locally more than 5 km thick sedimentary cover, we additionally used high-resolution magnetic and gravity datasets. In contrast to common deterministic modelling approaches, we performed a stochastic joint inversion of the geophysical data by applying a Monte Carlo Markov Chain algorithm. This method generates a large set of random but valid models, which enables a statistical evaluation of the results, e.g. concerning the model uncertainties. For a realistic attribution of the model, we used existing petrophysical databases of the region and measured the magnetic susceptibility of more than 430 rock samples. As a result of the inversion, high-resolution voxel models of the density and susceptibility distribution were generated, allowing conclusions about the composition and structure of the crystalline crust, which leads to a reduction of uncertainties and risks associated with deep geothermal drillings in the northern Upper Rhine Graben. Furthermore, our model will serve as a basis for realistic simulations of heat transport processes in the fractured basement and a meaningful assessment of the deep geothermal potential in the future.

How to cite: Frey, M., Weinert, S., Bär, K., van der Vaart, J., Dezayes, C., Calcagno, P., and Sass, I.: 3D Modelling of the Northern Upper Rhine Graben Crystalline Basement by Joint Inversion of Gravity and Magnetic Data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-477, https://doi.org/10.5194/egusphere-egu21-477, 2021.

Daniel Pflieger, Miguel de la Varga Hormazabal, Simon Virgo, Jan von Harten, and Florian Wellmann

Three dimensional modeling is a rapidly developing field in geological scientific and commercial applications. The combination of modeling and uncertainty analysis aides in understanding and quantitatively assessing complex subsurface structures. In recent years, many methods have been developed to facilitate this combined analysis, usually either through an extension of existing desktop applications or by making use of Jupyter notebooks as frontends. We evaluate here if modern web browser technology, linked to high-performance cloud services, can also be used for these types of analyses.

For this purpose, we developed a web application as proof-of-concept with the aim to visualize three dimensional geological models provided by a server. The implementation enables the modification of input parameters with assigned probability distributions. This step enables the generation of randomized realizations of models and the quantification and visualization of propagated uncertainties. The software is implemented using HTML Web Components on the client side and a Python server, providing a RESTful API to the open source geological modeling tool “GemPy”. Encapsulating the main components in custom elements, in combination with a minimalistic state management approach and a template parser, allows for high modularity. This enables rapid extendibility of the functionality of the components depending on the user’s needs and an easy integration into existing web platforms.

Our implementation shows that it is possible to extend and simplify modeling processes by creating an expandable web-based platform for probabilistic modeling, with the aim to increase the usability and to facilitate access to this functionality for a wide range of scientific analyses. The ability to compute models rapidly and with any given device in a web browser makes it flexible to use, and more accessible to a broader range of users.

How to cite: Pflieger, D., de la Varga Hormazabal, M., Virgo, S., von Harten, J., and Wellmann, F.: Rapid 3D geological modeling to assess and visualize uncertainties in a web application, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13187, https://doi.org/10.5194/egusphere-egu21-13187, 2021.

Virginia Toy, Steffen Abe, Paul Bons, Simon J. Buckley, Hagen Deckert, Selina Fenske, Martina Kirilova, Conor Lewis, Sebastian Mutz, Julian Owin, Till Sachau, Bernhard Schuck, and Philipp Seelos

In September 2020, the Corona crisis offered us an opportunity to develop and test a blended real and virtual interdisciplinary field mapping class, as well as revealing the need for, and stimulating development of new web-based tools for structural interpretation.

Universität Mainz’ usual Master’s advanced field mapping, and Universität Tübingen’s usual Bachelor’s mapping classes were replaced with combinations of (i) virtual field mapping of Jurassic-Cretaceous sedimentary units at Molinos, Teruel Province, Spain, and (ii) field mapping of metamorphic rocks in the Mittelrhein Gorge and the Arh Valley, and outcrops of sedimentary rocks near Tübingen, Germany, which the students were mostly able to access on day trips using public transport or by bicycle.

For the Molinos part of the exercise both groups were offered hand specimens containing distinctive fossils, linked to locations (and pseudo-locations) by google .kmz files, a variety of structural measurements also linked via .kmz files, and detailed satellite imagery within which mappable geological units display distinct characteristics. Introductions to the stratigraphy were made in three virtual outcrop sections examined in Google Street View from within Google Earth, and via web-based photogrammetric 3D outcrop models made available on the V3Geo virtual 3D geoscience platform. The students then extrapolated this stratigraphy based on the satellite imagery and .kmz file information.

Our perception, validated by student feedback, is that the real parts of both field excursions were very important since they allowed us to teach and refine mapping and compass methodology and best demonstrate how to analyze 3D geometries of geological structures. Universität Mainz students particularly benefited from being able to visit locations where we had already made 3D outcrop models and offered a digital excursion, in the Ahr Valley (Rhenish Massif). They were able to compare real structural measurements with those derived from the precisely georeferenced 3D models, which enhanced their ability to subsequently obtain such information solely from the models. Although final student maps were of comparable quality to those produced in the field, structural interpretations were hampered by a lack of field measurements. In many cases, the Google Earth DEM is of too low resolution and ways should be found to include higher-resolution DEMs in web-based data sets.

Overall, we think there were advantages compared to traditional field mapping, such as (i) enhanced evidence that methods like ‘structure contouring’ were used in all mapping, (ii) we were stimulated to teach the students to use digital methods to acquire field data, such as StraboSpot and Stereonet11 Apps. We observed these tools, and others we were unaware of, being used in combination with traditional paper and compass during the real mapping exercise. We hope to continue to employ this blended teaching approach even when the Corona crisis passes. This will be facilitated by our development of further 3D outcrop models, .kmz files with key information about outcrops in the Mittelrhein, and especially, web-based (rather than PC-based) tools to extract structural data such as plane and line orientations from 3D outcrop models and enable collaborative work on one data set.

How to cite: Toy, V., Abe, S., Bons, P., Buckley, S. J., Deckert, H., Fenske, S., Kirilova, M., Lewis, C., Mutz, S., Owin, J., Sachau, T., Schuck, B., and Seelos, P.: A blended learning approach to structural field mapping: combining local geology, virtual geology, and web-based tools , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3581, https://doi.org/10.5194/egusphere-egu21-3581, 2021.

Michał Michalak and Lesław Teper

In geological modelling it is often assumed that sub-conformable contacts are parallel. Here, we challenged this assumption by comparing conformable and unconformable horizons within Kraków-Silesian Homocline, Poland. The study objective was to provide both quantitative and qualitative analyses of dissimilarities between contacts of interest. The quantitative portion of the research involved geostatistical modelling of angular distance between contacts subdivided by Delaunay triangulation. We confirmed that in general the angular distances within conformable contacts are smaller than these between genetically unrelated horizons. However, there are exceptions from this rule related mainly to elongated zones of unknown origin in which angular distances are greater for sub-conformable contacts. The qualitative portion of the research was based on a specific variant of spatial clustering method based on Delaunay triangulation. Using this method, we aimed to identify geological differences underlying the increased values of angular distances in specific places. We identified convex forms that are developed only in some of the analysed contacts. These convex forms may be due to differences in the competence of tectonically deformed rocks. In such a case, discrete displacements of brittle sandstones are replaced by continuous deformation of claystones in the cover, represented by fault-related flexures or drape folds, which results in sharp changes in the angular distances observed. Acknowledgements: The project is funded by National Science Centre Poland, 2020/37/N/ST10/02504

How to cite: Michalak, M. and Teper, L.: Parallel or not? Quantitative and qualitative methods to identify dissimilarities between sub-conformable contacts, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4820, https://doi.org/10.5194/egusphere-egu21-4820, 2021.

Michael Hillier, Florian Wellmann, Boyan Brodaric, Eric de Kemp, and Ernst Schetselaar

A new approach for constrained 3-D structural geological modelling using Graph Neural Networks (GNN) has been developed that is driven by a learning through training paradigm. Graph neural networks are an emerging deep learning model for graph structured data which can produce vector embeddings of graph elements including nodes, edges, and graphs themselves, useful for various learning objectives. In this work our graphs represent unstructured volumetric meshes. Our developed GNN architecture can generate spatially interpolated implicit scalar fields and discrete geological unit predictions on graph nodes (e.g. mesh vertices) to construct 3-D structural models. Interpolations are constrained by scattered point data sampling geological units, interfaces, as well as linear and planar orientation measurements. Interpolation constraints are incorporated into the neural architecture using loss functions associated with each constraint type that measure the error between the network’s predictions and data observations. This presentation will describe key concepts involved within this approach including vector embeddings, spatial-based convolutions on graphs, and loss functions for structural geological features. In addition, several modelling results will be given that demonstrate the capabilities and potential of GNNs for representing geological structures.

How to cite: Hillier, M., Wellmann, F., Brodaric, B., de Kemp, E., and Schetselaar, E.: Using Graph Neural Networks for 3-D Structural Geological Modelling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12978, https://doi.org/10.5194/egusphere-egu21-12978, 2021.

Ramy Abdallah, Clare E. Bond, and Robert W.H. Butler

Machine learning is being presented as a new solution for a wide range of geoscience problems. Primarily machine learning has been used for 3D seismic data processing, seismic facies analysis and well log data correlation. The rapid development in technology with open-source artificial intelligence libraries and the accessibility of affordable computer graphics processing units (GPU) makes the application of machine learning in geosciences increasingly tractable. However, the application of artificial intelligence in structural interpretation workflows of subsurface datasets is still ambiguous. This study aims to use machine learning techniques to classify images of folds and fold-thrust structures. Here we show that convolutional neural networks (CNNs) as supervised deep learning techniques provide excellent algorithms to discriminate between geological image datasets. Four different datasets of images have been used to train and test the machine learning models. These four datasets are a seismic character dataset with five classes (faults, folds, salt, flat layers and basement), folds types with three classes (buckle, chevron and conjugate), fault types with three classes (normal, reverse and thrust) and fold-thrust geometries with three classes (fault bend fold, fault propagation fold and detachment fold). These image datasets are used to investigate three machine learning models. One Feedforward linear neural network model and two convolutional neural networks models (Convolution 2d layer transforms sequential model and Residual block model (ResNet with 9, 34, and 50 layers)). Validation and testing datasets forms a critical part of testing the model’s performance accuracy. The ResNet model records the highest performance accuracy score, of the machine learning models tested. Our CNN image classification model analysis provides a framework for applying machine learning to increase structural interpretation efficiency, and shows that CNN classification models can be applied effectively to geoscience problems. The study provides a starting point to apply unsupervised machine learning approaches to sub-surface structural interpretation workflows.

How to cite: Abdallah, R., Bond, C. E., and Butler, R. W. H.: Train Deep Learning Models using subsurface geological images datasets, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6385, https://doi.org/10.5194/egusphere-egu21-6385, 2021.

Mustaeen Ur Rehman Qazi and Florian Wellmann

Structural geological models are often calculated on a specific spatial resolution – for example in the form of grid representations, or when surfaces are extracted from implicit fields. However, the structural inventory in these models is limited by the underlying mathematical formulations. It is therefore logical that, above a certain resolution, no additional information is added to the representation.

We evaluate here if Deep Neural Networks can be trained to obtain a high-resolution representation based on a low-resolution structural model, at different levels of resolution. More specifically, we test the use of state-of-the-art Generative Adversarial Networks (GAN’s) for image superresolution in the context of 2-D geological model sections. These techniques aim to learn the hidden structure or information in high resolution image data set and then reproduce highly detailed and super resolved image from its low resolution counterpart. We propose the use of Generative Adversarial Networks GANS for super resolution of geological images and 2D geological models represented as images. In this work a generative adversarial network called SRGAN has been used which uses a perceptual loss function consisting of an adversarial loss, mean squared error loss and content loss for photo realistic image super resolution. First results are promising, but challenges remain due to the different interpretation of color in images for which these GAN’s are typically used, whereas we are mostly interested in structures.

How to cite: Qazi, M. U. R. and Wellmann, F.: Super-resolution in Structural Geological Models., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10333, https://doi.org/10.5194/egusphere-egu21-10333, 2021.

Tom Manzocchi

Faults can control the large-scale properties of rock volumes through their behaviour as flow conduits and/or barriers or by localising geomechanical effects. Hence, often the fidelity of a numerical model of faulted site relies on the accuracy with which the fault zone is represented.  There are two distinct factors that must be considered in a modelling study: first, does the model contain the most relevant characteristics of the fault that influence the behaviour of interest; and second, are these characteristics assigned realistic and representative values that capture both their natural variability and the uncertainty with which they can be determined for the specific case of interest. These two factors are contained in the conceptual fault model and choice of modelling proxy-properties, respectively.

In recent years, two classes of conceptual fault zone model have dominated the description of fault zones, broadly characterised by either a continuous or a discrete approach. Continuous fault zone properties (e.g. fault core and damage zone thickness, displacement partitioning statistics) often show high variability which many modelling studies attempt to capture by running multiple model containing property values sampled from the distribution. Discrete descriptions focus on the presence of individual fault zone elements (e.g. shale smears, relay zones), and models guided by a discrete conceptual model attempt to place representative frequencies of elements. A single discrete model might contain the same property distributions as an ensemble of continuous models yet, because it contains a representative frequency of different elements, its behaviour might lie beyond the extreme behaviour of the continuous ensemble. Hence, the manner in which a geologist’s conceptual model is represented in a modeller’s numerical model can be hugely important for the outcome of the study, and it is in the interest of both modellers and geologists to ensure that they have a correct understanding of the other’s part of the process.

How to cite: Manzocchi, T.: Discrete and continuous conceptual models of fault zones., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7348, https://doi.org/10.5194/egusphere-egu21-7348, 2021.

Jan von Harten, Florian Wellmann, and Miguel de la Varga

Implicit methods have been the basis of many developments in 3-D structural geologic modeling.  Typical input data for these types of models include surface points and orientations of geologic units, as well as the corresponding age relations (stratigraphic pile). In addition, the range of influence of input points needs to be defined, but it is difficult to infer a reasonable stationary estimate from data with highly variable configuration.

Often, this results in models that show artefacts due to data configuration including oversimplified results (underfitting) in areas where data is missing, overcomplex results (overfitting) in areas of high data density and geologically unreasonable surface shapes.

In this work we explore various methods to improve 3-D implicit geologic modeling by manipulating the data configuration using locally varying anisotropic kernels and kernel density estimation. In other words, the influence of input data in the interpolation is weighted based on directions and data density. Input parameters for these methods can either be based on the original input data configuration, inferred from additional supportive data, or be based on geologic expert knowledge. The proposed methods aim to increase model control while retaining the key advantages of implicit modeling.

Model improvements will be shown using a set of typical geologic structures and regularly occurring artefacts. We compare results to previously proposed methods that integrate anisotropies in traditional kriging applications and discuss the specific requirements for applicability in implicit structural geomodeling.

How to cite: von Harten, J., Wellmann, F., and de la Varga, M.: Improving implicit geologic models based on data configuration, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10865, https://doi.org/10.5194/egusphere-egu21-10865, 2021.

Sofia Mantilla Salas, Miguel Corrales, Hussein Hoteit, Abdulkader Alafifi, and Alexandros Tasianas

The development of Carbon Capture Utilization and Storage (CCUS) technology paired with existing energy systems will facilitate a successful transition to a carbon-neutral economy that offers efficient and sustainable energy. It will also enable the survival of multiple and vital economic sectors of high-energy industries that possess few other options to decarbonize. Nowadays, just about one-ten-thousandth of the global annual emissions are being captured and geologically-stored, and therefore with today’s emission panorama, CCS large-scale deployment is more pressing than ever. In this study, a 3D model that represents the key reservoir uncertainties for a CCUS pilot was constructed to investigate the feasibility of CO2 storage in the Unayzah Formation in Saudi Arabia. The study site covers the area of the city of Riyadh and the Hawtah and Nuayyim Trends, which contain one of the most prolific petroleum-producing systems in the country. The Unayzah reservoir is highly stratified and it is subdivided into three compartments: the Unayzah C (Ghazal Member), the Unayzah B (Jawb Member), and the Unayzah A (Wudayhi and Tinat Members). This formation was deposited under a variety of environments, such as glaciofluvial, fluvial, eolian, and coastal plain. Facies probability trend maps and well log data were used to generate a facies model that accounted for the architecture, facies distribution, and lateral and vertical heterogeneity of this high complexity reservoir. Porosity and predicted permeability logs were used with Sequential Gaussian Simulation and co-kriging methods to construct the porosity and permeability models. The static model was then used for CO2 injection simulation purposes to understand the impact of the flow conduits, barriers, and baffles in CO2 flow in all dimensions. Similarly, the CO2 simulations allowed us to better understand the CO2 entrapment process and to estimate a more realistic and reliable CO2 storage capacity of the Unayzah reservoir in the area. To test the robustness of the model predictions, geological uncertainty quantification and a sensitivity analysis were run. Parameters such as porosity, permeability, pay thickness, anisotropy, and connectivity were analyzed as well as how various combinations between them affected the CO2 storage capacity, injectivity, and containment. This approach could improve the storage efficiency of CO2 exceeding 60%. The analyzed reservoir was found to be a promising storage site. The proposed workflow and findings of the static and dynamic modeling described in this publication could serve as a guideline methodology to test the feasibility of the imminent upcoming pilots and facilitate the large-scale deployment of this very promising technology.

How to cite: Mantilla Salas, S., Corrales, M., Hoteit, H., Alafifi, A., and Tasianas, A.: Quantifying Uncertainty through 3D Geological Modeling for Carbon Capture Utilization and Storage in the Unayzah Formation in Saudi Arabia, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12839, https://doi.org/10.5194/egusphere-egu21-12839, 2021.

Zhouji Liang, Denise Degen, and Florian Wellmann

Numerical simulations of subsurface processes are essential to the success of many geoengineering projects. These simulations often contain significant uncertainties due to imperfect knowledge of material properties and their spatial distribution, boundary conditions, and initial conditions. However, efficient implementations for the quantification of uncertainties for such simulations are big challenges in Computational Geoscience, mainly due to the curse of dimensionality. Process simulations often involve solving high-dimensional Partial Differential Equations (PDE) by using discretization methods such as Finite Difference (FD) or Finite Elements (FE) methods. Although such methods often give good approximations, they are computationally intensive and expensive and therefore infeasible in the applications such as MCMC where thousands of evaluations of the forward simulation are required. Previous work by Degen et.al. (2020) has addressed this problem by using a model order reduction method, the so-called reduced basis (RB) method. However, the method has limitations when considering complex (i.e., hyperbolic and non-linear) PDEs. In this work, we aim to employ the recently developed Fourier Neural Operator (FNO) (Li, 2020) as a tool to implement efficient approximation of PDEs in the application of Geothermal reservoir simulation. FNO involves a Fast Fourier transform to directly learn the mapping from the input function to the output function. FNO has the advantage of being independent of the resolution and complexity of the governing PDE. Our preliminary results show that FNO can provide good approximation results in solving four-dimensional PDEs and thus can be used as a tool for further probability studies of the parameters of interest.

How to cite: Liang, Z., Degen, D., and Wellmann, F.: The Application of Neural Operator in subsurface process simulation , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12940, https://doi.org/10.5194/egusphere-egu21-12940, 2021.

Gloria Arienti, Andrea Bistacchi, Bruno Monopoli, Giorgio Vittorio Dal Piaz, Giovanni Dal Piaz, and Davide Bertolo

3D geological modelling of complex metamorphic terrains that underwent a sequence of ductile and brittle deformation events is an extremely challenging task. Difficulties start from the input data that are frequently sparse and heterogeneous in quality and distribution. In projects based on field data only (without significant subsurface data) uncertainties are even more pronounced, but, in our project, we had the rugged topography of the Western Alps on our side, with elevations ranging from c. 1200 m to c. 3200 m and very continuous outcrops. Other problems, that we address in this contribution, arise during the modelling process. We tested different commercial software packages and some open-source research libraries and we found that no one is capable of modelling our complex structures out-of-the-box. This is not surprising since generally these codes, and particularly the commercial ones, are geared towards modelling gently deformed sedimentary sequences. However, it is possible to overcome a large range of obstacles by “fooling” implicit structural modelling algorithms, simply “cheating” on the geological meaning of model entities. This means (1) developing a conceptual model of polyphase ductile and brittle deformation, (2) finding geological/mathematical entities that are at the same time implemented in the code and able to represent the complex structures, and finally (3) carrying out the implicit modelling. For instance, tectonic contacts between large-scale tectono-metamorphic units can be treated as unconformities (and not as faults) to obtain a realistic representation. In some cases, also conformal lithological boundaries can be considered as unconformities with the goal of allowing larger thickness variations. In other situations, a “fake” stratigraphy where the same units are repeated several times can be used to model sequences of isoclinal folds and thin tectonic slices. In this contribution, some of these modelling solutions are compared in terms of (1) their straightforward implementation, and (2) their ability to generate models that properly fit the very detailed geological maps available in our study area (c. 60 km2 mapped at 1:5.000-1:10.000 with a dense set of structural stations).

How to cite: Arienti, G., Bistacchi, A., Monopoli, B., Dal Piaz, G. V., Dal Piaz, G., and Bertolo, D.: 3D geomodelling in the complex metamorphic and poly-deformed units of the Italian Western Alps (Conca di By, Aosta Valley, Italy), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15515, https://doi.org/10.5194/egusphere-egu21-15515, 2021.

Nils Chudalla, Florian Wellmann, Alexander Jüstel, and Jan von Harten

Expectations for geological models for underground characterization rise with complex engineering tasks. In this project we examine a target area as a potential site for the gravitational-wave observatory “Einstein Telescope” in the Meuse-Rhine Euroregion (Netherlands, Belgium, Germany).  The Einstein Telescope will be the world’s most sensitive observatory of its kind. It consists of a triangular shaped facility connected by 10 km long arms in 200-300 m depth. A high accuracy 3-D structural geological model is required to constrain the best position of the Einstein Telescope with geophysical and geotechnical methods.

We use an implicit modeling approach based on surface points and orientation data for modeling. This data is extracted from seismic surveys and well logs available in the region. The application of probabilistic methods in this workflow allows to propagate uncertainty of the input data into a resulting model suite, allowing to define a measure of uncertainty for the final model. Specific local difficulties that were encountered during the modelling process, including data management, the representation of complex fault networks and scaling issues will be discussed.

We will show 3-D geological models for the Meuse-Rhine Eurogregion to significantly improve our geological understanding of the target area. This improved understanding is crucial for finding the optimal position for the Einstein Telescope. Data is managed using the open-source library GemGIS. Models are created using the open-source library GemPy.

How to cite: Chudalla, N., Wellmann, F., Jüstel, A., and von Harten, J.: Implicit geological modeling for the Einstein Telescope (Meuse-Rhine Euroregion), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15814, https://doi.org/10.5194/egusphere-egu21-15814, 2021.