<p>Geological models, as 3-D representations of subsurface structures and property distributions, are used in many economic, scientific, and societal decision processes. These models are built on prior assumptions and imperfect information, and they often result from an integration of geological and geophysical data types with varying quality. These aspects result in uncertainties about the predicted subsurface structures and property distributions, which will affect the subsequent decision process.</p><p>We discuss approaches to evaluate uncertainties in geological models and to integrate geological and geophysical information in combined workflows. A first step is the consideration of uncertainties in prior model parameters on the basis of uncertainty propagation (forward uncertainty quantification). When applied to structural geological models with discrete classes, these methods result in a class probability for each point in space, often represented in tessellated grid cells. These results can then be visualized or forwarded to process simulations. Another option is to add risk functions for subsequent decision analyses. In recent work, these geological uncertainty fields have also been used as an input to subsequent geophysical inversions.</p><p>A logical extension to these existing approaches is the integration of geological forward operators into inverse frameworks, to enable a full flow of inference for a wider range of relevant parameters. We investigate here specifically the use of probabilistic machine learning tools in combination with geological and geophysical modeling. Challenges exist due to the hierarchical nature of the probabilistic models, but modern sampling strategies allow for efficient sampling in these complex settings. We showcase the application with examples combining geological modeling and geophysical potential field measurements in an integrated model for improved decision making.</p>

<p>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.</p><p>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.</p><p>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.</p><p>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.</p>

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<p>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.</p><p>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.</p><p>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.</p>