Often, geological models are generated based on information obtained from exploration data, e.g. borehole records. These borehole records contain descriptions and interpretations about petrography (lithology) and stratigraphy, respectively. The information is crucial for modeling the spatial distribution of lithostratigraphic layers. However, the interpretation of the drilling profiles is error-prone as it depends on several factors, including date of recording, exploration target, quality of digitalization of the borehole record and the human interpretation bias of the responsible expert, among others. Due to the large number of boreholes drilled during explorations, separating adequate from insufficient drilling profiles is of great importance, yet rather difficult. While visual inspection of the inferred geological model is a viable approach it results in numerous iterations to filter inadequate drilling profiles which is time-consuming and expensive.

We present a Python-based software package that predicts the quality of lithostratrigraphic data from borehole records based on several criteria. Using pre-checked reference drilling profiles, we train a random forest model to predict the quality of non-checked drilling profiles for geologically comparable regions. The aforementioned selection criteria as well as the predictors are individually definable by the software user.

As a study area, we selected a former lignite mining area of Lusatia bordering the Federal State of Brandenburg and the Free State of Sachsen (Saxony). Here, 71 pre-checked and classified drilling profiles exist which are combined with erroneous synthetic profiles and used to train the random forest model to predict the quality of the >3000 remaining profiles.

Until now, the BGR’s geological general map series of different scales were not connected to each other in terms of data technology. Accordingly, the GK1000 (Geological Map of the Federal Republic of Germany 1:1,000,000) was previously generated manually from the GÜK250 / GÜK200 (The General Geological Map of the Federal Republic of Germany 1:250,000 / 1:200,000). Within the framework of the AGNES (Automated generalisation/derivation of geological spatial data) project, the generalisation tool “AutoGen” of the State Office for Geology, Raw Materials and Mining in Baden-Württemberg (LGRB) has been adapted using the above-mentioned two general maps, so that a largely automated derivation of small-scale spatial data from large-scale spatial data is possible. The talk will give an overview of the results of the AGNES project and the tool “AutoGen” as well as an outlook on further work in the future.

3D information systems are becoming increasingly important in Hesse (Lehné et al. 2017). The pilot study “Darmstadt_3D”, which delivers urban subsurface data, including technical infrastructure, is highly utilizes by the user community of the city of Darmstadt, the HLNUG and the TUDa. Based on these project experiences, a further project was initiated in 2020 for a small working area in northern Hesse, southeast of Kassel (Lewin et al. 2021). Meanwhile, the working area, geologically located in the Hessian depression, has been expanded to cover the entire city area, spanning approximately 240km².

The database of the structural geological 3D model consists of 9535 quality-checked drillings, several of which have also been used to construct 50 cross sections that help to better elaborate the fault network and layer dip. Additionally, four geological maps (GK25) were harmonized, and nine already existing cross sections were incorporated. The 3D modelling is performed using SKUA-GOCAD and includes the base of the stratigraphic horizons Quaternary, Tertiary, as well as the lower Triassic formations Röt, Solling and Hardegsen. Difficulties arise from varying data densities and conflicting input data.

Initial results reveal a complex tectonic situation, primarily characterized by the WNW-ESE trending graben system of Kassel. Displacement rates of stratigraphic horizons can exceed 100m.

To archive a fully integrated urban 3D-information system for Kassel, the structural geological 3D model will be parameterized (e.g. hydraulic conductivities, radon potentials) and published via GST.

Started as a project at a geoscientific hackathon in 2018 and released to the public in 2020, GST[AR] is an app-based attempt of utilizing Augmented Reality (AR) to bring 3D geological data to almost everyone with a smartphone or tablet. In this way, multiple european-based geological surveys already offer some of their models to experts and interested users alike today. 3D subsurface models especially are great for public engagement and education as they are easier to grasp and fun to interact with. GST[AR] joined the growing list of tools that allow users to visualize geological data without the need for expensive and proprietary software, but chose to do it with the rather novel technology of AR.

AR holds great potential since it is a fun and intuitive way to interact with 3D data and is readily available on most portable devices. Enabling users to directly manipulate a 3D scene is essential for user engagement, but also for gaining a deeper understanding of the spatial relations and dimensions. Simpler methods like creating an animation, or still image, of a 3D model fall short in that regard because they lack the interactive component. In this presentation we will look into the capabilities of the app, ways for everyone to utilize their own models, and the potential this holds for conferences and education.