Multiple-point statistical simulation for hydrogeological models: 3D training image development and conditioning strategies

Most studies about the application of geostatistical simulations based on multiple-point statistics (MPS) to hydrogeological modelling focus on relatively fine-scale models and concentrate on the estimation of facies-level, structural uncertainty. Much less attention is paid to the use of input data and optimal construction of training images. For instance, even though the training image should capture a set of spatial geological characteristics to guide the simulations, the majority of the research still relies on 2D or quasi-3D training images. In the present study, we demonstrate a novel strategy for 3D MPS modelling characterized by: (i) realistic 3D training images, and (ii) an effective workflow for incorporating a diverse group of geological and geophysical data sets. The study covers an area of 2810 km2 in the southern part of Denmark. MPS simulations are performed on a subset of the geological succession (the lower to middle Miocene sediments) which is characterized by relatively uniform structures and dominated by sand and clay. The simulated domain is large and each of the geostatistical realizations contains approximately 45 million voxels with size 100 m × 100 m × 5 m. Data used for the modelling include water well logs, high-resolution seismic data, and a previously published 3D geological model. We apply a series of different strategies for the simulations based on data quality, and develop a novel method to effectively create observed sand/clay spatial trends. The training image is constructed as a small 3D voxel model covering an area of 90 km2. We use an iterative training image development strategy and find that even slight modifications in the training image create significant changes in simulations. Thus, the study underlines that it is important to consider both the geological environment, and the type and quality of input information in order to achieve optimal results from MPS modelling. In this study we present a possible workflow to build the training image and effectively handle different types of input information to perform large-scale geostatistical modellin

3D modelling of the dolerite dyke network within the Siilinjärvi phosphate deposit

Non peer reviewe

A 3D GEOLOGICAL MODEL OF THE NASIA SUB-BASIN, NORTHERN GHANA-INTERPRETATIONS FROM THE INVERSION RESULTS OF REPROCESSED GEOTEM DATA

Inversion of regional-scale airborne electromagnetic (AEM) data was used in building a 3D geologic model of the Nasia Sub-Basin, Northern Ghana. Geological interpretation of the AEM data was guided by a priori knowledge obtained from previous research in the area, geological maps and reports. A key requirement of the modelling process was to define the subsurface stratigraphy of the area, chiefly to provide 1) a detailed stratigraphical context for hydrogeologic use, 2) a repository of comprehensive stratigraphic knowledge of the study area which will be easily accessible. The AEM measurements, consisting of GEOTEM data, was originally collected for mineral exploration purposes. The B-field data have been (re)processed to enhance the resolution and inverted by using spatial constraints to preserve the lateral and vertical coherence. During the processing and inversion phases, the regularization strategies and associated parameters have been tuned by following an iterative approach characterized by a tight collaboration between geologists and geophysicists to retrieve the geophysical model that is in the best agreement, not only with the geophysical data, but also with the geological expectations. The new (pseudo-)3D inversion showed very different features with respect to the previous Conductivity-Depth Images (CDI). The new geophysical model led to new interpretations of the geological settings and to the construction of a comprehensive 3D geomodel of the basin based on the integration of AEM and borehole information. Nevertheless, in order to have a model, suitable for hydrogeological characterization, it will be necessary to include more details regarding the upper, weathered zone as the AEM survey was optimized to have a very deep penetration and not an extremely high shallow resolution. These aspects will be remedied by the inclusion, for example, of the interpretations from Electrical Resistivity Tomography (ERT) data, which will provide the necessary resolution in the upper sections

VGC 2023 - Unveiling the dynamic Earth with digital methods: 5th Virtual Geoscience Conference: Book of Abstracts

Conference proceedings of the 5th Virtual Geoscience Conference, 21-22 September 2023, held in Dresden. The VGC is a multidisciplinary forum for researchers in geoscience, geomatics and related disciplines to share their latest developments and applications.:Short Courses 9 Workshops Stream 1 10 Workshop Stream 2 11 Workshop Stream 3 12 Session 1 – Point Cloud Processing: Workflows, Geometry & Semantics 14 Session 2 – Visualisation, communication & Teaching 27 Session 3 – Applying Machine Learning in Geosciences 36 Session 4 – Digital Outcrop Characterisation & Analysis 49 Session 5 – Airborne & Remote Mapping 58 Session 6 – Recent Developments in Geomorphic Process and Hazard Monitoring 69 Session 7 – Applications in Hydrology & Ecology 82 Poster Contributions 9

Seismic interpretation of sill complexes in sedimentary basins : implications for the sub-sill imaging problem

Acknowledgements: We thank reviewers Craig Magee and Murray Hoggett for considerate and insightful reviews that considerably improved this manuscript. The LIDAR data were acquired by Julien Vallet and Samuel Pitiot of Helimap Systems. We acknowledge NORSAR for an academic licence of the seismic modelling software SeisRoX, which was used to generate synthetic seismograms in this study, and NORSAR-2D, which was used for analysis of seismic propagation through the overburden models. The virtual outcrop was visualized and interpreted using LIME (http://virtualoutcrop.com/lime). We also acknowledge Tore Aadland for writing invaluable scripts used for import of the outcrop models to seismic modelling software, and Gijs A. Henstra and Björn Nyberg for assistance in the field. Funding: Funding for data acquisition was provided from the Research Council of Norway through the PETROMAKS project 193059 and the FORCE Safari project. Funding for data analysis and modelling was provided from PETROMAKS through the Trias North project (234152).Peer reviewedPostprin