Teaching with digital geology in the high Arctic: opportunities and challenges

The Covid-19 pandemic occurred at a time of major revolution in the geosciences – the era of digital geology. Digital outcrop models (DOMs) acquired from consumer drones, processed using user-friendly photogrammetric software and shared with the wider audience through online platforms are a cornerstone of this digital geological revolution. Integration of DOMs with other geoscientific data, such as geological maps, satellite imagery, terrain models, geophysical data and field observations, strengthens their application in both research and education. Teaching geology with digital tools advances students' learning experience by providing access to high-quality outcrops, enhancing visualization of 3D geological structures and improving data integration. Similarly, active use of DOMs to integrate new field observations will facilitate more effective fieldwork and quantitative research. From a student's perspective, georeferenced and scaled DOMs allow for an improved appreciation of scale and of 3D architecture, which is a major threshold concept in geoscientific education. DOMs allow us to bring geoscientists to the outcrops digitally, which is particularly important in view of the Covid-19 pandemic that restricts travel and thus direct access to outcrops. At the University Centre in Svalbard (UNIS), located at 78∘ N in Longyearbyen in Arctic Norway, DOMs are actively used even in non-pandemic years, as the summer field season is short and not overlapping with the Bachelor “Arctic Geology” course package held from January to June each year. In 2017, we at UNIS developed a new course (AG222 “Integrated Geological Methods: From Outcrop To Geomodel”) to encourage the use of emerging techniques like DOMs and data integration to solve authentic geoscientific challenges. In parallel, we have established the open-access Svalbox geoscientific portal, which forms the backbone of the AG222 course activities and provides easy access to a growing number of DOMs, 360∘ imagery, subsurface data and published geoscientific data from Svalbard. Considering the rapid onset of the Covid-19 pandemic, the Svalbox portal and the pre-Covid work on digital techniques in AG222 allowed us to rapidly adapt and fulfil at least some of the students' learning objectives during the pandemic. In this contribution, we provide an overview of the course development and share experiences from running the AG222 course and the Svalbox platform, both before and during the Covid-19 pandemic.

Teaching with digital geology in the high Arctic: opportunities and challenges

The Covid-19 pandemic occurred at a time of major revolution in the geosciences – the era of digital geology. Digital outcrop models (DOMs) acquired from consumer drones, processed using user-friendly photogrammetric software and shared with the wider audience through online platforms are a cornerstone of this digital geological revolution. Integration of DOMs with other geoscientific data, such as geological maps, satellite imagery, terrain models, geophysical data and field observations strengthens their application in both research and education. Teaching geology with digital tools advances students’ learning experience by providing access to spectacular outcrops, enhancing visualization of 3D geological structures and improving data integration. Similarly, active use of DOMs to integrate new field observations will facilitate more effective fieldwork and quantitative research. From a student’s perspective, geo-referenced and scaled DOMs allow an improved appreciation of scale and of 3D architecture, a major threshold concept in geoscientific education.In view of the Covid-19 pandemic, DOMs allow to bring geoscientists to the outcrops digitally. At the University Centre in Svalbard (UNIS), located at 78° N in Longyearbyen in Arctic Norway, DOMs are actively used even in non-pandemic years, as the summer field season is short and not overlapping with the Bachelor “Arctic Geology” course package held from January to June each year. In 2017, we at UNIS developed a new course (‘AG222: Integrated Geological Methods: from outcrop to geomodel’) to encourage the use of emerging techniques like DOMs and data integration to solve authentic geoscientific challenges. In parallel, we have established the open access Svalbox geoscientific portal, which forms the backbone of the AG222 course activities and provides easy access to a growing number of DOMs, 360° imagery, subsurface data and published geoscientific data from Svalbard. Considering the rapid onset of the Covid-19 pandemic, the Svalbox portal and the pre-Covid work on digital techniques in AG222 allowed us to rapidly adapt and fulfill at least some of the students’ learning objectives during the pandemic. In this contribution, we provide an overview of the course development and share experiences from running the AG222 course and the Svalbox platform, both before and during the Covid-19 pandemic.

GemGIS – GemPy Geographic: Open-Source Spatial Data Processing for Geological Modeling

<p>Geological modeling methods are widely used to represent subsurface structures for a multitude of applications – from scientific investigations, over natural resource and reservoir studies, to large-scale analyses and geological representations by geological surveys. In recent years, we have seen an increase in the availability of geological modeling methods. However, many of these methods are difficult to use due to preliminary data processing steps, which can be specifically difficult for geoscientific data in geographic coordinate systems.</p><p>We attempt to simplify the access to open-source spatial data processing for geological modeling with the development of GemGIS, a Python-based open-source library. GemGIS wraps and extends the functionality of packages known to the geo-community such as GeoPandas, Rasterio, OWSLib, Shapely, PyVista, Pandas, NumPy and the geomodelling package GemPy. The aim of GemGIS, as indicated by the name, is to become a bridge between conventional geoinformation systems (GIS) such as ArcGIS and QGIS, and geomodelling tools such as GemPy, allowing simpler and more automated workflows from one environment to the other.</p><p>Data within the different disciplines of geosciences are often available in a variety of data formats that need to be converted or transformed for visualization in 2D and 3D and subsequent geomodelling methods. This is where GemGIS comes into play. GemGIS is capable of working with vector data created in GIS systems through GeoPandas, Pandas and Shapely, with raster data through rasterio and NumPy, with data obtained from web services such as maps or digital elevation models through OWSLib and with meshes through PyVista. Support for geophysical data and additional geo-formats are constantly added.</p><p>The GemGIS package already contains several tutorials explaining how the different modules can be used to process spatial data. It was decided against creating new data classes in case users are already familiar with concepts such as (Geo-)DataFrames in (Geo-)Pandas or PolyData/Grids in PyVista.</p><p>The GemGIS package is hosted at https://github.com/cgre-aachen/gemgis, the documentation is available at https://gemgis.readthedocs.io/en/latest/index.html. GemGIS is also available on PyPi. You can install GemGIS in your Python environment using ‘pip install gemgis’.</p><p>We welcome contributions to the project through pull requests and are open to suggestions and comments, also over Github issues, especially about possible links to other existing software developments and approaches to integrate geoscientific data processing and geomodelling.</p>

dh2loop 1.0: an open-source python library for automated processing and classification of geological logs

Exploration and mining companies rely on geological drill core logs to target and obtain initial information on geology of the area to build models for prospectivity mapping or mine planning. A huge amount of legacy drilling data is available in geological survey but cannot be used directly as it is compiled and recorded in an unstructured textural form and using different formats depending on the database structure, company, logging geologist, investigation method, investigated materials and/or drilling campaign. It is subjective and plagued with uncertainty as it is likely to have been conducted by tens to hundreds geologists, all of whom would have their own personal biases. However, this is valuable information that adds value to geoscientific data for research and exploration, specifically in efficiently targeting sustainable new discoveries and providing better shallow subsurface constraints for 3D geological models. dh2loop (https://github.com/Loop3D/dh2loop) is an open-source python library that provides the functionality to extract and standardize geologic drill hole data and export it into readily importable interval tables (collar, survey, lithology). In this contribution, we extract, process and classify lithological logs from the Geological Survey of Western Australia Mineral Exploration Reports Database in the Yalgoo-Singleton Greenstone Belt (YSGB) region. For this study case, the extraction rate for collar, survey and lithology data is respectively 93 %, 865 and 34 %. It also addresses the subjective nature and variability of nomenclature of lithological descriptions within and across different drilling campaigns by using thesauri and fuzzy string matching. 86% of the extracted lithology data is successfully matched to lithologies in the thesauri. Since this process can be tedious, we attempted to test the string matching with the comments, which resulted to a matching rate of 16 % (7,870 successfully matched records out of 47,823 records). The standardized lithological data is then classified into multi-level groupings that can be used to systematically upscale and downscale drill hole data inputs for multiscale 3D geological modelling. dh2loop formats legacy data bridging the gap between utilization and maximization of legacy drill hole data and drill hole analysis functionalities available in existing python libraries (lasio, welly, striplog).

Guidance for Autonomous Underwater Vehicles in Confined Semistructured Environments

In this work, we present the design, implementation, and testing of a guidance system for the UX-1 robot, a novel spherical underwater vehicle designed to explore and map flooded underground mines. For this purpose, it needs to navigate completely autonomously, as no communications are possible, in the 3D networks of tunnels of semistructured but unknown environments and gather various geoscientific data. First, the overall design concepts of the robot are presented. Then, the guidance system and its subsystems are explained. Finally, the system’s validation and integration with the rest of the UX-1 robot systems are presented. A series of experimental tests following the software-in-the-loop and the hardware-in-the-loop paradigms have been carried out, designed to simulate as closely as possible navigation in mine tunnel environments. The results obtained in these tests demonstrate the effectiveness of the guidance system and its proper integration with the rest of the systems of the robot, and validate the abilities of the UX-1 platform to perform complex missions in flooded mine environments.

Capture the variation of the pore pressure with different geological age from seismic inversion study in the Jaisalmer sub-basin, India

Geoscientific evidence shows that various parameters such as compaction, buoyancy effect, hydrocarbon maturation, gas effect and tectonic activities control the pore pressure of sub-surface geology. Spatially controlled geoscientific data in the tectonically active areas is significantly useful for robust estimation of pre-drill pore pressure. The reservoir which is tectonically complex and pore pressure is changing frequently that circumference motivated us to conduct this study. The changes in pore pressure have been captured from the fine-scale to the broad scale in the Jaisalmer sub-basin. Pore pressure variation has been distinctly observed in pre- and post-Jurassic age based on the current study. Post-stack seismic inversion study was conducted to capturing the variation of pore pressure. Analysis of low-frequency spectrum and integrated interval velocity model provided a detailed feature of pore pressure in each compartment of the study area. Pore pressure estimated from well log data was correlated with seismic inversion based result. Based on the current study one well has been proposed where pore pressure was estimated and two distinguished trends are identified in the study zone. The approaches of the current study were analysed thoroughly and it will be highly useful in complex reservoir condition where pore pressure varies frequently.

Geophysics introduces new section on multiphysics and joint inversion

Geophysicists seek to extract useful and potentially actionable information about the subsurface by interpreting various types of geophysical data together with prior geologic information. It is well recognized that reliable imaging, characterization, and monitoring of subsurface systems require integration of multiple sources of information from a multitude of geoscientific data sets. With increasing data volumes and computational power, new data types, constant development of inversion algorithms, and the advent of the big data era, Geophysics editors see multiphysics integration as an effective means of meeting some of the challenges arising from imaging subsurface systems with higher resolution and reliability as well as exploring geologically more complicated areas. To advance the field of multiphysics integration and to showcase its added value, Geophysics will introduce a new section “Multiphysics and Joint Inversion” in 2021. Submissions are accepted now.