Teaching with digital geology in the high Arctic: opportunities and challenges
-
Published:2021-09-28
Issue:3
Volume:4
Page:399-420
-
ISSN:2569-7110
-
Container-title:Geoscience Communication
-
language:en
-
Short-container-title:Geosci. Commun.
Author:
Senger KimORCID, Betlem Peter, Grundvåg Sten-Andreas, Horota Rafael Kenji, Buckley Simon JohnORCID, Smyrak-Sikora Aleksandra, Jochmann Malte MichelORCID, Birchall Thomas, Janocha Julian, Ogata KeiORCID, Kuckero Lilith, Johannessen Rakul Maria, Lecomte IsabelleORCID, Cohen Sara Mollie, Olaussen SnorreORCID
Abstract
Abstract. 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.
Funder
University of the Arctic SFI Offshore Mechatronics
Publisher
Copernicus GmbH
Reference99 articles.
1. Ahlborn, M. and Stemmerik, L.: Depositional evolution of the Upper
Carboniferous-Lower Permian Wordiekammen carbonate platform, Nordfjorden
High, central Spitsbergen, Arctic Norway, Norw. J. Geol., 95, 91–126, 2015. 2. Anell, I., Braathen, A., and Olaussen, S.: The Triassic-Early Jurassic of
the northern Barents Shelf: a regional understanding of the Longyearbyen
CO2 reservoir, Norw. J. Geol., 94, 83–98, 2014. 3. Anell, I., Lecomte, I., Braathen, A., and Buckley, S.: Synthetic seismic
illumination of small-scale growth faults, paralic deposits and low-angle
clinoforms: A case study of the Triassic successions on Edgeøya, NW
Barents Shelf, Mar. Petrol. Geol., 77, 625–639, https://doi.org/10.1016/j.marpetgeo.2016.07.005, 2016. 4. Beka, T. I., Senger, K., Autio, U. A., Smirnov, M., and Birkelund, Y.:
Integrated electromagnetic data investigation of a Mesozoic CO2 storage
target reservoir-cap-rock succession, Svalbard, J. Appl. Geophys., 136, 417–430, 2017. 5. Bergen, K. J., Johnson, P. A., Maarten, V., and Beroza, G. C.: Machine
learning for data-driven discovery in solid Earth geoscience, Science, 363, eaau0323, https://doi.org/10.1126/science.aau0323, 2019.
Cited by
11 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|