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Record W7106032985 · doi:10.7939/83032

Investigating the Geothermal Energy Potential and Permafrost Structure in Nunavut with Magnetotelluric Data

2025· dissertation· en· W7106032985 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Alberta Library · 2025
Typedissertation
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMagnetotelluricsPermafrostGeothermal gradientGeothermal energyFossil fuelInversion (geology)Electrical resistivity tomographyGroundwater

Abstract

fetched live from OpenAlex

To reduce reliance on fossil fuels, Canada is investigating the development of alternative energy systems that have lower carbon emissions than those currently in use. This is particularly important for communities in the Canadian Arctic, where the vast majority of electricity and heat is currently generated by burning fossil fuels. Geothermal energy systems have the potential to provide an alternate source of heat in this region, and their feasibility in remote Northern settlements is being investigated. Knowledge of subsurface rock type and groundwater conditions is an essential part of exploration for geothermal energy. In summer 2023 a group from the University of Alberta collected magnetotelluric (MT) data at two communities in Nunavut to investigate the subsurface conditions. The MT method can determine the subsurface electrical resistivity to depths of several kilometers. Resistivity is a parameter that is sensitive to the temperature and porosity of the subsurface, and to the quantity and salinity of groundwater. At Cambridge Bay, MT data was collected at 26 stations in July 2023 in an east-west array that covered an area of 17 x 8 km. At Resolute Bay, 33 stations were recorded in August 2023 in a region that was 8 x 5 km. Data from an additional 11 stations were previously recorded at Resolute Bay in 2022. The time series data were processed to give frequency domain data in the frequency band 500 – 0.001 Hz. The data were then used to generate models of subsurface resistivity using a combination of 1-D and 3-D inversion methods. Interpretation of the resistivity used knowledge of subsurface temperatures and experiments on frozen / unfrozen sedimentary rocks that contained saline ground water. At Cambridge Bay the resistivity model was characterized by two layers: (1) a 100 m thick surficial layer that has a low-resistivity due to the presence of limestone saturated with partially frozen saline pore water and (2) a deeper high-resistivity layer interpreted as limestone containing low salinity pore water. Knowledge of the geothermal gradient suggests that the upper part of this surface layer was frozen and the lower part unfrozen. At Resolute Bay, the resistivity model was characterized by three layers: (1) a high-resistivity limestone layer containing partially frozen saline pore water (2) a low-resistivity layer containing unfrozen saline pore fluids, and (3) a deeper high-resistivity layer containing low salinity pore water. Porosity estimates of the subsurface below Cambridge Bay were low, indicating poor natural reservoir potential, whereas the subsurface below Resolute Bay may have greater porosity. These results also indicate that the geothermal gradients at both communities are relatively low, suggesting that direct use geothermal installations are more feasible at each location, and engineered geothermal systems (EGS) may be required to produce the reservoir characteristics required for geothermal use. The low resistivity of the 100 m thick surface layer at Cambridge Bay may have originated in the diffusion of seawater from the surface when the land surface was submerged after the retreat of the Laurentide Ice sheet. Simple 1-D diffusion modelling is consistent with the timescales and layer thickness of approximately 100 m.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.597
Threshold uncertainty score0.915

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.171
Teacher spread0.165 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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