Magnetotelluric Investigations of the Tintina Fault Zone for Geothermal Exploration at Watson Lake, Yukon
Bibliographic record
Abstract
Geothermal energy is being investigated as a renewable source of electricity and heat production for communities in northern Canada. The successful development of a geothermal energy project requires three key components: (1) there must be a source of sufficient heat at near-surface depths. Typically, geothermal projects require temperatures of at least 150°C for efficient electricity production while lower temperature resources can be directly used for heating, (2) the rock must contain pore fluids that can be used to transport the heat to the surface through pores, fractures, and boreholes, (3) the host rock must have sufficient permeability such that fluids can move easily through the subsurface in pores and fractures. The community of Watson Lake, Yukon may be a potential site for a geothermal energy project. Studies have show that the area surrounding Watson Lake has high values of surface heat flow and temperatures that may exceed 200°C at depths of 6 km below the surface (Majorowicz and Grasby, 2014). The nearby Tintina Fault zone, a dextral strike-slip fault system, may represent a zone of elevated porosity through which pore fluids could flow and allow heat to be extracted for the production of electricity and heat. However, the exact location of the Tintina Fault zone is poorly constrained due to coverage by glacial deposits. Geophysical methods such as the magnetotelluric method must be utilized to better constrain the location of the Tintina Fault and to identify potential geothermal reservoirs within the fault zone. The magnetotelluric (MT) method measures naturally occurring variations of Earth’s magnetic and electric fields. Analysis of the time variations of these fields can be used to determine the subsurface resistivity structure of the Earth. The MT method is able to locate subsurface areas of low electrical resistivity that may be due to trapped pore fluids in the crust and upper mantle. This makes it one of the preferred geophysical methods for geothermal exploration. During July and August of 2021, 36 broadband MT stations were deployed near the community of Watson Lake. These MT stations recorded time series data that produce apparent resistivity, phase, and tipper data in the frequency range of 0.001 – 100 Hz when transformed to the frequency domain. The data were filtered to remove noise from the signals. These data were then utilized in a 3D MT inversion to create a model of the spatial variation of electrical resistivity in three dimensions. This was done using the ModEM algorithm developed by Kelbert et al., (2014), which was run on the Cedar computing cluster provided by the Digital Research Alliance of Canada. The resulting electrical resistivity model displays four key features of interest for geothermal development at Watson Lake. Two conducting bodies were resolved by the inversion, with electrical resistivity values in the ranges of 1 – 10 Ωm and 10 – 30 Ωm respectively. These features were named C1 and C2. Conductor C1 is located between Highway 37 and the Liard River and is approximately 15 km wide. The top of the conductor is situated at 500 m below sea level, and it extends to a depth of 3 km below sea level. Conductor C2 is located North of the Alaska Highway and East of Watson Lake, with a width of 3 km. The top of C2 is situated at 500 m below sea level and it extends to a depth of 10 km below sea level. Two resistive features were resolved with resistivity in the range of 300 – 1000 Ωm and named R1 and R2. It was determined that the low resistivity of C1 and C2 were most likely the result of either saline pore fluids or interconnected graphite films present within the block of rock located within the Tintina Fault zone. Analysis of the phase tensor data indicates that the Tintina Fault has a geologic strike of between N30°W and N45°W and is likely located further west than previously thought (Yukon Geological Survey, 2022). Future studies are necessary to better characterize the geothermal potential of Watson Lake. These should include expanding the grid of MT stations to better constrain the features detected by the MT study and drilling test wells to sample pore fluids as well as measure the thermal gradient of the area.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".