Combining magnetotelluric, DC resistivity, and time-domain electromagnetic data in geothermal exploration: An example from the M’Deek Geothermal Field in Western British Columbia, Canada
Bibliographic record
Abstract
The M’Deek geothermal field is an extensional, fracture-controlled hydrothermal system located in western British Columbia, Canada. The system has surface expressions of hot springs and pockmarks. Heat is derived from past subduction on the west coast of North America. Electric and electromagnetic (EM) geophysical methods are commonly used in the exploration for geothermal resources due to their sensitivity to the presence of both fluids and regions of hydrothermal alteration. In the initial stages of exploration, prior to this study, time-domain electromagnetics (TDEM) and direct-current (DC) resistivity methods were used to measure the near-surface resistivity structure. These datasets were inverted to obtain resistivity models, which showed a ∼100 m thick near-surface conductor. However, both of these methods have a limited depth of exploration. To image the deeper structure of the geothermal field, broadband magnetotelluric (MT) data were acquired in 2020 and 2022. The TDEM and DC inversion models were incorporated into the starting models for the MT inversion in order to improve the resolution of the near-surface conductor. The preferred 3D MT inversion resistivity model of the M’Deek geothermal field shows a sub-vertical low resistivity feature at depths between 500 m and 5 km. This feature was interpreted to be the fault system, which acts as a conduit for the thermal waters that supply the hot springs. The fault system is the target for ongoing geothermal exploration. Sensitivity analysis showed that the fault had an estimated width of 400–1000 m, a strike between S and S45°W and a porosity of 10–20 %.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".