Estimating the Curie Point Depth Using Magnetic Field Data to Assess the Geothermal Potential
Bibliographic record
Abstract
Summary New technologies that allow geothermal energy production in colder conditions result in interest for geothermal exploration in low heat flux regions. The Province of Québec, eastern Canada, is such a case. Mapping the Curie point depth (CPD) is appealing as an exploration tool due to the scarcity of the direct data. We have revisited a methodology to estimate the CPD using a fractal source distribution model and aeromagnetic data. Our methodology relies on a statistical model of crustal magnetization having a constant magnetization direction and random magnetization amplitude. The shape of the radial average of the logarithm of the power spectrum of magnetic anomalies is predicted using this model. The model parameters (thickness and depth to the top of the magnetic layer, the fractal exponent β and the constant C’) are obtained by calculating the best fit between the theoretical and observed radial power spectra using a non-linear least-square algorithm. Rather than using a constant value for the fractal exponent β, we propose a new calibration workflow based on heat flux measurements and lithology. This workflow includes the use of normal kriging of heat flux data. Mapping the CPD will help identifying potential areas for further detailed exploration programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".