Modeling the thermal behavior of geothermal systems at Mount Meager, Southwestern Canada, using artificial neural networks and audio-magnetotellurics
Bibliographic record
Abstract
ABSTRACT Precise modeling of subsurface temperatures is crucial for a comprehensive understanding and the exploitation of geothermal reservoirs. An artificial neural network method is used to estimate the subsurface temperature by analyzing 3D resistivity models derived from audio-magnetotelluric data and temperature logs from the Mount Meager Volcanic Complex (MMVC), southwestern British Columbia, Canada. A multilayer perceptron algorithm is used to capture the complexity of the data and estimate the subsurface temperature to a depth of 3 km. The model is trained on 70% of the 1160 data points, validated using the remaining 30%, and fine tuned based on data and error analysis. Subsequently, it is tested on three temperature logs that are not part of the training process, to ensure the robustness and reliability of the model predictions. The final model achieved a root mean square (rms) of 13.1°C (5% error) and an R2 value of 0.97 when estimating the subsurface temperature using the training data set, which is much more promising than using conventional analytical models that indicate an rms of 61%. The 3D temperature model of the MMVC is correlated with the available geologic data. This methodology offers a cost effective and noninvasive alternative for the thermal characterization of potential geothermal reserves, providing a powerful tool for resource development.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".