Three-Dimensional Modeling of Geothermal Systems in the Garibaldi Volcanic Belt, Canada, Using Magnetotelluric Data
Bibliographic record
Abstract
Geothermal systems are practical when the proper temperature settings, geological structures, and petrophysical characteristics exist near the ground surface, all of which should be evaluated to identify economically viable reserves.This dissertation presents procedures to examine (1) geological structures, (2) fluid flow pathways and their physical properties, and (3) subsurface temperature of geothermal systems by incorporating various geoscience datasets, including audio-magnetotelluric (AMT) data, rock physical properties, well logs, and fluid sample data.To examine the geological structures, this dissertation uses a comprehensive AMT dataset focused on Mount Meager in the Garibaldi Volcanic Belt, the location of which has one of the highest geothermal potentials in Canada.As a result of inverting the AMT data, the 3-D resistivity model of the area indicates conductive features near the surface south of Pylon Peak that are related to hydrothermal alteration and can act as the caprock of the geothermal system.The caprock overlays fractured quartz diorite with a higher resistivity expression, coinciding with hightemperature zones (T>250 °C).Furthermore, the resistivity model illustrates possible pathways through which the hydrothermal fluids emerged as thermal springs at the surface.This dissertation introduces petrophysical relationships for Mount Meager to examine the flow pathways.Various petrophysical models and known empirical relations are evaluated using core samples and well logs, which indicates that these pathways can account for porosity up to iii 8.5% and permeability of the order 0.249 mD.The results suggest that fractures may provide the primary way of flow in Mount Meager.Therefore, local fault and fracture networks are studied.Additionally, this dissertation determines the relationship between porosity and thermal conductivity at Mount Meager.To evaluate the subsurface temperature, a novel method is presented to simulate the temperature using the 3-D resistivity model.A machine-learning technique is utilized to capture the complexity of the data and estimate the relationships between electrical resistivity and temperature.Finally, the temperature model is tested and validated using borehole temperature data.This dissertation indicates that integrating AMT data with borehole logs and laboratory measurements is crucial to create 3-D models of geothermal systems.These models will help in different phases of geothermal energy development at Mount Meager.iv Dedication Dedicated to whom encouraged me and taught me to question everything and think differently and critically!
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".