Improving Deep Exploration with Cost-Effective Geophysical Methods
Bibliographic record
Abstract
Subsurface exploration is rapidly changing and ‘easy to target’ deposits are depleting across the world. This reality has pushed exploration in two directions: re-evaluating known deposits and exploring greater depths. The goal of this thesis was to address these trends in a cost-effective manner. First, by combining geophysical, borehole, and open-source spatial data, a 3D model was synthesized for a volcanogenic massive sulphide (VMS) deposit located in Nash Creek, NB. Evaluating this model showed a need for structural controls to better understand the genesis of the deposit. A lesser-known geophysical system, Extremely Low Frequency EM (ELF-EM), measures ~2km deep and can produce conductivity models. While perfect for Nash Creek, ELF lacked modern software support which limited the modelling that could be done. Using an open-source inversion package, a python script is presented with this thesis that runs inversions of tipper (ELF) data to produce 3D conductivity models. This new workflow was tested at the Key Anacon VMS deposit near Bathurst, NB. A 3D wireframe model derived from geophysical surveying and borehole logs was available to compare with the ELF-EM derived model at Key Anacon. While individual mineralized horizons could not be discerned, a ‘conductive envelope’ follows a very similar strike and dip to the wireframe model. Promising results from Key Anacon led to the re-interpretation of past ELF-EM surveys. The final section of this thesis revisits a survey in Burwash Landing, Yukon to compare conductivity modelling results. The Burwash Landing survey aimed to identify potential geothermal wells drilling sites along the Denali fault. The new 3D model showed a coherent fault trace along strike, as well as eliminated several anomalies the researchers in the original paper could not explain. This improved ELF-EM inversion workflow has greatly improved 3D modelling of deep conductivity contrasts. In future, the techniques outlined here can be applied to various exploration scenarios while following the current trends in exploration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".