Three-dimensional forward modelling and inversion of magnetotelluric data using unstructured meshes for understanding realistic geological systems: method development, algorithms and model construction for the Lalor deposit, Manitoba
Bibliographic record
Abstract
The work presented here covers three major branches of geophysical prospecting including the development of model construction routines for realistic subsurface structures, forward modelling and magnetotelluric inversion strategies. Using a combination of tools, a routine is developed that produces a sealed model using tessellated geological surfaces representing lithostratigraphic contacts between rock units of the Lalor VMS deposit. This model is suitable for generating volumetric tetrahedral meshes required for mesh-based modelling and inversion of geophysical data. A finite-element based forward modelling technique is developed that discretizes the magnetotelluric problem on un-structured tetrahedral meshes. These meshes are suitable for producing realistic complex-shaped features. Magnetotelluric responses for the Lalor model are successfully calculated. A model space minimum-structure inversion is developed that is based on the iterative Gauss-Newton approach using sensitivity matrix-vector products calculated implicitly by finite-element based forward and pseudo-forward solutions.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".