Geologically driven 3D modelling of physical rock properties in support of interpreting the seismic response of the Lalor volcanogenic massive sulphide deposit, Snow Lake, Manitoba, Canada
Bibliographic record
Abstract
Abstract 3D lithofacies and physical rock property models were generated to interpret 3D seismic data acquired over the Lalor volcanogenic massive sulphide deposit, Manitoba, Canada. The lithofacies model revealed that strong seismic reflectivity is associated with ore–host rock and mafic–felsic lithofacies contacts, including their hydrothermally altered equivalents. Different physical rock property models were subjected to 3D seismic forward modelling using the SOFI3D finite difference code. Seismic synthetics from discrete and interpolated models in which kriging of P-wave velocity and density was conditioned by curvilinear grids conformable to the 3D-modelled geological structure showed a much better match to the seismic data in comparison with those generated by kriging in Cartesian space. Synthetics from these curvilinear grid models corroborate the origin of seismic reflectors, as qualitatively inferred from the lithofacies model. Seismic synthetics generated from physical rock property models in which physical rock properties were augmented by densely sampled secondary variables, such as FeO percentage, enhanced lateral continuity of seismic reflectivity, although these co-kriged petrophysical models were not more accurate than their kriged equivalents. The physical rock property modelling methodology was also useful for testing the utility of passive interferometric seismic surveys, as this highlighted the limitations of the discrete physical rock property model.
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 | 0.000 |
| 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".