Elastic finite-difference modeling of volcanic-hosted massive sulfide deposits: A case study from Half Mile Lake, New Brunswick, Canada
Bibliographic record
Abstract
ABSTRACT We present elastic finite-difference modeling results over a geologically realistic 2D representation of the Half Mile Lake volcanic-hosted massive sulfide deposit, New Brunswick, Canada. The model is constrained by geologic information from surface mapping and boreholes, whereas petrophysical properties are provided by wireline logging data acquired in two boreholes intersecting different parts of the deposit. We analyzed the P-P, P-S, S-P, and S-S responses of the lower and deep mineralized zones and assessed some compositional effects by substituting massive sulfides with gabbro properties in the model. Finite-difference modeling results predict complex scattering signature associated with the lower and deep sulfide zones. Both zones scattered back P-P, P-S, S-P, and S-S waves generally having strongest amplitudes in the stratigraphy down-dip direction. The P-S, S-P, and S-S scattered waves, if properly recorded on multicomponent data, represent useful signal that could help the targeting of deep sulfide mineralization. Finite-difference simulations further reveal phase-reversals on P-P wavefields scattered at the lower and deep zones. The phase reversals are not observed for gabbro inclusions, suggesting that this signature could be used to discriminate gabbro units from sulfide mineralization. The finite-difference simulation successfully reproduces many events of the VSP data, in particular P-S and S-S events on the radial component and P-P and S-P events on the vertical component. Comparison with 3D data is rather poor and only shows weak correlation with P-P events from the lower and deep zones. Despite the poor correlation, a prestack time migrated S-P section displays an amplitude anomaly at the location of the deep zone, suggesting that S-P waves were recorded on the 3D data, although this survey was acquired with explosive sources and vertical geophones.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".