3D seismic imaging of volcanogenic massive sulfide deposits in the Flin Flon mining camp, Canada: Part 2 — Forward modeling
Bibliographic record
Abstract
ABSTRACT We applied seismic modeling for a detailed 3D geologic model of the Flin Flon mining camp (Canada) to address some imaging and interpretation issues related to a 17-km2 3D survey acquired in the camp and described in a complementary paper (part 1). A 3D geologic volumetric model of the camp was created based on a compilation of geologic data constraints from drillholes, surface geologic mapping, interpretation of 2D seismic profiles, and 3D surface and grid geostatistical modeling techniques. The 3D modeling methodology was based on a hierarchical approach to account for the heterogeneous spatial distribution of geologic constraints. Elastic parameters were assigned within the model based on core sample measurements and correlation with the different lithologies. The phase-screen algorithm used for seismic modeling was validated against analytic and finite-difference solutions to ensure that it provided accurate amplitude-variation-with-offset behavior for dipping strata. Synthetic data were generated to form zero-offset (stack) volume and also a complete prestack data set using the geometry of the real 3D survey. We found that the ability to detect a clear signature of the volcanogenic massive sulfide with ore deposits is dependent on the mineralization type (pyrite versus pyrrhotite rich ore), especially when ore-host rock interaction is considered. In the presence of an increasing fraction of the host rhyolite rock within the model volume, the response from the lower impedance pyrrhotite ore is masked by that of the rhyolite. Migration tests showed that poststack migration effectively enhances noisy 3D DMO data and provides comparable results to more computationally expensive prestack time migration. Amplitude anomalies identified in the original 3D data, which were not predicted by our modeling, could represent potential exploration targets in an undeveloped part of the camp, assuming that our a priori earth model is sufficiently accurate.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".