A seismic survey in a permafrost environment: challenges in imaging sediment hosted massive sulphide Zn-Pb deposits
Bibliographic record
Abstract
In this study, seismic data collected in 1998 in the Red Dog mine (Alaska, USA), illustrates the challenges that can occur in acquisition and processing when targeting sulphide ore bodies in a permafrost environment. The region is also unique due to the large quantity of barite. In a permafrost environment, strong surface waves are the main challenge faced when processing. In this paper an fk filter was used to attenuate as much of the surface wave as possible. Strong surface waves, weak high velocity direct waves, and low signal to noise ratios characterize seismic data targeting shallow (200-400m) reflectors in permafrost environments. After reprocessing of the 1998 seismic data an image was obtained for the barite/sulphides that correlated well with the top and bottom of mineralization given from borehole data. Optimal seismic survey design and processing for a 2D/3D survey has been investigated through a 2D seismic modeling study. The modeling study investigated the feasibility of using high resolution seismic techniques to image massive sulphides that are overlain by thick barite in a sediment host rock. Petrophysical parameters for our geologic model, including p-wave velocity and density, were derived from logged borehole data in the Red Dog area. Using a 2D elastic finite difference wave modeling code we were able to generate synthetic high resolution seismic data. The main challenge presented in this type of geologic setting, as mentioned above, is the presence of a strong surface wave that interferes with shallow target reflections. An fk filter can be used to remove surface wave energy provided trace spacing is small enough (4-5m). Additionally, making use of an optimum offset time window to avoid surface and direct waves when processing, has implications for 3D seismic survey design and is an alternative to fk filtering.
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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 | 0.000 |
| 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) | 0.001 | 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".