Improved target illumination at Ludvika mines of Sweden through seismic‐interferometric surface‐wave suppression
Bibliographic record
Abstract
ABSTRACT In mineral exploration, new methods to improve the delineation of ore deposits at depth are in demand. For this purpose, increasing the signal‐to‐noise ratio through suitable data processing is an important requirement. Seismic reflection methods have proven to be useful to image mineral deposits. However, in most hard rock environments, surface waves constitute the most undesirable source‐generated or ambient noise in the data that, especially given their typical broadband nature, often mask the events of interest like body‐wave reflections and diffractions. In this study, we show the efficacy of a two‐step procedure to suppress surface waves in an active‐source reflection seismic dataset acquired in the Ludvika mining area of Sweden. First, we use seismic interferometry to estimate the surface‐wave energy between receivers, given that they are the most energetic arrivals in the dataset. Second, we adaptively subtract the retrieved surface waves from the original shot gathers, checking the quality of the unveiled reflections. We see that several reflections, judged to be from the mineralization zone, are enhanced and better visualized after this two‐step procedure. Our comparison with results from frequency‐wavenumber filtering verifies the effectiveness of our scheme, since the presence of linear artefacts is reduced. The results are encouraging, as they open up new possibilities for denoising hard rock seismic data and, in particular, for imaging of deep mineral deposits using seismic reflections. This approach is purely data driven and does not require significant judgment on the dip and frequency content of present surface waves, which often vary from place to place.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".