Seismic imaging of a near-vertical vein using controlled-source seismic interferometry
Bibliographic record
Abstract
Abstract New methodologies for narrow-vein mining are making thin, steeply dipping mineralized veins economically viable mining targets. Drilling is the normal method for delineation and resource evaluation prior to mining. However, for the evaluation of narrow veins, significant drilling of barren rock is required. Controlled-source seismic interferometry has the potential to significantly decrease the costs of target delineation by providing high-resolution seismic images of thin, steeply dipping mineralized veins. We present a case study that employs seismic interferometry in conjunction with a walkaway vertical seismic profiling survey to image a thin (0.5–4 m), steeply dipping barite vein. The footprint of the seismic data acquisition is relatively small and compatible with operations in areas with limited access (e.g., mining camps). The technique requires some care with experimental design and data processing, but it is clearly demonstrated to produce a high-resolution seismic image. Furthermore, we demonstrate that inversion of the depth-migrated image can be used to quantify vein thickness and provide direct information for resource evaluation and reserve estimation.
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 | 0.001 | 0.001 |
| 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".