Combined surface and borehole seismic imaging in a hard rock terrain: A field test of seismic interferometry
Bibliographic record
Abstract
ABSTRACT Seismic images are inherently directionally biased by the source-receiver geometry. This directional bias is particularly problematic for seismic imaging in hard rock terrains where structural dips may have any orientation with respect to the surface. We tested a technique for partially mitigating directional bias by combining surface and borehole seismic data and evaluated the results of a first field test of the technique. In this technique, surface data acquired using standard 2D acquisition procedures were combined with borehole data derived from a walk-away vertical seismic profile (VSP). The VSP data were transformed into the borehole datum using seismic interferometry. The interferometry created virtual shot records comprising sources and receivers in the borehole. The virtual shot records were then processed, using standard common midpoint techniques, resulting in an image from the borehole datum. The combination of the surface and borehole data increased the range of illumination angles resulting in seismic images that included reflections from structures with a wider range of dips than is available to surface profiling alone. The field test demonstrated that the surface and borehole data provide complementary information, which is more than either data set alone can provide. The test also verified the robustness of the virtual source technique even when the original VSP data are highly contaminated by high-amplitude tube waves. These results demonstrated that the combined imaging approach has significant potential for application in the polydeformed hard rock domains often encountered in minerals exploration.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".