Illumination Compensation with Seismic-While-Drilling Plus Surface Seismic Imaging
Bibliographic record
Abstract
Summary The drill-bit generates significant elastic wave energy whose ray paths are unique relative to those induced by standard surface seismic. Provided that we understand the challenges associated with characterizing the source radiation properties of the drill bit- rock interaction, the data arising from seismic-while-drilling (SWD) are complementary to surface data, and have the potential to enhance geophysical evaluation of the subsurface. Hence, it brings an opportunity to address seismic illumination issue by adding new measurements into the imaging problem. In this paper, we carry out a feasibility study of the SWD method to mitigate the illumination problem in imaging. Source signature estimation of the drill bit- rock interaction is a necessary step for pre-stack migration of the SWD dataset. To do so, we applied a multichannel sparse blind deconvolution technique to estimate the signature and later, we feed the signature into the SWD imaging workflow. Finally, we merge the SWD image with the surface seismic migrated section to improve the illumination of the subsurface features. The efficiency of the workflow is tested against Sigsbee2a model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".