Bibliographic record
Abstract
Abstract We present a way to image through complex overburden. The method uses surface shots with downhole receivers placed below the most complex part of the troublesome overburden. No knowledge of the velocity model between shots and receivers is required. The method uses time-reversal logic to create a new downward-continued data set with virtual sources (VS's) at the geophone locations. Time reversal focuses energy that passes through the overburden into useful primary energy for the VS. In contrast to physical acoustics, our time reversal is done on a computer, utilizing conventional acquisition with surface shots and downhole geophones. With this approach, we can image below extremely complex (realistic) overburden — in fact, the more complex the better. We recast the data to those with sources where we actually know and can control the waveform that has a downward-radiation pattern that may also be controlled, and is reproducible for 4D even if the near-surface changes or the shooting geometry is altered slightly. To illustrate the method, we apply the VS technique to a synthetic, elastic example with extreme heterogeneity, where conventional approaches fail to image the data. A 4D field-data example shows that the VS method (VSM) enables sensitive reservoir monitoring below a complex, time-variant near surface that is not achievable with surface 4D seismic or conventional 4D vertical seismic profiling (VSP).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".