Analyzing three-component 3D vertical seismic profiling data
Bibliographic record
Abstract
Abstract A three-component 3D vertical seismic profile (VSP) was acquired over the Blackfoot oil field in Alberta, Canada. The VSP survey was recorded simultaneously with a surface seismic program. The objectives of the VSP were to develop recording logistics, data handling, and processing procedures and to determine if the 3D VSP volumes could image the glauconitic sand reservoir of the Blackfoot field. Dynamite shots from the surface seismic survey, which fell within a 2200-m offset from the recording well, were used in the VSP analysis. The shots were recorded by a string of three-component borehole receivers that was moved seven times, resulting in a receiver depth range of 400 to 910 m. The borehole data were processed using basic VSP processing techniques that included hodogram analysis, wavefield separation using median filters, and VSP deconvolution. The final P-P and P-S image volumes were obtained by VSP common-depth point and VSP common-conversion point stacking the upgoing wavefields followed by f-xy deconvolution. The P-P and P-S images from the VSP correlate well with those from the surface seismic survey. Time slices from the VSP also indicate the trend of the sand channel of the Blackfoot field.
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 | 0.002 | 0.002 |
| 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) | 0.002 | 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".