Bibliographic record
Abstract
Well logs are often used for the estimation of seismic wavelets. The phase is obtained by forcing a well‐derived synthetic to match the seismic, thus assuming the well log provides ground truth. However, well logs are not always available and can predict different phase corrections at nearby locations. Thus, a wavelet estimation method that can reliably predict phase from the seismic alone is required. We test three statistical wavelet estimation techniques against the deterministic method of seismic‐to‐well ties. We explore how the choice of method influences the estimated wavelet phase, with the aim of finding a statistical method which consistently predicts a phase in agreement with well logs. The statistical method of kurtosis maximization by constant phase rotation was consistently able to extract a phase in agreement with seismic‐to‐well ties. Furthermore, the second method based on a modified mutual‐information‐rate criterion provided frequency‐dependent phase wavelets where the deterministic method could not. Time‐varying wavelets were also estimated — a challenge for deterministic approaches due to the short logging sequence. We conclude that statistical techniques can be employed as quality control tools for the deterministic methods, a way of interpolating phase between wells, or act as standalone tools in the absence of wells.
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 | 0.025 | 0.195 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".