Did you smooth your well logs the right way for seismic interpretation?
Bibliographic record
Abstract
Correlations between physical properties and seismic reflection data are useful to determine the geological nature of seismic reflections and the lateral extent of geological strata. The difference in resolution between well logs and seismic data is a major hurdle faced by seismic interpreters when tying both data sets. In general, log data have a resolution of at least two orders of magnitude greater than seismic data. Smoothing physical property logs improves correlation at the seismic scale. Three different approaches were used and compared to smooth a density log: binomial filtering, seismic wavelet filtering and discrete wavelet transform (DWT) filtering. Regression plots between the density logs and the acoustic impedance show that the data smoothed with the DWT is the only method that preserves the original relationship between the raw density data and the acoustic impedance. Smoothed logs were then used to generate synthetic seismograms that were tied to seismic data at the borehole site. Best ties were achieved using the synthetic seismogram computed with the density log processed with the DWT. The good performance of the DWT is explained by its adaptive multi-scale characteristic which preserved significant local changes of density on the high-resolution data series that were also pictured at the seismic scale. Since synthetic seismograms are generated using smoothed logs, the choice of the smoothing method impacts on the quality of seismic-to-well ties. This ultimately can have economical implications during hydrocarbon exploration or exploitation phases.
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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 | 0.007 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".