Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Knowledge of Q is desirable for improving seismic resolution through inverse Q filtering, facilitating amplitude analysis and seismic interpretation. However, the question of reliable Q estimation remains, especially in case of unfavorable signal-to-noise ratio (SNR). In addition, estimating Q from VSP data or even reflection data in presence of moderate noise with sufficient accuracy is still very challenging. To address this problem, a match-filter method for Q estimation is proposed and evaluated using synthetic 1D, 2D data and field data in this paper. Given two narrow time windows as might be used in the spectral-ratio method, we compute minimum phase equivalent wavelets for each window and then, by direct search over a broad Q range, we find the optimal forward Q filter that best matches the shallow wavelet to the deeper one. Testing results show that the proposed method is, compared to the spectral-ratio method, more robust to noise and more suitable for the Q estimation from reflection data, and has the potential to indentify a localized low Q zone of the subsurface, which can be used as a gas indicator.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it