A borehole-model-derived algorithm for estimating QP logs from full-waveform sonic logs
Bibliographic record
Abstract
Abstract We develop a processing algorithm to estimate the intrinsic seismic attenuation QP−1 from P head waves of full-waveform sonic logs. The algorithm, based on an extended version of the amplitude spectral ratio (ASR) method, corrects the apparent attenuation for the effects of multiple raypaths within the borehole, geometric spreading of head waves, and formation inhomogeneity. The algorithm is derived from two ray models. The first model simulates the interaction among rays reflected within the borehole and rays reflected from layer interfaces. This model removes these reflections and extracts the leading borehole wavelet. The second model uses a single-ray model for the borehole head wave in layered formations. This model provides the transmission coefficients across the layer interfaces between the source depth and the receiver depth with sectional geometric spreading within these layers. We use these two models to simultaneously separate, correct for, and normalize the effects of the borehole, geometric spreading, and layering. Then we test the accuracy and limits of the models using the finite-difference solution of a point source in a fluid-filled borehole surrounded by uniform and layered formations. In 255 tested cases, the absolute difference between the estimated and exact divided by the exact QP−1 is within 3% for more than half of the cases and 5% for more than two-thirds of the cases. The lower accuracy in the remaining cases is associated mainly with certain receiver locations with regard to the layer interfaces and is caused by the limitations of the ray models.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".