Estimating P- and S-wave inverse quality factors from observed seismic data using an attenuative elastic impedance
Bibliographic record
Abstract
P- and S-wave inverse quality factors quantify seismic wave attenuation, which is related to several key reservoir parameters (porosity, saturation, and viscosity). Estimating the inverse quality factors from observed seismic data provides additional and useful information during gas-bearing reservoir prediction. First, we have developed an approximate reflection coefficient and attenuative elastic impedance (QEI) in terms of the inverse quality factors, and then we established an approach to estimate elastic properties (P- and S-wave impedances, and density) and attenuation (P- and S-wave inverse quality factors) from seismic data at different incidence angles and frequencies. The approach is implemented as a two-step inversion: a model-based and damped least-squares inversion for QEI, and a Bayesian Markov chain Monte Carlo inversion for the inverse quality factors. Synthetic data tests confirm that P- and S-wave impedances and inverse quality factors are reasonably estimated in the case of moderate data error or noise. Applying the established approach to a real data set is suggestive of the robustness of the approach, and furthermore that physically meaningful inverse quality factors can be estimated from seismic data acquired over a gas-bearing reservoir.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".