Frequency domain full-waveform inversion in a fluid-saturated poroelastic medium
Bibliographic record
Abstract
SUMMARY Determining subsurface properties is of fundamental importance in exploration seismic imaging. Poroelasticity theory provides an opportunity to extract quantitative fluid- and attenuation-dependent properties from seismic data. Following Pratt’s frequency-domain full-waveform-inversion (FWI) procedure and extending the basic FWI equations from the elastic case to the poroelastic case, we implement poroelastic FWI (PFWI) of fluid-saturated porous media. By analysing the sensitivity kernels of poroelastic parameters, we explain the reason why some parameters are more difficult to recover than others. We also show the analytical and numerical radiation patterns based on which we predict the trade-offs among parameters. In numerical experiments, we invert two models to demonstrate the feasibility and effectiveness of the proposed PFWI and to verify our predictions about trade-offs for two-parameter PFWI. Finally, we discuss the various factors that influence the inversion results.
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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) | 0.003 | 0.001 |
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".