Fluid pressure diffusion effects on the seismic reflectivity of a single fracture
Bibliographic record
Abstract
When seismic waves travel through a fluid-saturated porous medium containing a fracture, fluid pressure gradients are induced between the compliant fracture and the stiffer embedding background. The resulting equilibration through fluid pressure diffusion (FPD) produces a frequency dependence of the stiffening effect of the fluid saturating the fracture. As the reflectivity of a fracture is mainly controlled by the stiffness contrast with respect to the background, these frequency-dependent effects are expected to affect the fracture reflectivity. The present work explores the P- and S-wave reflectivity of a fracture modeled as a thin porous layer separating two half-spaces. Assuming planar wave propagation and P-wave incidence, this article analyzes the FPD effects on the reflection coefficients through comparisons with a low-frequency approximation of the underlying poroelastic model and an elastic model based on Gassmann's equations. The results indicate that, while the impact of global flow on fracture reflectivity is rather small, FPD effects can be significant, especially for P-waves and low incidence angles. These effects get particularly strong for very thin and compliant, liquid-saturated fractures and embedded in a high-permeability background. In particular, this study suggests that in common environments and typical seismic experiments FPD effects can significantly increase the seismic visibility of fractures.
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.
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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".