Macroscopic framework for viscoelasticity, poroelasticity, and wave-induced fluid flows — Part 2: Effective media
Bibliographic record
Abstract
ABSTRACT Sedimentary rocks possess complex microstructures and require simplified descriptions in terms of averaged, or effective mechanical properties. Most conventional approaches to effective media use the concept of viscoelastic moduli to describe the frequency-dependent wave velocities and attenuation. However, for rock containing pore fluids, a single pair of bulk and shear moduli does not account for slow P- and S-waves and for reflections and conversions in heterogeneous media. To overcome these limitations, we use the general linear solid (GLS) theoretical framework to derive multiphase models of effective media. Two types of models are considered. First, for sandstone containing thin layers saturated with brine and gas, two-phase effective-medium relations are derived in a (relatively) closed form for the density and elasticity, and the parameters of internal friction are inferred by fitting the dispersion spectra of both fast and slow P-waves. In the second application, we consider the generalized standard linear solid (GSLS) medium, which is broadly used in numerical simulations of seismic wavefields. The GLS point of view suggests that (petro)physical significance should always be sought for the mathematical variables usually assumed in GSLS models. Inertial effects and interactions between internal variables cause additional wave modes in a GSLS medium. Contrary to what is often thought, with inertial effects and fuller interactions between the internal variables, near-zero or negative velocity dispersion can occur in a medium with band-limited attenuation.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".