Macroscopic framework for viscoelasticity, poroelasticity, and wave-induced fluid flows — Part 1: General linear solid
Bibliographic record
Abstract
ABSTRACT Field and laboratory observations of seismic wave propagation and attenuation are usually explained using the viscoelastic (VE) model and effective moduli. However, in sedimentary rocks, wave velocities and attenuation rates are dominated by pore-fluid effects, such as poroelasticity, squirt, and mesoscopic wave-induced fluid flows. Physically, such effects are significantly different from viscoelasticity, and the pore-fluid and VE phenomena are difficult to compare quantitatively without a common theoretical framework. We develop such a unified macroscopic framework that we call the general linear solid (GLS). The GLS is based on Lagrangian continuum mechanics, and it can be summarized as multiphase poroelasticity extended by solid and fluid viscosities. The formulation is carried out strictly in terms of continuum mechanics, measurable physical properties, and boundary conditions, from which the observable wave velocities and attenuation are predicted. Explicit differential equations are derived in matrix form, from which a variety of numerical modeling schemes can be obtained. A rigorous correspondence principle is formulated, in which viscosity effects contribute to complex-valued VE moduli, and Darcy friction lead to a complex-valued density matrix. Within the GLS framework, the viscoelasticity represents an end member characterized by zero Darcy-type friction, whereas the poroelasticity is an end member with zero solid viscosity. Transitions between these end members and their extensions yield macroscopic models of viscoporoelasticity, poroelasticity with multiple saturating fluids and double porosity, and poroelasticity with squirt flows. The approach is illustrated on models of layered poroelastic and viscoporoelastic media. Applications of the GLS framework are continued in part 2 of this study.
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 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".