Inversion for non-Biot's internal material properties of sandstone using a macroscopic Lagrangian model
Bibliographic record
Abstract
Recently, by adding the consideration of internal local deformation in the heterogeneous media to the Lagrangian continuum-mechanics model of porous media, Deng and Morozov (2019) proposed a rigorous Biot-consistent detailed model to describe the deformation of the medium and the internal mechanical friction due to fluid or solid viscosity. Based on this mechanics model, and utilizing the Fontainebleau sandstone measurements in the subresonant attenuation experiments by Pimienta et al. (2015), we invert for the macroscopic material properties of the rock sample saturated with brine. The inversion is performed in three steps: (1) Correction for experimental conditions such as the size and shape of the rock sample, resulting in an effective Biot's model for bulk and shear moduli for the material; (2) Deriving a sufficiently accurate initial model for the material properties from the corrected data; and (3) Using the least squares fitting method, inversion of the frequency-dependent Biot's model (data) for true, frequency-independent properties of the rock. The new material properties we invert for are denoted PJJ, Q2J and PJJ', where J =1 to 5 denote five modes of internal deformations within rock. The property PJJ represents the elastic (free) energy of the internal deformations, Q2J describes their elastic couplings with pore flows, and PJJ' denotes the viscosity counterpart of PJJ. The results of fitting the effective drained results are shown in Figure 1, and the resulting material properties are listed in Table 1.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".