Correlation between seismic wave velocity, rock porosity and maximum principal stress based on the laboratory test data
Bibliographic record
Abstract
In order to determine the internal relationships among seismic wave velocity, axial pressure, and rock porosity, the rock samples taken from NRS170143 borehole of the Nickel Rim South mine are tested using a Hoek type triaxial cell equipped with axial linear variable differential transducers (LVDTs) and a data acquisition module. The empirical expression between seismic wave velocity and rock pressure is fitted based on the laboratory test data of rock samples. Then, P-V model P-φ model and φ-ε model are created to analyze the laboratory test data. The results show that: (1) the relationship between axial pressure and rock porosity can be represented by a new empirical equation φ=a*e-b*P-c. With an increase of axial pressure, the value of rock porosity gradually decreases below the straight line φ=1% and close to 0. The P-φ model can be a good judge if the pressure has reached the maximum compression pressure in the process of rock compression experiment; (2) The P wave velocity and S wave velocity exponentially increase with increasing axial pressure; Rock porosity and crack density parameter exponentially decrease with increasing axial pressure; (3) there is a linear positive correlation between the ratio of rock porosity to crack density parameter and the ratio of crack thickness to crack length. (4) the relationship between Vp and Vs in each compression test can be fitted to the linear equation Vp=a*Vs+b; for all different samples of NR170143, the ratios (M) of Vp to Vs ranges from 1.35 to 1.85. In summary, the P-V model, P-φ model, φ-ε model and Vp-Vs-φ model can intuitively reflect the relationship among seismic wave velocity, axial pressure and rock porosity.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".