Prediction of Shear Wave Velocity Using Machine Learning Models and Characteristics of the Porous Medium
Bibliographic record
Abstract
Summary Shear wave (S-wave) velocity is an important parameter of rocks, as it is used for the evaluation of the morphology and saturation of the porous medium. Estimation of missing values of S-wave has been a focal point of interest in the energy sector for a long time. For this study, we collected a comprehensive set of data (about 1,200 data points) from different rock types and different machine learning (ML) models to find the best fit for the prediction of the S-wave. The ML models used were linear regression (LR), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP). Our results confirmed that considering the conceptual effect of porosity as well as compressional wave (P-wave) within the model gives a very good prediction of the S-wave with R2 = 0.996. The proposed model is independent of medium saturation, confining stress, and fluid type and depends on the type of the porous medium (e.g., rock type). The linear form of the ML model is quite simple and can be easily used, while the MLP model gives less error in estimation.
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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".