Subsurface exploration using MASW technique—an inversion combining predominant mode with genetic algorithm
Bibliographic record
Abstract
The genetic algorithm (GA) has been employed to perform the inversion considering the predominant modal curve in the dispersion image using the multichannel analysis of surface wave (MASW) technique. Using the stiffness matrix method (SMM) for a given layered medium, the predominant modal curve was generated by imposing the criterion of the maximum vertical displacement at the free surface. The current study involves the MASW-based (i) synthetic experiments on four different geologic profiles using the finite elements (FEs) analysis, and (ii) in situ tests on five different sites. From the FE-based MASW dispersion images, it is revealed that the modal plot in the dispersion image matches closely with the corresponding theoretically generated predominant modal curve which is obtained using the SMM. For the experimental data, the inverted geological profiles, derived from the observed predominant dispersion plot and with the usage of the SMM, were subsequently used for performing the wave propagation simulation using the FE analysis. The simulated dispersion images using the FE analysis showed a great match with the corresponding observed experimental images. It is found that the current predominant mode-based GA inversion approach is more accurate as compared to the fundamental/multiple modes based inversion method.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".