Fusion of geotechnical and geophysical data for 2D subsurface site characterization using multi-source Bayesian compressive sampling
Why this work is in the frame
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Bibliographic record
Abstract
Subsurface site characterization is essential for geotechnical engineering applications (e.g., slope stability analysis and deep excavation design), which is usually achieved through geotechnical site investigation and might be supplemented by geophysical survey. Geotechnical and geophysical investigations are complementary in many aspects. Geotechnical investigation provides direct measurement data with high accuracy but only at limited locations. On the other hand, geophysical survey provides abundant two-dimensional (2D) or three-dimensional measurements, but the data are often indirect. In addition, geotechnical and geophysical data are usually correlated. Therefore, fusion of geotechnical and geophysical data during site characterization is beneficial. This paper proposed a novel data fusion method, called multi-source Bayesian compressive sampling, for fusion of geotechnical and geophysical data and statistical characterization of 2D subsurface profiles. The proposed method is data-driven and non-parametric, without the need for an empirical parametric function between geotechnical and geophysical data. The proposed method was illustrated and validated using both numerical and real-life examples. The results show that the proposed method not only properly characterizes 2D subsurface profiles but also explicitly quantifies the statistical uncertainty associated with the site characterization.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it