Multidimensional reconstruction of noise correlation functions and its application in improving surface-wave inversion
Why this work is in the frame
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Bibliographic record
Abstract
SUMMARY The cross-correlation of the ambient noise recordings, also known as noise correlation functions (NCFs), can converge to Green’s functions (GFs) which describe wave propagation between a pair of stations. However, the NCFs are often biased from the true GFs due to the presence of random noise and spurious arrivals arising from non-diffuse wavefields. Additionally, the limited spatial and temporal coverage of recording stations can lead to large data gaps in the retrieved virtual shot gathers, particularly at large interstation distances (far offsets). Both these factors impose great challenges to retrieving high-quality NCFs and conducting reliable subsurface imaging. In this study, we propose a multidimensional (4-D) reconstruction method to compensate for the insufficient station coverage and simultaneously attenuate incoherent noise in the NCFs. We test the feasibility of the proposed method using a dense seismic array deployed in western Canada. Our results demonstrate that the reconstructed virtual common midpoint gather can greatly improve the stability and reliability of the surface-wave dispersion measurements and subsequent shear velocity inversions compared to the conventional approaches. The proposed ambient noise processing framework enables us to construct accurate 3-D velocity model of the subsurface.
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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 | 0.000 | 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 it