Joint inversion of receiver function and surface wave dispersion based on the unscented Kalman inversion
Why this work is in the frame
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Bibliographic record
Abstract
SUMMARY Joint inversion, such as the combination of receiver function and surface wave dispersion, can significantly improve subsurface imaging by exploiting their complementary sensitivities. Bayesian methods have been demonstrated to be effective in this field. However, there are practical challenges associated with this approach. Notably, most Bayesian methods, such as the Markov Chain Monte Carlo method, are computationally intensive. Additionally, accurately determining the data noise across different data sets to ensure effective inversion is often a complex task. This study explores the unscented Kalman inversion (UKI) as a potential alternative. Through a data-driven approach to adjust estimated noise levels, we can achieve a balance between actual noise and the weights assigned to different data sets, enhancing the effectiveness of the inversion process. Synthetic tests of joint inversion of receiver function and surface wave dispersions indicate that the UKI can provide robust solutions across a range of data noise levels. Furthermore, we apply the UKI to real data from seismic arrays in Pamir and evaluate the accuracy of the joint inversion through posterior Gaussian distribution. Our results demonstrate that the UKI presents a promising supplement to conventional Bayesian methods in the joint inversion of geophysical data sets with superior computational efficiency.
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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