Joint inversion of receiver function and surface wave dispersion based on the unscented Kalman inversion
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".