The influence of nonuniform ambient noise on crustal tomography in Europe
Bibliographic record
Abstract
Abstract Ambient‐noise seismology is of great relevance to high‐resolution crustal imaging, thanks to the unprecedented dense data coverage it affords in regions of little seismicity. Under the assumption of uniformly distributed noise sources, it has been used to extract the Green's function between two receivers. We determine the imprint of this assumption by means of wave propagation and adjoint methods in realistic 3‐D Earth models. In this context, we quantify the sensitivity of ambient‐noise cross‐correlations from central Europe with respect to noise‐source locations and shear wave‐speed structure. We use ambient noise recorded over 1 year at 196 stations, resulting in a database of 864 cross‐correlations. Our mesh is built upon a combined crustal and 3‐D tomographic model. We simulate synthetic ambient‐noise cross‐correlations in different frequency bands using a 3‐D spectral‐element method. Traveltime cross‐correlation measurements in these different frequency bands define the misfit between synthetics and observations as a basis to compute sensitivity kernels using the adjoint method. We perform a comprehensive analysis varying geographic station and noise‐source distributions around the European seas. The deterministic sensitivity analysis allows for estimating where the starting crustal model shows better accordance with our data set, and gain insight into the distribution of noise sources in the European region. This highlights the potential importance of considering localized noise distributions for tomographic imaging, and forms the basis of a tomographic inversion in which the distribution of noise sources may be treated as a free parameter similar to earthquake tomography.
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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 | 0.000 | 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 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".