Linear Array Ambient Noise Adjoint Tomography Reveals Intense Crust‐Mantle Interactions in North China Craton
Bibliographic record
Abstract
Abstract We present a 2‐D ambient noise adjoint tomography technique for a linear array with a significant reduction in computational cost and show its application to an array in North China. We first convert the observed data for 3‐D media, i.e., surface‐wave empirical Green's functions (EGFs) to the reconstructed EGFs (REGFs) for 2‐D media using a 3‐D/2‐D transformation scheme. Different from the conventional steps of measuring phase dispersion, this technology refines 2‐D shear wave speeds along the profile directly from REGFs. With an initial model based on traditional ambient noise tomography, adjoint tomography updates the model by minimizing the frequency‐dependent Rayleigh wave traveltime delays between the REGFs and synthetic Green functions calculated by the spectral‐element method. The multitaper traveltime difference measurement is applied in four‐period bands: 20–35 s, 15–30 s, 10–20 s, and 6–15 s. The recovered model shows detailed crustal structures including pronounced low‐velocity anomalies in the lower crust and a gradual crust‐mantle transition zone beneath the northern Trans‐North China Orogen, which suggest the possible intense thermo‐chemical interactions between mantle‐derived upwelling melts and the lower crust, probably associated with the magmatic underplating during the Mesozoic to Cenozoic evolution of this region. To our knowledge, it is the first time that ambient noise adjoint tomography is implemented for a 2‐D medium. Compared with the intensive computational cost and storage requirement of 3‐D adjoint tomography, this method offers a computationally efficient and inexpensive alternative to imaging fine‐scale crustal structures beneath linear arrays.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".