3D structure beneath Iranian plateau and Zagros using adjoint tomography
Bibliographic record
Abstract
We perform an adjoint waveform tomography using a combined data set consisting of regional earthquake waveforms and Rayleigh wave ambient-noise Green's function to construct a new 3-D wave velocity model of the crust and uppermost mantle beneath the Iranian plateau. The earthquake waveforms come from 250 regional events with magnitudes of 4.5-6.5 recorded by 136 broadband seismic stations. The EGFs are derived from cross correlations of more than three years of continuous seismic noise. The inversion starts with an initial model derived from the global Crust1 model locally modified using information from previous studies. Adjoint tomography refines the initial model by iteratively minimizing the frequency-dependent travel-time misfits between real and synthetic earthquake data and EGFs and synthetic Green’s functions measured in different period bands. Our new model covers the known tectonic units such as the Central Iranian Block, Zagros fold-and-thrust belt, Sanandaj-Sirjan metamorphic zone and Urumieh-Dokhtar magmatic arc. Overall, the adjoint tomography provides images with more real resolutions and amplitudes due to the finite-frequency consideration. Using the numerical spectral-element solver in adjoint tomography provides accurate structural sensitivity kernels, which help generate more robust images rather than those generated by ray-theory tomography. Our study also demonstrates improvement of lateral resolution and depth sensitivity using combined data set instead of only earthquake data.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".