Bibliographic record
Abstract
We determine centroid-moment tensor (CMT) solutions by minimizing waveform differences between observed and simulated seismograms based on an adjoint method. Synthetic seismograms and Fréchet derivatives are calculated based on a spectral-element method. The non-linear adjoint CMT inversion algorithm requires three simulations for each iteration: one ‘forward’ simulation to obtain synthetics for the current source parameters, one ‘adjoint’ simulation which involves injecting time-reversed differences between observed and simulated seismograms as simultaneous virtual sources at each of the receivers, and an extra forward simulation to compute the step length in the conjugate-gradient direction. Whereas the vertical component of the adjoint wavefield reflects the radiation pattern near the centroid location, the components of the adjoint strain tensor capture the elements of the moment tensor. We use the method to determine adjoint CMT solutions for two representative southern California earthquakes using recent 3-D crustal model CVM-6.2. The adjoint CMT solutions are in good agreement with classical Hessian-based CMT solutions involving 3-D Green's functions. In general, adjoint CMT inversions require fewer numerical simulations than traditional Hessian-based inversions. This faster convergence holds promise for multiple moment-tensor and kinematic rupture inversions in 3-D earth models.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".