A Preliminary Green Function Database for Global 3-D Centroid Moment Tensor Inversions
Bibliographic record
Abstract
Currently, the accuracy of synthetic seismograms used for Global CMT inversion, which are based on modern 3D Earth models, is limited by the validity of the path-average approximation for mode summation and surface-wave ray theory. Inaccurate computation of the ground motion’s amplitude and polarization as well as other effects that are not modeled may bias inverted earthquake parameters. Synthetic seismograms of higher accuracy will improve the determination of seismic sources in the CMT analysis, and remove concerns about this source of uncertainty. Strain tensors, and databases thereof, have recently been implemented for the spectral-element solver SPECFEM3D (Ding et al., 2020) based on the theory of previous work (Zhao et al., 2006) for regional inversion of seismograms for earthquake parameters. The main barriers to a global database of Green functions have been storage, I/O, and computation. But, compression tricks and smart selection of spectral elements, fast I/O data formats for high-performance computing, such as ADIOS, and wave-equation solution on GPUs, have dramatically decreased the cost of storage, I/O, and computation, respectively. Additionally, the global spectral-element grid matches the accuracy of a full forward calculation by virtue of Lagrange interpolation. Here, we present our first preliminary database of stored Green functions for 17 seismic stations of the global seismic networks to be used in future 3-D centroid moment tensor inversions. We demonstrate the fast retrieval and computation of seismograms from the database.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.013 |
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".