Recent progress on full waveform inversion
Bibliographic record
Abstract
Full waveform inversion is an acoustic/elastic/anelastic wave equation-based high accuracy seismic imaging method for studying the Earth's interior structure. To date, it has been widely used in exploration seismology, studies on crustal and mantle structures at both regional and global scales. With this approach, we are able to build a unified theory and algorithm platform to constrain multi-parameter seismic models for the Earth's interior, including P and S wave velocities, anisotropy, attenuation, density and reflectivity, etc. By jointly interpreting these seismic parameters, we hope to better constrain variations in temperature and composition, mantle convection and distribution of water and volatiles. Recent developments include selection of optimal misfit functions, multi-parameter inversion, model regularization, resolution and uncertainty quantification, as well as its applications to special types of datasets, such as ambient-noise recordings and teleseismic scattered waves recorded by dense linear arrays. Furthermore, in order to better interpret inverted multi-parameters and investigate related problems in Earth sciences, we need collaboration among different disciplines, such as synthesizing results from seismology, mineral physics and geodynamic modeling. These results enable us to better understand reservoirs, basin structures, fault distribution and mantle convection.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".