A review of inverse methods in seismic site characterization
Bibliographic record
Abstract
Abstract Seismic site characterization attempts to quantify seismic wave behavior at a specific location based on near-surface geophysical properties, for the purpose of mitigating damage caused by earthquakes. In recent years, techniques for estimating near-surface properties for site characterization using geophysical observations recorded at the surface have become an increasingly popular alternative to invasive methods. These observations include surface-wave phenomenology such as dispersion (velocity-frequency relationship) as well as, more recently, full seismic waveforms. Models of near-surface geophysical properties are estimated from these data via inversion, such that they reproduce the observed seismic observations. A wide range of inverse problems have been considered in site characterization, applying a variety of mathematical techniques for estimating the inverse solution. These problems vary with respect to seismic data type, algorithmic complexity, computational expense, physical dimension, and the ability to quantitatively estimate the uncertainty in the inverse solution. This paper presents a review of the common inversion strategies applied in seismic site characterization studies, with a focus on associated advantages/disadvantages as well as recent advancements.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".