Bibliographic record
Abstract
Abstract Waves propagating across a vertical seismic profiling (VSP) array may be distinguished by their differing arrival times and linear-moveout velocities. Current methods typically assume that the waves propagate uniformly with an unvarying wavelet shape and amplitude. These assumptions break down in the presence of irregular spatial sampling, event truncations, wavelet variations, and noise. I present a new method that allows each event to independently vary in its amplitude and arrival time as it propagates across the array. The method uses an iterative global nonlinear optimization scheme that consists of several least-squares and two eigenvalue problems at each step. Events are stripped from the data one at a time. As stronger events are predicted and removed, weaker events then become visible and can be modeled in turn. As each new event is approximately modeled, the fit for all previously removed events is then revisited and updated. Iterations continue until no remaining coherent events can be distinguished. As VSP data sets are typically not large, the expense of this method is not a significant limitation. I demonstrate with a real-data example that this iterative approach can lead to a significantly better VSP wavefield separation than that which has been available when using conventional techniques.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".