Formulation of the linearized forward problems for multicomponent OBS data in a water/solid configuration using the reciprocity theorem
Bibliographic record
Abstract
As is the case with other geophysical data, the interpretation of multicomponent data from the new ocean bottom seismic (OBS) acquisition system requires a solution to the forward problem of predicting seismograms.Born approximation (a single-scattering approximation) is one of the classical tools used for solving forward problems.The adaptation of this solution to OBS data requires (1) the explicit introduction of the boundary conditions at a water/solid interface, (2) the use of the reciprocity theorem in a £uid/solid con¢guration and (3) the use of P-and S-wave potentials.We present a formulation of the linearized forward problem for predicting multicomponent OBS data using Born approximation and the reciprocity theorem.The results of our formulation are valid for pressure data (hydrophone data) as well as particle velocity data (geophone data).Speci¢c cases where particle velocity data are cast into P-and S-wave potentials are also treated.For these scenarios, the background medium and the scatterer are considered arbitrarily anisotropic and heterogeneous.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".