Tutorial on rotational seismology and its applications in exploration geophysics
Bibliographic record
Abstract
ABSTRACT Traditionally, seismological interpretations are based on the measurement of only translational motions, such as particle displacement, velocity, and/or acceleration, possibly combined with pressure changes; yet theory indicates that rotational motions should also be observed for a complete description of all ground motions. The recent and ongoing development of rotational sensors renders a full analysis of the translational and rotational ground motion possible. We have developed the basic mathematical theory related to rotational motion. And we also evaluated several instruments used to directly measure the rotational ground motion, which may be applicable for exploration geophysics. Finally, we made several applications of rotational motion in exploration geophysics, namely, (1) P- and S-wavefield separation, (2) wavefield reconstruction, (3) ground-roll removal, (4) microseismic event localization and reflection seismic migration by wavefield extrapolation, and (5) moment tensor inversion. The cited research shows that in particular, the information on the spatial gradient of the wavefield obtained by rotational sensors is beneficial for many purposes. This tutorial is meant to (1) enhance familiarity with the concept of rotational seismology, (2) lead to additional applications, and (3) fast track the continued development of rotational sensors for global and exploration geophysical use.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.023 |
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".