Constraints on Deep Moonquake Focal Mechanisms Through Analyses of Tidal Stress
Bibliographic record
Abstract
[1] A relationship between deep moonquake occurrence and tidal forcing is suggested by the monthly periodicities observed in the occurrence times of events recorded by the Apollo Passive Seismic Experiment. In addition, the typically large S wave to P wave arrival amplitude ratios observed on deep moonquake seismograms are indicative of shear failure. Tidal stress, induced in the lunar interior by the gravitational influence of the Earth, may influence moonquake activity. We investigate the relationship between tidal stress and deep moonquake occurrence by searching for a linear combination of the normal and shear components of tidal stress that best approximates a constant value when evaluated at the times of moonquakes from 39 different moonquake clusters. We perform a grid search at each cluster location, computing the stresses resolved onto a suite of possible failure planes, to obtain the best fitting fault orientation at each location. We find that while linear combinations of stresses (and in some cases stress rates) can fit moonquake occurrence at many clusters quite well; for other clusters, the fit is not strongly dependent on plane orientation. This suggests that deep moonquakes may occur in response to factors other than, or in addition to, tidal stress. Several of our inferences support the hypothesis that deep moonquakes might be related to transformational faulting, in which shear failure is induced by mineral phase changes at depth. The occurrence of this process would have important implications for the lunar interior.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".