Bibliographic record
Abstract
SUMMARY We introduce a new relative moment tensor (MT) inversion method for clusters of nearby earthquakes. The method extends previous work by introducing constraints from S-waves that do not require modal decomposition and by employing principal component analysis to produce robust estimates of excitation. At each receiver, P and S waves from each event are independently aligned and decomposed into principal components. P-wave constraints on MTs are obtained from a ratio of coefficients corresponding to the first principal component, equivalent to a relative amplitude. For S waves we produce constraints on MTs involving three events, where one event is described as a linear combination of the other two, and coefficients are derived from the first two principal components. Nonlinear optimization is applied to efficiently find best-fitting tensile-earthquake and double-couple solutions for relative MT systems. Using synthetic data, we demonstrate the effectiveness of the P and S constraints both individually and in combination. We then apply the relative MT inversion to a set of 16 earthquakes from southern Alaska, at ∼125 km depth within the subducted Yakutat terrane. Most events are compatible with a stress tensor dominated by downdip tension, however, we observe several pairs of earthquakes with nearly antiparallel slip implying that the stress regime is heterogeneous and/or faults are extremely weak. The location of these events near the abrupt downdip termination of seismicity and the low-velocity zone suggest that they are caused by weakening via grain-size and volume reduction associated with eclogitization of the lower crustal gabbro layer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".