Aseismic Fault Slip During a Shallow Normal‐Faulting Seismic Swarm Constrained Using a Physically Informed Geodetic Inversion Method
Bibliographic record
Abstract
Abstract Improved imaging of the spatio‐temporal growth of fault slip is crucial for understanding the driving mechanisms of earthquakes and faulting. This is especially critical to properly evaluate the evolution of seismic swarms and earthquake precursory phenomena. Fault slip inversion is an ill‐posed problem and hence regularization is required to obtain stable and interpretable solutions. An analysis of compiled finite fault slip models shows that slip distributions can be approximated with a generic elliptical shape, particularly well for M ≤ 7.5 events. Therefore, we introduce a new physically informed regularization to constrain the spatial pattern of slip distribution. Our approach adapts a crack model derived from mechanical laboratory experiments and allows for complex slipping patterns by stacking multiple cracks. The new inversion method successfully recovered different simulated time‐dependent patterns of slip propagation, that is, crack‐like and pulse‐like ruptures, directly using wrapped satellite radar interferometry (InSAR) phase observations. We find that the new method reduces model parameter space, and favors simpler interpretable spatio‐temporal fault slip distributions. We apply the proposed method to the 2011 March–September normal‐faulting seismic swarm at Hawthorne (Nevada, USA), by computing ENVISAT and RADARSAT‐2 interferograms to estimate the spatio‐temporal evolution of fault slip distribution. The results show that (a) aseismic slip might play a significant role during the initial stage and (b) this shallow seismic swarm had slip rates consistent with those of slow earthquake processes. The proposed method will be useful in retrieving time‐dependent fault slip evolution and is expected to be widely applicable to studying fault mechanics, particularly in slow earthquakes.
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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 | 0.000 | 0.001 |
| 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) | 0.000 | 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".