Conversations between earthquakes: Dynamics and delays of the 2019 Ridgecrest rupture sequence
Bibliographic record
Abstract
Abstract The overwhelming observational difficulties and the complexity of earthquake physics have rendered seismic hazard assessment largely empirical. Despite increasingly high-quality geodetic, seismic, and field observations, data-driven earthquake imaging yields stark differences and physics-based models explaining all observed dynamic complexities are elusive. Here we present data-assimilated 3D dynamic rupture models which untwine California's biggest earthquakes in more than 20 years: the moment magnitude (Mw) 6.4 Searles Valley and Mw7.1 Ridgecrest, California, sequence breaking multiple segments of the same fault system. Our models use supercomputing to find the link between the two large earthquakes. We unify the uniquely high-quality strong-motion and teleseismic, field mapping, high-rate GNSS, and space geodetic foreshock and mainshock datasets with earthquake physics. We find that the regional structure, the ambient long- and short-term stress, as well as the dynamic and static fault system interactions, are conjointly crucial to understand the dynamics and delays of the sequence. Dynamic rupture of a statically strong yet dynamically weak fault system is driven by overpressurized fluids and low dynamic friction in our models. The observed earthquake complexity results from static and dynamic stress changes acting across a non-vertical quasi-orthogonal conjugate fault structure. We demonstrate that joint physics-based and data-driven illumination of the mechanics of complex fault systems and earthquake sequences is possible when reconciling dense earthquake recordings, 3D regional structure and stress models. We foresee that physics-based interpretation of big observational data-sets characterizing complex nonlinear systems will have a transformative impact on future geohazard mitigation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".