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Record W7161840911 · doi:10.82308/49808

Fault zone geometrical complexity and material contrast on earthquake rupture propagation: Observation and modeling

2021· dissertation· en· W7161840911 on OpenAlexaboutno aff
Ge Li

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsnot available
Fundersnot available
KeywordsIntraplate earthquakeInduced seismicityFault (geology)Extensional definitionSeismic hazardSeismotectonicsSeismic gapTransform fault

Abstract

fetched live from OpenAlex

Seismic hazard analysis of continental faults is a difficult task due to many challenges. Continental faults, especially intraplate faults are usually poorly mapped with elusive activity. In addition, they usually exhibit high geometrical complexities such as step-overs and bends. Finally, continental faults can also serve as material interfaces, introducing the bimaterial effect. Fault geometrical complexity and bimaterial contrast often coexist and the study on their joint influence is still insufficient. This thesis is designed to address these challenges using both seismic observations and numerical models. I first explore how to clearly identify the seismogenic potential as well as subsurface geometries of the Leech River fault zone (LRFZ), an intraplate fault system in southern Vancouver Island. My results reveal an ~ 8-10 km wide, NNE-dipping zone of seismicity representing a subsurface structure associated with the LRFZ. Based on seismicity clustering analysis, repeating events analysis and focal mechanism inversion, I find that the LRFZ can be interpreted as an extensional step-over system, which consists of two right-lateral active fault structures: the LRF structure to the north, and a secondary sub-vertical structure to the south. The latter is possibly an extension of the Southern Whidbey Island fault (SWIF).Then, I perform 3D finite element simulations to study rupture jumping scenarios from the LRF (source fault) to the SWIF (receiver fault), focusing on the influences of the offset distance (L0), fault stress ratio (S0), and fault burial depth (D). A smaller L0, a smaller S0 on either fault, or a shallower D will promote rupture jumping. I show that the final rupture jumping scenario depends on various parameters, which can be collectively represented by two keystone variables, the time-averaged Over Stressed Zone size $\overline{R_e}$ and the receiver fault initial stress level. Specifically, a smaller L0, a smaller S0 on either fault, or a shallower D will lead to a larger $\overline{R_e}$. The seismic moment on the receiver fault increases with increasing $\overline{R_e}$. When $\overline{R_e}$ reaches the threshold dependent on the receiver fault S0, the rupture becomes break-away.Finally, I conduct 2D finite-element simulations to investigate the joint influence of fault bends and the bimaterial effect on rupture processes. The bimaterial effect promotes rupture propagation in the preferred direction with higher rupture speeds and peak slip rates, and produces larger rupture sizes in partially ruptured scenarios. But it cannot promote a completely arrested rupture evolving into a partially or completely ruptured one. This is attributed to the fact that the bimaterial effect is severely suppressed when the bending angle is large. The rupture is completely arrested when $\Delta G$ (the difference between the static energy release rate and the fracture energy on the kinked segment) is negative. A larger $\Delta G$ produces a larger rupture. Compared to the influence of fault bending, the bimaterial effect can only increase $\Delta G$ by a negligible amount. Therefore, the fault bending geometry is the dominant factor controlling earthquake rupture propagation, while the material contrast is a secondary factor

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.051
GPT teacher head0.252
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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