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Record W2158300980 · doi:10.5194/npg-10-573-2003

Characteristic scales of earthquake rupture from numerical models

2003· article· en· W2158300980 on OpenAlex
Moritz Heimpel

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNonlinear processes in geophysics · 2003
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAsperity (geotechnical engineering)Quake (natural phenomenon)GeologySlip (aerodynamics)PlanarEarthquake ruptureStatistical physicsMechanicsFault (geology)SeismologyPhysicsGeotechnical engineeringComputer science

Abstract

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Abstract. Numerical models of earthquake rupture are used to investigate characteristic length scales and size distributions of repeated earthquakes on vertical, planar fault segments. The models are based on exact solutions of static three-dimensional (3-D) elasticity. Dynamical rupture is approximated by allowing the static stress field to expand from slip motions at a single velocity. To show how the vertical fault width affects earthquake size distributions for a broad range of fault behaviors, two different fault strength models are used; a smooth model and a heterogeneous asperity model. The smooth model is a simplified version of the Dieterich-Ruina rate and state dependent friction law. The heterogeneous asperity model uses a slip-dependent random powerlaw strength distribution. It is shown that the characteristic scale of fault segmentation is proportional to the vertical width of a seismogenic fault. This conclusion holds for both the smooth and the heterogeneous models. For the smooth models characteristic quake distributions result, with populations of large events that are obviously distinct from smaller events. The distributions of large events have well-defined mean lengths and moments. The heterogeneous models result in Gutenberg-Richter (GR) powerlaw distributions of event sizes up to a characteristic quake size. Quakes larger than the characteristic size fall off the GR distribution such that the powerlaw would greatly overestimate the probability of occurrence of the larger events.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.631

Codex and Gemma teacher scores by category

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

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 head0.017
GPT teacher head0.224
Teacher spread0.207 · 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