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A unified numerical model for the simulation of the seismic cycle for normal and reverse fault earthquakes in Italy

2020· article· en· W3094253074 on OpenAlexaff
Matteo Albano, S. Barba, Christian Bignami, Carlo Doglioni, Eugenio Carminati, Michele Saroli, Marco Moro, Salvatore Stramondo, Sergey Samsonov

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsGeologySeismologyCrustSlip (aerodynamics)GeodesyGeophysics

Abstract

fetched live from OpenAlex

Earthquakes are the result of the strain accumulation in the earth's crust over a variable decade to millennial period, i.e., the interseismic stage, followed by a sudden stress release at a crustal discontinuity, i.e., the coseismic stage, finally evolving in a postseismic stage. Commonly, the seismic cycle is modelled with analytical and numerical approaches. Analytical methods simulate the interseismic, coseismic and postseismic phases independently. These models impose the slip of single or multiple planar sources to infer fault geometry, slip distribution and regional deformations to fit the available geodetic or seismological measurements, often regardless of the magnitude and orientation of the interseismic gravitational and tectonic forces. Numerical approaches allow simulating complex geometries in heterogeneous media and at different modelling scales, assuming various constitutive laws. Such models often impose the slip on the fault plane to simulate the observed coseismic dislocation or the propagation of the seismic waves, or they adopt ad-hoc boundary conditions to investigate the interseismic stress accumulation or the postseismic relaxation for specific cases. We contribute to the understanding of the seismic cycle associated to a single fault by developing a numerical model to simulate the long-term crustal interseismic deformation, the coseismic brittle episodic dislocation, and the postseismic relaxation of the upper crust within a unified environment for both normal and reverse fault earthquakes in Italy, including the forces acting during the interseismic period, i.e., the lithostatic load and the horizontal stress field, the latter simulated with the application of a shear traction a the model’s base. We adjusted the setup of our model to simulate the interseismic, coseismic and postseismic phases for two seismic events: the Mw 6.1 L’Aquila 2009 normal fault earthquake and the Mw 5.9 Emilia-Romagna 2012 reverse fault earthquake. The simulation results show that the applied basal shear traction is fundamental to model the large-scale interseismic pattern since it allows for a first-order simulation of the ongoing crustal interseismic extension of the Central Apennines and compression of the Adriatic foreland and the north-eastern part of the Italian territory. The action of shear tractions and lithostatic forces generates a local concentration of stresses and strains in the presence of local heterogeneities or discontinuities, i.e., at the transition between the brittle locked fault and the ductile unlocked slipping fault during the interseismic stage. Such an interseismic strain partitioning provides maximum horizontal stress sufficient to exceed the friction on the locked brittle part of the fault, with the subsequent collapse of the hangingwall in case of extensional earthquakes or the expulsion of the hangingwall in case of compressional earthquakes. The instantaneous slip of the hangingwall perturbs the crustal pore fluid pressures, triggering groundwater flow in the postseismic phase from regions of higher pore pressures, which further compress, to regions of lower pore pressures, which further dilate. As a result, displacements gradually accumulate in the postseismic phase, according to the dissipation of pore pressure excess. Once the postseismic phase terminates, a new cycle of interseismic loading can start again.

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: none
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.034
GPT teacher head0.240
Teacher spread0.206 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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