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Record W4220948824 · doi:10.5194/egusphere-egu22-7507

Slip model of the 2013 April 16 Mw 7.7 Saravan intra-slab earthquake (Makran subduction zone) derived from InSAR, GPS, and Teleseismic P-wave modeling

2022· preprint· en· W4220948824 on OpenAlexaff
Andréa Walpersdorf, Meysam Amiri, Erwan Pathier, Zahra Mousavi, Fatemeh Khorrami, Sergey Samsonov

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsGeologySeismologySubductionInterferometric synthetic aperture radarGeodesySlabSlip (aerodynamics)Geodetic datumSynthetic aperture radarTectonicsGeophysicsRemote sensing

Abstract

fetched live from OpenAlex

The 2013 April 16 Mw 7.7 Saravan earthquake, an intra-slab earthquake with a normal faulting mechanism at of 50 km depth, occurred in the western part of the Makran subduction zone, where the Arabian oceanic lithosphere subducts northward under Iran and Pakistan. This event was the first instrumental recorded earthquake with a magnitude larger than Mw 6 since the last century. Studying this earthquake using geodetic and seismological data brings a unique opportunity to measure surface displacement due to the earthquake and assess causative fault parameters. Furthermore, it enables us to address some problems in the Makran subduction zone including slab dip angle, depth of dip angle change. We used interferograms generated from RADARSAT-2 Synthetic Aperture Radar (SAR) data and coseismic GPS velocity field to combine with teleseismic P-wave data to model source fault parameters. First, we apply uniform slip modeling using a Bayesian bootstrap optimization nonlinear inversion method to find causative fault parameters. We specify search grids based on the LOS displacement map and focal mechanism solutions for each fault parameter to find the best solutions. These parameters include length, width, depth, strike, dip, rake, slip, location of the fault plane, rupture nucleation point, and origin time. Based on some prior tests and seismological information of earthquake, we decreased the search area of each parameter: depth 30- 70 km, dip 40˚- 80˚, strike 200˚-250˚, length 50-120 km, width 30-50 km, rake -150˚ -80˚ slip 1-4 m and let rupture nucleation point and origin time to be wide enough implying that all possible and reasonable fault geometry and kinematics parameters can be explored. Synthetic static displacements and seismic waveforms in a layered medium were computed with the Green's functions calculated using QSSP and PSGRN/PSCMP, respectively (Wang et al., 2006; Wang et al., 2017). A Green's function store contains pre-calculated Green's functions on a grid for combinations of source depth and source-receiver surface distance. For the layered half-space medium, we used the velocity structure of the GOSH seismic station to derive the Green Functions (Sebastian et al., 2016). After 450,000 iterations, the waveform fits, subsampled surface displacements as observed, modeled, and residual maps based on the best model are resolved. The distributions and resulting confidence intervals indicate that the parameters were well constrained. The joint inversion's best result indicates that the Saravan 2013 causative fault is a North-dipping normal fault with a dip of ~ 67°. The earthquake source length and width are approximately 120 and 80 km respectively. In the second step, we model the derived fault plane in the previous step to retrieve the distributed slip model, allowing the slip to vary across the fault plane. In this step, all the parameters assumed fixed except slip. We extend fault length and width to 150 km and 100 km to prevent unwanted slip in the corners. The slip variation along the causative fault is characterized by one significant patch at the depth between 30-65 km with a maximum magnitude of about 4 m at 42-52 km.

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)0.000
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 score0.045
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.046
GPT teacher head0.213
Teacher spread0.167 · 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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Citations1
Published2022
Admission routes1
Has abstractyes

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