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Record W2136089348 · doi:10.1002/2014gl061644

A 3‐D spectral‐element and frequency‐wave number hybrid method for high‐resolution seismic array imaging

2014· article· en· W2136089348 on OpenAlexaff
Ping Tong, Dimitri Komatitsch, T. Tseng, S. Hung, Chin‐Wu Chen, P. Basini, Qinya Liu

Bibliographic record

VenueGeophysical Research Letters · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWaveformGeologySeismologySpectral element methodSubductionScatteringReflection (computer programming)Seismic waveResolution (logic)Plane waveAcousticsOpticsFinite element methodComputer sciencePhysicsTelecommunications

Abstract

fetched live from OpenAlex

Abstract We present a three‐dimensional (3‐D) hybrid method that interfaces the spectral‐element method (SEM) with the frequency‐wave number (FK) technique to model the propagation of teleseismic plane waves beneath seismic arrays. The accuracy of the resulting 3‐D SEM‐FK hybrid method is benchmarked against semianalytical FK solutions for 1‐D models. The accuracy of 2.5‐D modeling based on 2‐D SEM‐FK hybrid method is also investigated through comparisons to this 3‐D hybrid method. Synthetic examples for structural models of the Alaska subduction zone and the central Tibet crust show that this method is capable of accurately capturing interactions between incident plane waves and local heterogeneities. This hybrid method presents an essential tool for the receiver function and scattering imaging community to verify and further improve their techniques. These numerical examples also show the promising future of the 3‐D SEM‐FK hybrid method in high‐resolution regional seismic imaging based on waveform inversions of converted/scattered waves recorded by seismic array.

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 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)0.002
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.691
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.025
GPT teacher head0.295
Teacher spread0.270 · 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 teacher head, 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

Citations60
Published2014
Admission routes1
Has abstractyes

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