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Record W3210183623 · doi:10.1093/gji/ggab418

Global reference seismological data sets: multimode surface wave dispersion

2021· article· en· W3210183623 on OpenAlexaff
P. Moulik, V. Lekić, Barbara Romanowicz, Zhitu Ma, A. J. Schaeffer, Tak Ho, É. Beucler, E. Debayle, Arwen Deuss, S. Durand, Göran Ekström, Sergei Lebedev, G. Masters, Keith Priestley, Jeroen Ritsema, Karin Sigloch, Jeannot Trampert, Adam M. Dziewoński

Bibliographic record

VenueGeophysical Journal International · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicHigh-pressure geophysics and materials
Canadian institutionsGeological Survey of Canada
FundersDavid and Lucile Packard FoundationNational Science Foundation
KeywordsOvertoneDispersion (optics)GeologyGeodesyData qualityMetadataData setRemote sensingComputer scienceSeismologyOpticsPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

SUMMARY Global variations in the propagation of fundamental-mode and overtone surface waves provide unique constraints on the low-frequency source properties and structure of the Earth’s upper mantle, transition zone and mid mantle. We construct a reference data set of multimode dispersion measurements by reconciling large and diverse catalogues of Love-wave (49.65 million) and Rayleigh-wave dispersion (177.66 million) from eight groups worldwide. The reference data set summarizes measurements of dispersion of fundamental-mode surface waves and up to six overtone branches from 44 871 earthquakes recorded on 12 222 globally distributed seismographic stations. Dispersion curves are specified at a set of reference periods between 25 and 250 s to determine propagation-phase anomalies with respect to a reference Earth model. Our procedures for reconciling data sets include: (1) controlling quality and salvaging missing metadata; (2) identifying discrepant measurements and reasons for discrepancies; (3) equalizing geographic coverage by constructing summary rays for travel-time observations and (4) constructing phase velocity maps at various wavelengths with combination of data types to evaluate inter-dataset consistency. We retrieved missing station and earthquake metadata in several legacy compilations and codified scalable formats to facilitate reproducibility, easy storage and fast input/output on high-performance-computing systems. Outliers can be attributed to cycle skipping, station polarity issues or overtone interference at specific epicentral distances. By assessing inter-dataset consistency across similar paths, we empirically quantified uncertainties in traveltime measurements. More than 95 per cent measurements of fundamental-mode dispersion are internally consistent, but agreement deteriorates for overtones especially branches 5 and 6. Systematic discrepancies between raw phase anomalies from various techniques can be attributed to discrepant theoretical approximations, reference Earth models and processing schemes. Phase-velocity variations yielded by the inversion of the summary data set are highly correlated (R ≥ 0.8) with those from the quality-controlled contributing data sets. Long-wavelength variations in fundamental-mode dispersion (50–100 s) are largely independent of the measurement technique with high correlations extending up to degree ∼25. Agreement degrades with increasing branch number and period; highly correlated structure is found only up to degree ∼10 at longer periods (T > 150 s) and up to degree ∼8 for overtones. Only 2ζ azimuthal variations in phase velocity of fundamental-mode Rayleigh waves were required by the reference data set; maps of 2ζ azimuthal variations are highly consistent between catalogues ( R = 0.6–0.8). Reference data with uncertainties are useful for improving existing measurement techniques, validating models of interior structure, calculating teleseismic data corrections in local or multiscale investigations and developing a 3-D reference Earth model.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.477
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.058
GPT teacher head0.289
Teacher spread0.231 · 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.

Study designObservational
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

Citations22
Published2021
Admission routes1
Has abstractyes

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