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Bias in seismic estimates of crustal properties

2010· article· en· W2102457532 on OpenAlexaff
M. G. Bostock, M. Ravi Kumar

Bibliographic record

VenueGeophysical Journal International · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGeologyEstimatorCrustSeismologyLeast-squares function approximationGeodesySeismic velocityGridSeismic arrayWaveformMatching (statistics)GeophysicsStatisticsMathematicsPhysics

Abstract

fetched live from OpenAlex

It has come to our attention that the least-squares estimator we proposed in a recent paper to recover bulk crustal P velocity, VP, and P velocity/S velocity ratio, R, of the crust from traveltimes of scattered teleseismic phases, is inconsistent. That is, in the presence of errors, estimates of R will be biased downwards and estimates of VP will be biased upwards from their true values. In this note we supply bias corrections for both quantities that depend upon an estimate of the variance in a ratio of traveltime sums, and demonstrate their validity through comparison with estimates based on a two-parameter grid search. Application to station HYB yields R= 1.756 ± 0.005, VP= 6.2 ± 0.1 km s−1, after correction of a systematic traveltime picking error that had affected our previous results. In addition, we provide an alternative waveform-stacking approach that involves a 2-D grid search over R and VP followed by a 1-D line search over crustal thickness H. An estimate of the direct Ps conversion time is required, and results from station HYB are consistent with those produced using traveltimes alone.

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)0.005
metaresearch head score (Gemma)0.031
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.031
GPT teacher head0.243
Teacher spread0.212 · 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".

Quick stats

Citations8
Published2010
Admission routes1
Has abstractyes

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