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Record W3137125663 · doi:10.4133/sageep.32-056

HVSR MEASUREMENTS OF TWO-DIMENSIONAL SUBSURFACE STRUCTURES – COMPARISONS WITH SEISMIC REFLECTION PROFILES

2019· article· en· W3137125663 on OpenAlexaff
B Dietiker, James A. Hunter

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsGeological Survey of Canada
Fundersnot available
KeywordsReflection (computer programming)GeologySeismologyRemote sensingOpticsComputer sciencePhysics

Abstract

fetched live from OpenAlex

Horizontal-to-vertical spectral ratio (HVSR) measurements of ambient seismic noise have gained popularity for seismic microzonation and assessment of earthquake site characteristics such as fundamental frequency (or period). Empirical relationships have been developed to link the fundamental frequency and sediment thickness at sites where a one-dimensional model is a good approximation of the subsurface. This assumption is not always valid, and we present key points when a 1D assumption is out of place. Effects of two-dimensional subsurface structure (non-horizontal layering) have been successfully modelled and can be observed on the orthogonal horizontal spectral ratios in that N-S/V and E-W/V show different peak frequencies and peak amplitudes. We examine both orthogonal horizontal components separately and show that differences between the orthogonal H/V components infer a 2D subsurface. Rotating the horizontal components to maximize/minimize peak amplitudes allows the calculation of azimuthal spectral angles. These preferred angle orientations are shown to be related to the subsurface structure.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.033
GPT teacher head0.254
Teacher spread0.221 · 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 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

Citations1
Published2019
Admission routes1
Has abstractyes

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