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Record W4410032486 · doi:10.1785/0220240435

Detection of Seismic Core Phases from the Northern Atlantic Cyclones on the Australian Spiral-Arm Arrays

2025· article· en· W4410032486 on OpenAlexaboutno aff
Abhay Pandey, ‪Hrvoje Tkalčić

Bibliographic record

VenueSeismological Research Letters · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeologySeismologyCore (optical fiber)Spiral (railway)EngineeringTelecommunications

Abstract

fetched live from OpenAlex

Abstract Microseisms, generated due to continuous harmonic forces by the ocean waves, represent a crucial source of seismic data. Although array techniques effectively detect microseismic signals, especially with dense large-aperture arrays, identifying low-amplitude, core-sensitive body-wave phases remain challenging and particularly rare in the southern hemisphere. The challenge of detecting microseismic core phases is intensified by overlapping with stronger, more pervasive surface-wave signals from proximal microseismic sources. Here, we investigate the feasibility of using small-aperture arrays with a spiral-arm design deployed at multiple locations in Australia. We observe energetic arrivals in secondary microseisms during the northern hemisphere winters and identify the core-sensitive seismic phases from the distant storms in the North Atlantic Ocean. We infer the character and geographic distribution of the sources generating these body-wave signals using the Capon and backprojection methods. We present the observations of distinct PKP branches from the southern tip of Greenland and Newfoundland basin, within the 4–6 s period band. Our findings highlight the efficacy of small-aperture spiral arrays in isolating core-sensitive signals from distant storm activity, with possible future implications for studying the Earth’s interior.

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)0.001
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.082
GPT teacher head0.311
Teacher spread0.229 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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