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Record W4401206833 · doi:10.1093/gji/ggae246

Full waveform inversion of ocean bottom seismometer data from the oceanic Pacific Plate of the Japan trench

2024· article· en· W4401206833 on OpenAlexfundno aff
Umedzhon Kakhkhorov, W. W. Weibul, Espen Birger Raknes, Shuichi Kodaira, G. Fujie, Børge Arntsen

Bibliographic record

VenueGeophysical Journal International · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsnot available
FundersNorges ForskningsrådCanadian Geological Foundation
KeywordsGeologySeismologyTrenchSeismometerCrustOceanic crustReceiver functionInversion (geology)GeodesyTomographySeismic tomographyWaveformGeophysicsMantle (geology)SubductionTectonicsLithosphereOpticsLayer (electronics)

Abstract

fetched live from OpenAlex

SUMMARY The geometrical and seismic structure of the Pacific oceanic Plate of the Japan Trench is essential for the understanding of earthquake activity in the area. Ocean Bottom Seismometer (OBS) data can be used, via ray-based tomography, to obtain estimates of properties such as crust thickness and structure, hydration and depth to the Moho boundary. The spatial resolution of these properties can be substantially improved by using the full waveform inversion (FWI) method. Most OBS data in this area are acquired with a sparse receiver spacing of 5–6 km, whereas FWI is assumed to work best with denser (1–2 km) receiver spacing. We show that FWI can be adapted to sparsely sampled data with better resolution than traveltime tomography. Using a 500 km long OBS longitudinal profile from the Japan Trench we obtain a detailed velocity structure of the crust, a better definition of the Moho boundary, a well-defined low-velocity layer in the lower crust and a clear spatial definition of areas with velocity inversions.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.707
Threshold uncertainty score1.000

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.237
Teacher spread0.214 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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