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Record W2746427643 · doi:10.1190/segam2017-17130568.1

Elastic microseismic source localization using rotational motion

2017· article· en· W2746427643 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeophysics and Sensor Technology
Canadian institutionsUniversity of Alberta
FundersMicroseismic Industry Consortium
KeywordsMicroseismRotation around a fixed axisMotion (physics)Computer sciencePhysicsGeologySeismologyArtificial intelligenceClassical mechanics

Abstract

fetched live from OpenAlex

Rotational motion is an important component for complete description of elastic wave propagation. Unfortunately, it is generally neglected. It provides information on the spatial gradient of particle displacement motion which aids in imaging passive sources using elastic waves. Event localization is for instance important in earthquake seismology and detection of microseismic events during hydraulic fracturing treatments of hydrocarbon reservoirs or injection of carbon dioxide (CO2) in depleted reservoirs. We propose an elastic reverse time extrapolation technique for passive event localization incorporating a new representation-theorem-based expression that explicitly uses recordings from rotational and particle velocity sensors either simultaneously or separately, leading to enhanced imaging results. Presentation Date: Monday, September 25, 2017 Start Time: 4:20 PM Location: 362D Presentation Type: ORAL

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.535
Threshold uncertainty score0.224

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.212
Teacher spread0.200 · 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

Quick stats

Citations1
Published2017
Admission routes2
Has abstractyes

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