MétaCan
Menu
Back to cohort
Record W2501113198 · doi:10.1190/1.9781560802197.ch7

Inversion of Seismic Data for Elastic Parameters: A Tool for Gas-Hydrate Characterization

2010· book-chapter· en· W2501113198 on OpenAlexaff
Michael Riedel, M. W. Lee, Gilles Bellefleur

Bibliographic record

VenueSociety of Exploration Geophysicists eBooks · 2010
Typebook-chapter
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsGeological Survey of CanadaNatural Resources Canada
Fundersnot available
KeywordsPhysicsCrystallographyAnalytical Chemistry (journal)ChemistryEnvironmental chemistry

Abstract

fetched live from OpenAlex

This paper reviews various seismic inversion techniques (amplitude-versus-offset [AVO], acoustic and elastic impedance, prestack waveform inversion) for assessing elastic parameters of sediments and more specifically hydrate-bearing sediments. Several theoretical approaches are described, and examples of the application of the inversion schemes to assess gas-hydrate deposits in three different geologic environments are compared. The first example is from a permafrost-related gas-hydrate deposit at Mallik, the second example is from the Blake Ridge offshore Carolina (location of Ocean Drilling Program Leg 164), and the third example is from the Gulf of Mexico (Atwater Valley and Keathley Canyon). The techniques used in these areas are band-limited acoustic impedance inversion (Mallik), poststack elastic impedance inversion (Blake Ridge), and a hybrid inversion scheme, utilizing prestack waveform inversion with poststack AVO inversion (Gulf of Mexico).

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.047
GPT teacher head0.238
Teacher spread0.191 · 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
GenreMethods

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

Citations7
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueSociety of Exploration Geophysicists eBooksSame topicSeismic Imaging and Inversion TechniquesFrench-language works237,207