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Record W2075822215 · doi:10.1190/1.1438948

Integrating coherence cube imaging and seismic inversion

2001· article· en· W2075822215 on OpenAlexaff
Satinder Chopra

Bibliographic record

VenueThe Leading Edge · 2001
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsPenn West Exploration (Canada)
Fundersnot available
KeywordsExploitInversion (geology)Cube (algebra)GeologySeismologyCoherence (philosophical gambling strategy)Seismic inversionData cubeComputer scienceGeophysicsData miningGeographyPhysicsMeteorologyMathematicsGeometryTectonicsComputer security

Abstract

fetched live from OpenAlex

Despite the success of 3-D seismology, the geophysical community recognizes that these surveys contain more information than simply the structure or identification of isolated bodies in the subsurface. This conviction has led to new techniques to exploit the additional information. Amoco's development of coherence cube technology in the 1990s is an example. At about the same time, based on work by Roy Lindseth originally done in the 1970s, a plethora of 3-D poststack inversion algorithms arrived that delivered high-resolution information about the subsurface from seismic data.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.688
Threshold uncertainty score0.371

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 head0.017
GPT teacher head0.230
Teacher spread0.213 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations24
Published2001
Admission routes1
Has abstractyes

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