MétaCan
Menu
Back to cohort
Record W1534574487 · doi:10.1109/icassp.1987.1169317

AGIS: An expert system for automated geophysical interpretation of seismic images

2005· article· en· W1534574487 on OpenAlexaff
Ioannis Pitas, A. Venetsanopoulos

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGeologyAnticlineGeophysical imagingExpert systemInterpretation (philosophy)GeophysicsSeismologyComputer scienceArtificial intelligenceTectonics

Abstract

fetched live from OpenAlex

Geophysical interpretation of seismic images is part of geophysical oil prospecting. Its aim is the detection of geologic formations in a seismic cross-section of the earth, which are likely to contain oil reservoirs. It is a labour intensive task and it is heavily based on the experience of the interpreter. Therefore it has not been automated as ithas already been done with other tasks of geophysical seismic signal processing. The aim of this work is to construct an expert system which can automate, at least partly, seismic interpretation. The system developed is able to detect several geologic formations of interest (eg. faults, anticlines, unconformities, salt domes, reefs).

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.623
Threshold uncertainty score0.321

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 head0.291
Teacher spread0.280 · 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

Citations3
Published2005
Admission routes1
Has abstractyes

Explore more

Same topicReservoir Engineering and Simulation MethodsFrench-language works237,207