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Investigating the Effect of Fluid Substitution on AVO Response: A Physical Modelling Study

2019· article· en· W2986556378 on OpenAlexaff
Kamal Moravej, Alison Malcolm

Bibliographic record

VenueASEG Extended Abstracts · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsAmplitudeAmplitude versus offsetOffset (computer science)Reflection (computer programming)Substitution (logic)Plot (graphics)Position (finance)Point (geometry)AcousticsMechanicsGeologySoil scienceEnvironmental scienceComputer scienceMathematicsOpticsPhysicsStatisticsGeometry

Abstract

fetched live from OpenAlex

SummarySeismic physical modelling is a powerful method for verification of different algorithms and also for better understanding wave propagation phenomenon. In this paper, we design a physical model made out of Plexiglas to investigate the effect of fluid substitution on Amplitude Variation with offset (AVO) by designing a physical model in a way that remove the influence of other factors that can impact the AVO behavior. We record the seismic response of two scenarios, one that is representative of dry/gas saturated medium and another one is the representative of water saturated medium. After processing of recorded seismic data and picking the reflection amplitude, we implement some corrections over the reflection amplitude to represent true reflectivity. The corrected amplitude of both cases were used to extract Intercept (R0) and Gradient (G) AVO attributes based on the Shuey equation. The extracted values are mapped on R0 − G cross-plot. From the cross-plot, it is clear that the substitution of water with gas is resulted in shifting the position of point toward the center of the cross-plot.

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)0.001
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 score0.391
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.248
Teacher spread0.232 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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