{"id":"W2596997006","doi":"10.1190/geo2016-0448.1","title":"Processing and quality-control strategies for consistent time-lapse seismic attributes: A case history on an internal blowout using vintage data","year":2017,"lang":"en","type":"article","venue":"Geophysics","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vintage; Geology; Prestack; Data processing; Reflection (computer programming); Quality (philosophy); Data quality; Multiple; Well control; Control (management); Seismology; Attenuation; Computer science; Remote sensing; Artificial intelligence; Drilling; Engineering; Database; Geography; Operations management; Mathematics; Optics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004607886,0.0001572737,0.0002195947,0.00004215187,0.0005467843,0.0003131178,0.0004141971,0.00005109862,0.00003569816],"category_scores_gemma":[0.0000620026,0.0001397789,0.00003792499,0.00001534464,0.0002444319,0.001118238,0.0000426426,0.0001378885,0.00001483677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002067909,"about_ca_system_score_gemma":0.0001691153,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01303746,"about_ca_topic_score_gemma":0.0001098604,"domain_scores_codex":[0.9989861,0.00006460519,0.0001895507,0.0003825858,0.0001388808,0.000238312],"domain_scores_gemma":[0.9988353,0.0001093264,0.0002371329,0.0006584879,0.00006195954,0.00009775552],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001272677,0.0003974322,0.02586282,0.0009707287,0.0002715412,0.0009602473,0.004709944,0.002888945,0.003855516,0.0007926324,0.02636547,0.9316521],"study_design_scores_gemma":[0.00076346,0.0002491759,0.001471538,0.0001224318,0.00007356042,0.0001549515,0.0007741138,0.9850498,0.0001625067,0.001771115,0.00909861,0.0003087437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9874896,0.0002882901,0.00992916,0.0001995453,0.0002357932,0.0002546494,0.0007588425,0.0001004148,0.000743658],"genre_scores_gemma":[0.9960794,0.000006627972,0.001866483,0.001479591,0.0001801334,0.000001292972,0.0001702822,0.000006804644,0.0002094135],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9821609,"threshold_uncertainty_score":0.9935348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1018024112214836,"score_gpt":0.3095766685176468,"score_spread":0.2077742572961632,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}