{"id":"W4289238839","doi":"10.1190/geo2022-0148.1","title":"Unsupervised contrastive learning for seismic facies characterization","year":2022,"lang":"en","type":"article","venue":"Geophysics","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Petro-Canada","funders":"National Natural Science Foundation of China","keywords":"Facies; Geology; Seismic attribute; Leverage (statistics); Consistency (knowledge bases); Unsupervised learning; Computer science; Cluster analysis; Artificial intelligence; Pattern recognition (psychology); Seismology; Structural basin; Paleontology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001488898,0.000841699,0.0007003226,0.00159596,0.000496319,0.0007106047,0.001366111,0.0009242997,0.001162231],"category_scores_gemma":[0.004431943,0.0003576383,0.0007582981,0.000833919,0.0009964894,0.00108687,0.001294274,0.001416211,0.0004112904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000998794,"about_ca_system_score_gemma":0.0007325655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003935248,"about_ca_topic_score_gemma":0.005523753,"domain_scores_codex":[0.999245,0.0002115508,0.00003541615,0.0002474335,0.0001722744,0.00008835452],"domain_scores_gemma":[0.9976095,0.001259004,0.0002800742,0.0002679564,0.0004831326,0.0001002737],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004572172,0.0003443399,0.0111042,0.00006907794,0.000142668,0.000153129,0.0001495062,0.5613909,0.03383894,0.006175094,0.001804946,0.3843699],"study_design_scores_gemma":[0.000004094859,0.00001965827,0.0004924092,0.00000177789,0.000003137536,0.00001018656,0.000007289398,0.9954125,0.002431802,0.001496496,0.0001173174,0.000003379394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1086947,0.0001007502,0.8886013,0.0001625555,0.00001801234,0.00005525844,0.0001470519,0.0009488288,0.001271555],"genre_scores_gemma":[0.7602061,0.00004487547,0.2375488,0.0001051762,0.00004089615,0.00007349417,0.0006133805,0.0001104978,0.001256877],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003935248,"threshold_uncertainty_score":0.007874131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01117055176572635,"score_gpt":0.1934821685033518,"score_spread":0.1823116167376254,"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."}}