{"id":"W2891749414","doi":"10.3997/2214-4609.201801393","title":"Seismic Data Reconstruction with Generative Adversarial Networks","year":2018,"lang":"en","type":"article","venue":"Proceedings","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Sampling (signal processing); Artificial neural network; Compressed sensing; Artificial intelligence; Adaptive sampling; Process (computing); Key (lock); Iterative reconstruction; Adversarial system; Data mining; Pattern recognition (psychology); Machine learning; Algorithm; Computer vision; Mathematics","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":[0.001276182,0.0007174761,0.0006853937,0.0004398568,0.0002450188,0.0006714595,0.0009044313,0.001128244,0.001858276],"category_scores_gemma":[0.004069477,0.000591456,0.0006379304,0.0004189808,0.001230574,0.0007874708,0.001680541,0.001977397,0.0003997512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000795108,"about_ca_system_score_gemma":0.00062564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003890625,"about_ca_topic_score_gemma":0.003159653,"domain_scores_codex":[0.9995447,0.000169786,0.00001914486,0.0000957085,0.0001258288,0.00004476605],"domain_scores_gemma":[0.9978574,0.001524052,0.0001634334,0.0002224662,0.0001715898,0.0000611238],"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":[0.00004482261,0.00000806925,0.0002642863,0.00001599689,0.00001629068,0.00002875925,0.00001584583,0.9807896,0.0006055572,0.007969094,0.0004431249,0.009798527],"study_design_scores_gemma":[0.000001615488,0.000002594535,0.00001576364,0.000001664072,9.34668e-7,0.000003935842,0.000001061526,0.997152,0.000148647,0.0025807,0.00008957303,0.000001493245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01300123,0.0001745835,0.9842961,0.000396554,0.00002978236,0.00002214905,0.00008270214,0.0003614065,0.001635508],"genre_scores_gemma":[0.8352463,0.000283689,0.156861,0.0003463363,0.00009564133,0.0001203007,0.0004034271,0.0001314024,0.006511942],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003890625,"threshold_uncertainty_score":0.007735968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02209428242308527,"score_gpt":0.2202410631804739,"score_spread":0.1981467807573886,"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."}}