{"id":"W1993664640","doi":"10.3997/2214-4609.20140715","title":"Fast Uncertainty Quantification for 2D Full-waveform Inversion with Randomized Source Subsampling","year":2014,"lang":"en","type":"article","venue":"Proceedings","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Markov chain Monte Carlo; Posterior probability; Hessian matrix; Computer science; Bayesian probability; Algorithm; Bayesian inference; Inversion (geology); Monte Carlo method; Importance sampling; Gaussian; Probability distribution; Uncertainty quantification; Mathematical optimization; Applied mathematics; Mathematics; Statistics; Artificial intelligence; Geology","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":[0.00266141,0.0006058672,0.0007449147,0.0009461868,0.0004203562,0.00104187,0.001046654,0.0007323575,0.001610587],"category_scores_gemma":[0.009346504,0.0005505214,0.0006550801,0.0008209426,0.0008292174,0.001410429,0.001887488,0.001158372,0.0002821494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001008311,"about_ca_system_score_gemma":0.001326106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005104299,"about_ca_topic_score_gemma":0.005026327,"domain_scores_codex":[0.9990426,0.0003346355,0.00004948647,0.0001169223,0.0003956036,0.00006072433],"domain_scores_gemma":[0.9958292,0.003107636,0.0002030544,0.0003575402,0.0004197594,0.0000829412],"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":[0.00009638682,0.000025423,0.0005464138,0.00007658399,0.00003680846,0.00006640572,0.00005935066,0.9053833,0.005671506,0.03769806,0.0008135466,0.04952625],"study_design_scores_gemma":[0.000002694417,0.000003712311,0.00006291917,0.000003201513,0.000001333101,0.000007724948,0.000002809949,0.9917797,0.0006127491,0.007337682,0.000180988,0.000004376032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005017217,0.00006193072,0.9941531,0.0000673081,0.00001026616,0.00001629243,0.0000627641,0.000208216,0.0004028622],"genre_scores_gemma":[0.3476982,0.0001854408,0.6504745,0.00008906596,0.00004225228,0.0001792123,0.0003770109,0.0001719835,0.0007824384],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005104299,"threshold_uncertainty_score":0.01407504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01611017588458965,"score_gpt":0.2264314995949564,"score_spread":0.2103213237103667,"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."}}