{"id":"W2765690763","doi":"10.1190/geo2017-0321.1","title":"Assessing uncertainties in velocity models and images with a fast nonlinear uncertainty quantification method","year":2017,"lang":"en","type":"article","venue":"Geophysics","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Uncertainty quantification; Bayesian probability; Bayesian inference; Geophysical imaging; Algorithm; Nonlinear system; Computer science; Inversion (geology); Inverse problem; Inference; Image (mathematics); Geology; Mathematics; Artificial intelligence; Seismology; Physics; Machine learning","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":[0.003527767,0.0008616179,0.0006129654,0.001619173,0.0005129559,0.001511796,0.001396589,0.001123401,0.001188708],"category_scores_gemma":[0.01410595,0.0007464276,0.0008834511,0.0008193585,0.001418511,0.002352423,0.002285508,0.001656107,0.000239854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000962352,"about_ca_system_score_gemma":0.00161559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006077231,"about_ca_topic_score_gemma":0.00481987,"domain_scores_codex":[0.9987947,0.0004431902,0.00005931227,0.000175588,0.000460115,0.00006715399],"domain_scores_gemma":[0.9937882,0.00452208,0.0005085397,0.0005505757,0.0005418661,0.00008872445],"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":[0.00004141614,0.00002629609,0.0007044032,0.0000403686,0.00003766045,0.00006242032,0.00007798969,0.9072557,0.003866456,0.03722055,0.0002912191,0.05037551],"study_design_scores_gemma":[0.000002187379,0.000006836057,0.00009745112,0.000003353355,0.000002741354,0.00001420434,0.000004535407,0.9886737,0.0007794874,0.01022391,0.0001826662,0.000008929676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004247386,0.00002141508,0.9953796,0.00002959985,0.000003252277,0.00001009172,0.00002069511,0.0001004063,0.0001876849],"genre_scores_gemma":[0.260738,0.0001403548,0.7376704,0.00004734669,0.00003029102,0.0001041289,0.00020299,0.0001332755,0.0009332525],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006077231,"threshold_uncertainty_score":0.01865679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04014266133015374,"score_gpt":0.2966454224078366,"score_spread":0.2565027610776828,"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."}}