{"id":"W2037469421","doi":"10.1190/geo2012-0104.1","title":"Regularized seismic full waveform inversion with prior model information","year":2013,"lang":"en","type":"article","venue":"Geophysics","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":213,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geoscience BC","funders":"Grand Équipement National De Calcul Intensif","keywords":"Inversion (geology); Smoothing; Computer science; Algorithm; Inverse problem; Prior information; Weighting; Tikhonov regularization; Synthetic data; Prior probability; Mathematical optimization; Bayesian probability; Geology; Mathematics; Artificial intelligence; Computer vision","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007095648,0.0001188888,0.0001109428,0.00006756168,0.0001527777,0.00008777512,0.0001542386,0.00005206209,0.00031801],"category_scores_gemma":[0.000005771548,0.00008530929,0.00003611451,0.0001597498,0.00006148573,0.001656053,0.00001377158,0.0001229875,0.00210486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008321968,"about_ca_system_score_gemma":0.00005787827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004154957,"about_ca_topic_score_gemma":0.000006573637,"domain_scores_codex":[0.9992729,0.00001301328,0.0001371417,0.000117581,0.0002360168,0.0002233805],"domain_scores_gemma":[0.9995296,0.00001524086,0.00008547318,0.0002026458,0.0000869134,0.00008010026],"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":[0.0001225916,0.0000258541,0.002767136,0.00006488983,0.00002627577,0.000001728033,0.001178255,0.01313335,0.0003815856,0.0002664273,0.05091334,0.9311185],"study_design_scores_gemma":[0.0003032283,0.0001081261,0.00261903,0.00001785991,0.000008842573,0.000004328144,0.0001552621,0.9829299,0.000894458,0.008028909,0.004770542,0.0001594728],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9589428,0.000008407997,0.02359661,0.0009486802,0.00009625185,0.0003213439,0.00001478905,0.0002642813,0.01580684],"genre_scores_gemma":[0.9822915,0.00001727382,0.0125091,0.00424124,0.00003641662,0.000003109687,0.0002280057,0.00000340001,0.0006699382],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9697966,"threshold_uncertainty_score":0.9986721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005828216760964946,"score_gpt":0.1636845600290288,"score_spread":0.1578563432680638,"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."}}