{"id":"W3045289778","doi":"10.1093/gji/ggaa330","title":"Time-domain elastic Gauss–Newton full-waveform inversion: a matrix-free approach","year":2020,"lang":"en","type":"article","venue":"Geophysical Journal International","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Hessian matrix; Conjugate gradient method; Mathematics; Newton's method; Inversion (geology); Gauss; Least-squares function approximation; Mathematical analysis; Matrix (chemical analysis); Algorithm; Applied mathematics; Physics; Nonlinear system; Estimator; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"category_scores_codex":[0.0001801859,0.0001708061,0.0001916008,0.0001027848,0.0001870136,0.0001660545,0.0007929931,0.00006512221,0.004738885],"category_scores_gemma":[0.000104873,0.0001346972,0.0001743957,0.0001873217,0.0001274546,0.0004610587,0.00007486816,0.0005003073,0.002570263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000229857,"about_ca_system_score_gemma":0.00005938903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001040998,"about_ca_topic_score_gemma":7.077267e-7,"domain_scores_codex":[0.998413,0.00006309308,0.0002897341,0.0002543656,0.0006974661,0.0002823719],"domain_scores_gemma":[0.9991955,0.00007868721,0.0001494402,0.0001343788,0.0001033046,0.0003387611],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007747351,0.0001887462,0.003325629,0.00003356253,0.0002520656,0.0002551204,0.001055204,0.002431706,0.00231619,0.001246894,0.9434764,0.04464374],"study_design_scores_gemma":[0.001471121,0.000661623,0.003942891,0.00005731809,0.0000405857,0.0006475796,0.0005391054,0.7532423,0.0005748421,0.01809468,0.2202259,0.0005020497],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7459877,0.0002299549,0.03762911,0.1024852,0.002594039,0.0003907239,0.0002554023,0.0005842342,0.1098436],"genre_scores_gemma":[0.9662037,0.00006083267,0.0148762,0.01190418,0.00378246,0.000001446812,0.0002671354,0.00001585834,0.002888225],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7508106,"threshold_uncertainty_score":0.9982064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01288304641506868,"score_gpt":0.2140802119298003,"score_spread":0.2011971655147316,"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."}}