{"id":"W2580041566","doi":"10.1190/geo2015-0595.1","title":"Accelerating Hessian-free Gauss-Newton full-waveform inversion via l-BFGS preconditioned conjugate-gradient algorithm","year":2017,"lang":"en","type":"article","venue":"Geophysics","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geoscience BC; Penn West Exploration (Canada); University of Calgary","funders":"","keywords":"Hessian matrix; Quasi-Newton method; Broyden–Fletcher–Goldfarb–Shanno algorithm; Conjugate gradient method; Preconditioner; Nonlinear conjugate gradient method; Diagonal; Mathematics; Algorithm; Newton's method; Applied mathematics; Gradient descent; Mathematical optimization; Computer science; Iterative method; Geometry; Nonlinear system; Physics","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":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002150561,0.0002257187,0.0002318756,0.0000722085,0.001364375,0.0003305913,0.0007385374,0.0001019973,0.0008173405],"category_scores_gemma":[0.000040382,0.0001973502,0.0001101108,0.00007820148,0.0002039019,0.001107944,0.00009193482,0.0002515607,0.0007872947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001777584,"about_ca_system_score_gemma":0.00004808594,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007069841,"about_ca_topic_score_gemma":0.00007722857,"domain_scores_codex":[0.9985837,0.00003689501,0.0002473467,0.0003713784,0.0003303371,0.0004303809],"domain_scores_gemma":[0.9985871,0.0000460948,0.0002939933,0.0008322659,0.00007576186,0.0001647406],"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":[0.00004300345,0.00007258982,0.01545752,0.00004969362,0.00004736123,0.00003173229,0.0004919523,0.00004977177,0.000773794,0.0002133386,0.03873871,0.9440305],"study_design_scores_gemma":[0.002676091,0.001286514,0.1697483,0.0003036295,0.0001124937,0.00007845234,0.0007224451,0.6156152,0.05795508,0.09887762,0.05086868,0.001755412],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9423487,0.0001108952,0.005166529,0.003780186,0.002631494,0.0005678274,0.000286157,0.0005674192,0.04454086],"genre_scores_gemma":[0.9879228,0.00006695704,0.0083505,0.001384096,0.0005555885,0.000004423418,0.0004803464,0.00001216734,0.001223161],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9422751,"threshold_uncertainty_score":0.9999907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0192236425869114,"score_gpt":0.2224312761527107,"score_spread":0.2032076335657994,"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."}}