{"id":"W2342428708","doi":"10.1190/geo2015-0266.1","title":"Modified Gauss-Newton full-waveform inversion explained — Why sparsity-promoting updates do matter","year":2016,"lang":"en","type":"article","venue":"Geophysics","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Inversion (geology); Curvelet; Computer science; Algorithm; Nonlinear system; Fidelity; Mathematical optimization; Residual; Gradient descent; Norm (philosophy); Mathematics; Artificial intelligence; Artificial neural network; 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":[0.0008282744,0.0003531028,0.0003278639,0.0001547424,0.0002813345,0.0006300936,0.0005328861,0.0007885672,0.001945119],"category_scores_gemma":[0.003785506,0.0002403223,0.0002222652,0.0001892287,0.0008327013,0.0009743641,0.0005237567,0.001090029,0.0005023317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003423778,"about_ca_system_score_gemma":0.000509833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002020169,"about_ca_topic_score_gemma":0.001746814,"domain_scores_codex":[0.999746,0.00007154898,0.000009759457,0.0000349663,0.0001154046,0.0000223318],"domain_scores_gemma":[0.9989989,0.0005171364,0.00007870622,0.0001491603,0.0002125179,0.00004345256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002460214,0.00009715681,0.001906116,0.0001956789,0.00005114631,0.0003276807,0.0002836944,0.6606995,0.07731517,0.1464003,0.006346244,0.1061313],"study_design_scores_gemma":[0.000006211375,0.00001733202,0.0001613014,0.000005796168,0.000002293186,0.00003630245,0.000008558252,0.9877945,0.005633261,0.005192734,0.001135754,0.000005906915],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06612755,0.0002161719,0.9230857,0.001059871,0.0001163041,0.00004403318,0.00006089411,0.0005026002,0.008786813],"genre_scores_gemma":[0.6628803,0.0002640407,0.3307728,0.0002528514,0.00007035338,0.00005954647,0.0001578111,0.0003019725,0.005240352],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002020169,"threshold_uncertainty_score":0.006507039,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01272881402562908,"score_gpt":0.1920074646490837,"score_spread":0.1792786506234546,"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."}}