{"id":"W2967052740","doi":"10.1190/geo2018-0259.1","title":"A plane-wave formulation and numerical analysis of elastic multicomponent inverse scattering series internal multiple prediction","year":2019,"lang":"en","type":"article","venue":"Geophysics","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inversion (geology); Algorithm; Inverse problem; Series (stratigraphy); Inverse; Inverse scattering problem; Computer science; Process (computing); Domain (mathematical analysis); Plane (geometry); Plane wave; Time series; Time domain; Scattering; Mathematics; Geology; Mathematical analysis; Geometry; Physics; Optics; Machine learning; Seismology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007485275,0.0003911923,0.0002776678,0.0004530017,0.0003738954,0.0008207717,0.0006850094,0.0007715648,0.001921414],"category_scores_gemma":[0.002197867,0.0002255817,0.0004622827,0.0004636611,0.0009591664,0.0007616407,0.0006344679,0.0009851153,0.0002674224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006420326,"about_ca_system_score_gemma":0.0006321941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006006288,"about_ca_topic_score_gemma":0.004314795,"domain_scores_codex":[0.9998273,0.00004579349,0.000008114328,0.00002196645,0.00008127979,0.00001553554],"domain_scores_gemma":[0.9993762,0.0003359274,0.00006200191,0.00004378046,0.0001594466,0.00002275057],"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":[0.00001854167,0.00002811461,0.0008228878,0.00003529211,0.000008487046,0.0001049393,0.00006831031,0.9124297,0.002403526,0.07307202,0.0006211519,0.01038691],"study_design_scores_gemma":[6.105766e-7,0.000001827364,0.00002591706,0.000001186697,3.215734e-7,0.000003100504,0.000003596243,0.9982742,0.0001060611,0.001461836,0.0001203564,0.000001063083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03301034,0.0001790735,0.9593657,0.0003312223,0.00006303925,0.00004257085,0.00004313293,0.00007904661,0.00688598],"genre_scores_gemma":[0.596829,0.0004955278,0.3905926,0.0001364614,0.0001150731,0.0001564361,0.0001565093,0.0001252573,0.01139312],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006006288,"threshold_uncertainty_score":0.01194268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01131372323421081,"score_gpt":0.1910380308957677,"score_spread":0.1797243076615568,"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."}}