{"id":"W4400223192","doi":"10.1190/geo2023-0499.1","title":"Angle-dependent image-domain least-squares migration through analytical point spread functions","year":2024,"lang":"en","type":"article","venue":"Geophysics","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Point (geometry); Image (mathematics); Domain (mathematical analysis); Least-squares function approximation; Seismic migration; Geology; Mathematics; Algorithm; Computer science; Geometry; Mathematical analysis; Statistics; Artificial intelligence; Seismology","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":["insufficient_payload"],"category_scores_codex":[0.0001445381,0.0001393941,0.0001237973,0.00006710961,0.0001559953,0.0001982556,0.000126196,0.00005258677,0.001081361],"category_scores_gemma":[0.00001594687,0.000115535,0.0001106039,0.0002872443,0.0001046882,0.0005903094,0.00001421979,0.000199842,0.002487315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001185585,"about_ca_system_score_gemma":0.00005316755,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006817261,"about_ca_topic_score_gemma":0.0001208853,"domain_scores_codex":[0.9989709,0.00004966633,0.0001639253,0.0003008719,0.0002666353,0.0002480122],"domain_scores_gemma":[0.9995745,0.0000825187,0.00002643765,0.0002098588,0.00004131775,0.00006537244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008209934,0.0001390661,0.01388992,0.0001716521,0.0001663305,0.0001971347,0.002671364,0.000569153,0.001344534,0.008623959,0.6004301,0.3717147],"study_design_scores_gemma":[0.000435082,0.0006579974,0.02331503,0.000234141,0.0002063188,0.0001139621,0.00376109,0.3206552,0.005861665,0.1955771,0.4481203,0.00106207],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.746028,0.001970168,0.1384513,0.01743226,0.003111687,0.0005285258,0.0005920797,0.002260993,0.08962494],"genre_scores_gemma":[0.9932511,0.00005953187,0.002828353,0.001157623,0.0003998228,0.000002515681,0.0002796776,0.000008096445,0.002013217],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3706526,"threshold_uncertainty_score":0.9998318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01367417214630261,"score_gpt":0.2318798695768457,"score_spread":0.2182056974305431,"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."}}