{"id":"W4380231007","doi":"10.1190/geo2022-0599.1","title":"Surface-related multiple attenuation based on a self-supervised deep neural network with local wavefield characteristics","year":2023,"lang":"en","type":"article","venue":"Geophysics","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Petro-Canada","funders":"National Natural Science Foundation of China","keywords":"Multiple; Attenuation; Surface (topology); Amplitude; Algorithm; Residual; Function (biology); Convolutional neural network; Subtraction; Artificial neural network; Mathematics; Computer science; Artificial intelligence; Optics; Physics; Geometry; Arithmetic","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":[0.0006315076,0.001061363,0.0008830699,0.0005605828,0.0003339209,0.0005295479,0.001755283,0.0009380811,0.001741375],"category_scores_gemma":[0.001188736,0.0004792364,0.0007019804,0.0004680333,0.0004999476,0.001171753,0.0009403873,0.001183568,0.0004714522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008736511,"about_ca_system_score_gemma":0.001189615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01035645,"about_ca_topic_score_gemma":0.01244312,"domain_scores_codex":[0.9997203,0.00002954423,0.00001568101,0.00008720436,0.0000915561,0.00005564254],"domain_scores_gemma":[0.9994972,0.0001208324,0.00006106366,0.00004371354,0.0002407581,0.00003643031],"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":[0.000202828,0.0001940268,0.002523239,0.0000723422,0.0001057637,0.0001112844,0.00006812208,0.6883379,0.01204888,0.00225347,0.003778193,0.290304],"study_design_scores_gemma":[0.000003256093,0.0000136763,0.00008874699,0.000002068866,0.000005126839,0.000004935537,0.000001896171,0.9988472,0.0006655859,0.000280787,0.00008407399,0.000002546758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1142567,0.0004222891,0.8796157,0.0003684445,0.0001011398,0.00006512691,0.000175437,0.002276941,0.002718193],"genre_scores_gemma":[0.8714007,0.00021692,0.1188114,0.0003936832,0.00007785361,0.0001242854,0.0008144798,0.0001433139,0.008017511],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01035645,"threshold_uncertainty_score":0.02059233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01018138659470123,"score_gpt":0.1874659258271401,"score_spread":0.1772845392324389,"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."}}