{"id":"W2971588278","doi":"10.1190/geo2019-0099.1","title":"Noise suppression in 2D and 3D seismic data with data-driven sifting algorithms","year":2019,"lang":"en","type":"article","venue":"Geophysics","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Algorithm; Deconvolution; Maxima and minima; Computer science; Filter (signal processing); Noise (video); Computation; Hilbert–Huang transform; Noise reduction; Geology; Mathematics; Artificial intelligence; Image (mathematics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001888291,0.0001106582,0.0001436624,0.00004671622,0.00006158403,0.00005596789,0.0005761442,0.0000369237,0.0001069486],"category_scores_gemma":[0.00001024426,0.00008384395,0.000006439076,0.0001352945,0.00005416307,0.000793685,0.0001868902,0.0001672449,0.0001511361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002210502,"about_ca_system_score_gemma":0.00003679992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006524459,"about_ca_topic_score_gemma":0.00004822121,"domain_scores_codex":[0.9990224,0.00003642024,0.0001136596,0.0004331414,0.0001776555,0.0002167737],"domain_scores_gemma":[0.9989465,0.00007363329,0.00005707201,0.0008571158,0.00001379033,0.00005188346],"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":[0.00004806143,0.00003160298,0.3767731,0.00006529194,0.00001536493,0.0000272489,0.0003605611,0.002249514,0.0001357875,0.000006561098,0.01087725,0.6094096],"study_design_scores_gemma":[0.0002704945,0.00005986945,0.03275117,0.00007708863,0.000009130346,0.000007991115,0.000148597,0.9503007,0.0001025283,0.0001601721,0.01595763,0.0001546144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958374,0.0001527909,0.0008383942,0.0002915068,0.0001731783,0.0001859553,0.0004400242,0.00009238119,0.001988373],"genre_scores_gemma":[0.9871964,0.00007199858,0.009636637,0.0007636329,0.00007527357,2.922087e-7,0.002065928,0.000005465082,0.0001843416],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9480512,"threshold_uncertainty_score":0.9863073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02166989705905231,"score_gpt":0.2329865790070818,"score_spread":0.2113166819480294,"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."}}