{"id":"W4412751676","doi":"10.1190/geo2024-0891.1","title":"Unsupervised seismic random noise attenuation via 3D enhanced multiscale features","year":2025,"lang":"en","type":"article","venue":"Geophysics","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Petro-Canada","funders":"National Natural Science Foundation of China","keywords":"Computer science; Scale (ratio); Noise (video); Random noise; Seismic noise; Pattern recognition (psychology); Geology; Artificial intelligence; Seismology; Algorithm; Cartography; Geography","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":[0.000477745,0.0005226189,0.0005716057,0.0005461393,0.0001623038,0.0003916735,0.0007479917,0.0004586033,0.0007632283],"category_scores_gemma":[0.00126895,0.0002616766,0.0006892203,0.000457773,0.0004049371,0.0008256783,0.001027294,0.0006467985,0.0002699979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002907199,"about_ca_system_score_gemma":0.0004381349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001616019,"about_ca_topic_score_gemma":0.002220053,"domain_scores_codex":[0.9997706,0.0000414006,0.000009908777,0.00005451507,0.00009304622,0.00003059488],"domain_scores_gemma":[0.9996516,0.0001172386,0.00005595629,0.00006336078,0.00008954862,0.00002230792],"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":[0.0002368336,0.0001287843,0.002355095,0.0001042256,0.0001154665,0.0001705082,0.0001176355,0.5325508,0.1355049,0.01060114,0.001806962,0.3163075],"study_design_scores_gemma":[0.000003407567,0.00002057748,0.0002548854,0.000002011544,0.00000738558,0.00002457069,0.000003762736,0.9907958,0.007446521,0.001144088,0.0002924579,0.000004445297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03483968,0.00006024427,0.9640579,0.00004796093,0.00001121969,0.00001123478,0.00004149739,0.0004181251,0.0005121046],"genre_scores_gemma":[0.7411212,0.0001269322,0.256568,0.000106205,0.00002808681,0.00004225918,0.0003337605,0.0001304788,0.001543086],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001616019,"threshold_uncertainty_score":0.003213167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00538888025451729,"score_gpt":0.2083700719917417,"score_spread":0.2029811917372244,"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."}}