{"id":"W4411358607","doi":"10.1109/lgrs.2025.3580648","title":"Unsupervised Seismic Erratic Noise Suppression Using Implicit Neural Representation","year":2025,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Noise (video); Artificial neural network; Representation (politics); Pattern recognition (psychology); Speech recognition; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.00023995,0.00015025,0.0001614565,0.0002404153,0.0005120117,0.0001619835,0.0001487144,0.00005565344,0.00001162488],"category_scores_gemma":[0.00003836178,0.0001237999,0.00004894604,0.0004826129,0.0002858439,0.0003495284,0.00001815666,0.0001617272,0.000006929678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001073652,"about_ca_system_score_gemma":0.00003902247,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01610279,"about_ca_topic_score_gemma":0.00002534698,"domain_scores_codex":[0.9987061,0.00009890259,0.0002096299,0.0004246564,0.0002184784,0.0003422415],"domain_scores_gemma":[0.9995014,0.00008531623,0.00006911629,0.0002314786,0.00003234506,0.00008030904],"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":[0.00003162827,0.000004561105,0.006105421,0.00003954803,0.000008680589,0.00004033617,0.0004902966,0.005425015,0.247105,9.424451e-7,0.007450337,0.7332982],"study_design_scores_gemma":[0.0001660857,0.00001708125,0.01100841,0.0001226801,0.00001787891,0.00006355908,0.0001837295,0.9759736,0.01147431,0.000267059,0.0005520748,0.0001535179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9281244,0.00007989784,0.06508291,0.005057826,0.001043288,0.0001348671,0.000003701208,0.0001105286,0.0003625374],"genre_scores_gemma":[0.9660076,0.00003548861,0.0142622,0.01942843,0.00006380258,1.495691e-8,0.000009759975,0.000003403185,0.0001893174],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9705486,"threshold_uncertainty_score":0.9904491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01898857033768569,"score_gpt":0.2556654645070024,"score_spread":0.2366768941693168,"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."}}