{"id":"W2513686208","doi":"10.1190/segam2016-13858769.1","title":"Improved principal component analysis for 3D seismic data simultaneous reconstruction and denoising","year":2016,"lang":"en","type":"article","venue":"","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Key Research and Development Program of China","keywords":"Principal component analysis; Computer science; Dimensionality reduction; Data set; Signal reconstruction; Algorithm; Synthetic data; Noise (video); Set (abstract data type); Noise reduction; Data mining; Pattern recognition (psychology); Artificial intelligence; Signal processing; Image (mathematics)","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":[0.001019319,0.001058879,0.0007095551,0.001357217,0.0004043747,0.0006958224,0.0007892633,0.0007592063,0.002288057],"category_scores_gemma":[0.002526779,0.000479392,0.00115123,0.002262789,0.0005033248,0.001138566,0.001107548,0.001577755,0.001329463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004049391,"about_ca_system_score_gemma":0.0009716756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002740797,"about_ca_topic_score_gemma":0.003801629,"domain_scores_codex":[0.9990751,0.000208825,0.00004545914,0.000134529,0.0004834411,0.00005260007],"domain_scores_gemma":[0.9991756,0.0002533425,0.00006602685,0.0001422506,0.0003367047,0.00002603568],"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":[0.0001782051,0.00008481293,0.001019165,0.0002911706,0.000146481,0.0002045574,0.0002491189,0.2352489,0.09588179,0.02937183,0.008557653,0.6287664],"study_design_scores_gemma":[0.000006350106,0.00001707199,0.0004558371,0.000009587176,0.00001404933,0.00007123072,0.00001526506,0.9793256,0.01054494,0.004210173,0.005307001,0.00002294967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001820676,0.0001078698,0.9972921,0.00005042052,0.00002210472,0.00001380355,0.00004486068,0.0003324652,0.0003157377],"genre_scores_gemma":[0.04017544,0.0004325347,0.9574296,0.00004061306,0.00004840785,0.00009811312,0.0003683052,0.0002021625,0.001204755],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002740797,"threshold_uncertainty_score":0.00765425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02680110848100432,"score_gpt":0.2409390936607875,"score_spread":0.2141379851797832,"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."}}