{"id":"W2038782842","doi":"10.1190/1.2952571","title":"Bayesian wavefield separation by transform-domain sparsity promotion","year":2008,"lang":"en","type":"article","venue":"Geophysics","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Advanced Education","keywords":"Curvelet; Domain (mathematical analysis); Computer science; Algorithm; Bayesian probability; Clutter; Independence (probability theory); SIGNAL (programming language); Energy (signal processing); Noise (video); Blind signal separation; Amplitude; Phase (matter); Pattern recognition (psychology); Artificial intelligence; Wavelet; Mathematics; Image (mathematics); Statistics; Wavelet transform; Radar; Physics; Optics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009695936,0.0004810883,0.0004840387,0.0004585419,0.0002267649,0.0004976226,0.0006156966,0.000581015,0.001815328],"category_scores_gemma":[0.003123662,0.0003030364,0.0003760768,0.0005031794,0.0006990184,0.001051816,0.001448144,0.0009674449,0.0006890505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003323963,"about_ca_system_score_gemma":0.0009349337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001420764,"about_ca_topic_score_gemma":0.001512098,"domain_scores_codex":[0.9994487,0.0001410232,0.00002715947,0.00009342931,0.0002336879,0.0000560427],"domain_scores_gemma":[0.9985601,0.0007136567,0.0002068995,0.0001570119,0.0002880616,0.00007424823],"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":[0.0004167587,0.0002364555,0.00141212,0.0001098659,0.00006466484,0.00008007538,0.0001482489,0.4805832,0.06425396,0.08064799,0.003572961,0.3684737],"study_design_scores_gemma":[0.00001518263,0.00002826351,0.0001259549,0.000004312464,0.000004287892,0.00002396756,0.000003838923,0.9861814,0.007306819,0.00559814,0.0007000082,0.000007818687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01115694,0.00003244521,0.9877607,0.00007040601,0.000009857743,0.0000130977,0.00001697543,0.0001063834,0.0008332036],"genre_scores_gemma":[0.3974977,0.0001573385,0.5979015,0.0001206184,0.0000780544,0.00009811733,0.0002770681,0.00009910476,0.003770475],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001815328,"threshold_uncertainty_score":0.006072819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01336275891786273,"score_gpt":0.2081860552012308,"score_spread":0.194823296283368,"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."}}