{"id":"W2331844535","doi":"10.3997/2214-4609.201412942","title":"Fast \"Online\" Migration with Compressive Sensing","year":2015,"lang":"en","type":"article","venue":"Proceedings","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Computation; Algorithm; Sparse matrix; Compressed sensing; Norm (philosophy); Inversion (geology); Mathematical optimization; Least-squares function approximation; Seismic migration; Iterative method; Mathematics; Geology","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":[0.0004561826,0.0007254342,0.0006076107,0.0003651863,0.0003909365,0.0006400631,0.001073443,0.0008785392,0.004303452],"category_scores_gemma":[0.001762893,0.0003259541,0.0004225874,0.0004761962,0.0005386822,0.001115728,0.001445812,0.001170174,0.001676127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003048672,"about_ca_system_score_gemma":0.001110919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002842037,"about_ca_topic_score_gemma":0.003788434,"domain_scores_codex":[0.9996048,0.0000712722,0.0000205205,0.00006965206,0.0001982797,0.00003559566],"domain_scores_gemma":[0.9993473,0.0002089369,0.00005527062,0.000190925,0.0001651763,0.0000323277],"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":[0.0003693843,0.000139932,0.0008485397,0.0001838102,0.00007324499,0.0001717593,0.0001742169,0.3248278,0.09595019,0.02559351,0.01179725,0.5398704],"study_design_scores_gemma":[0.00003130521,0.00003205034,0.0001625578,0.000008468927,0.000004812251,0.0000606049,0.00001710108,0.97894,0.01066254,0.005149363,0.004914901,0.00001641159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006013042,0.0000513409,0.9911577,0.0001454949,0.00008019934,0.00003378701,0.00004706137,0.001091303,0.001380028],"genre_scores_gemma":[0.1241784,0.0001001158,0.8716913,0.0001308998,0.0000810671,0.0001298878,0.0002758108,0.0002442945,0.003168179],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004303452,"threshold_uncertainty_score":0.01439649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02395398689126671,"score_gpt":0.2175830946607651,"score_spread":0.1936291077694984,"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."}}