{"id":"W2591496053","doi":"10.1111/1365-2478.12500","title":"Three‐term inversion of prestack seismic data using a weighted <i>l</i><sub>2, 1</sub> mixed norm","year":2017,"lang":"en","type":"article","venue":"Geophysical Prospecting","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Agencia Nacional de Promoción Científica y Tecnológica; Natural Sciences and Engineering Research Council of Canada","keywords":"Algorithm; Inversion (geology); Estimator; Norm (philosophy); Prestack; Sparse matrix; Synthetic data; Seismic inversion; Covariance matrix; Computer science; Mathematics; Mathematical optimization; Geology; Gaussian; Physics; Geometry; Seismology","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":[0.0005298142,0.0006648594,0.0004350224,0.0004243342,0.0002154375,0.0008068279,0.0009695016,0.0007292799,0.001431838],"category_scores_gemma":[0.001430015,0.0003742099,0.0004065398,0.0004821835,0.0005678885,0.0009603112,0.0008530949,0.0008147319,0.0004682551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003395448,"about_ca_system_score_gemma":0.0009455159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002859037,"about_ca_topic_score_gemma":0.004051287,"domain_scores_codex":[0.9997459,0.00005903126,0.00001266646,0.00003391319,0.0001297721,0.00001876203],"domain_scores_gemma":[0.9996476,0.0001426474,0.00005526314,0.00003862454,0.00008972573,0.00002606167],"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":[0.0002348655,0.0001135391,0.001144873,0.0001789301,0.00007856324,0.0001668016,0.0001589091,0.6857181,0.09591039,0.0208893,0.00239995,0.1930057],"study_design_scores_gemma":[0.000003901447,0.00001256609,0.000121902,0.00000340084,0.000002769957,0.00002009909,0.000007612556,0.9926834,0.005574776,0.0008825861,0.0006796732,0.000007325994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01487432,0.00004126122,0.9839646,0.00007189478,0.00001610129,0.00001238239,0.00004216283,0.0002020011,0.000775407],"genre_scores_gemma":[0.2189483,0.0001320244,0.775957,0.0001165747,0.00003231771,0.0001173384,0.0003087354,0.000186736,0.00420099],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002859037,"threshold_uncertainty_score":0.005684853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03819746459904508,"score_gpt":0.2524905686624063,"score_spread":0.2142931040633612,"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."}}