{"id":"W4285163143","doi":"10.3997/2214-4609.202210843","title":"Deep Null Space Regularization for Seismic Inverse Problems","year":2022,"lang":"en","type":"article","venue":"83rd EAGE Annual Conference & Exhibition","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Regularization (linguistics); Inverse problem; Deconvolution; Singular value decomposition; Algorithm; Iterative reconstruction; Computer science; Inversion (geology); Synthetic data; Mathematical optimization; Applied mathematics; Mathematics; Artificial intelligence; Mathematical analysis","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005819643,0.0002024994,0.0001979414,0.0002052993,0.0006612586,0.0001213204,0.0002697844,0.00008688799,0.003934643],"category_scores_gemma":[0.00009675853,0.0002110787,0.00009705,0.0003233062,0.0001053521,0.0006283783,0.00004206464,0.0002691074,0.0002400773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003154745,"about_ca_system_score_gemma":0.0001054489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0018873,"about_ca_topic_score_gemma":0.000372151,"domain_scores_codex":[0.9982894,0.0001864105,0.0002925178,0.0004606004,0.0003838392,0.0003872158],"domain_scores_gemma":[0.9991012,0.0000798261,0.0001907423,0.0002899643,0.0002196646,0.0001185674],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005015368,0.0002478519,0.00836469,0.0003002578,0.00006329028,0.00001724783,0.01912373,0.04950901,0.002626633,0.002925144,0.8028262,0.1134944],"study_design_scores_gemma":[0.0005683786,0.0003495995,0.0005918756,0.00004356727,0.00002988518,0.00003603905,0.001686698,0.1775945,0.0009678093,0.02327152,0.7944095,0.0004506946],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2618029,0.0002994434,0.709379,0.01154787,0.001625462,0.002147542,0.001461403,0.00112489,0.01061149],"genre_scores_gemma":[0.973626,0.00004475164,0.007758193,0.004925716,0.0001694566,0.00005214278,0.005061981,0.00001400586,0.008347792],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7118231,"threshold_uncertainty_score":0.9969759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02786091750284027,"score_gpt":0.2312383650756529,"score_spread":0.2033774475728126,"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."}}