{"id":"W4385749285","doi":"10.1190/geo2023-0149.1","title":"Automated seismic semantic segmentation using attention U-Net","year":2023,"lang":"en","type":"article","venue":"Geophysics","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Segmentation; Convolutional neural network; Deep learning; Residual; Workflow; Facies; Artificial intelligence; Hyperparameter; Data set; Geology; Algorithm; Database","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001249849,0.00008217125,0.00008144452,0.00009324395,0.0001523754,0.00004549437,0.0000828571,0.00003259008,0.0001079952],"category_scores_gemma":[0.000005818215,0.00007828274,0.0000400497,0.0004589892,0.00003169,0.0002257059,0.000009215576,0.00006106003,0.001478152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005956731,"about_ca_system_score_gemma":0.00002033834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003126998,"about_ca_topic_score_gemma":0.000004699135,"domain_scores_codex":[0.9993251,0.00003649488,0.0001130199,0.000154976,0.00016853,0.0002018781],"domain_scores_gemma":[0.999747,0.00002518514,0.00005470284,0.0001114082,0.00002526991,0.00003638111],"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":[0.00004006773,0.00005847121,0.2760254,0.0002233069,0.000103744,0.00008246444,0.001153641,0.1040874,0.03122053,0.00006977453,0.165941,0.4209942],"study_design_scores_gemma":[0.0000897717,0.00002122933,0.0765261,0.00001819673,0.00001231092,0.000004155628,0.0001425361,0.9202615,0.00102258,0.001122058,0.0006807093,0.0000988649],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959658,0.00001445749,0.001353344,0.000170887,0.0003542711,0.0000943932,0.00002296203,0.001447277,0.0005766521],"genre_scores_gemma":[0.9975989,0.00002081211,0.0009101635,0.0005810791,0.00007745916,5.488043e-7,0.0004616048,0.000004301337,0.0003451172],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8161741,"threshold_uncertainty_score":0.9992993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0207174198803954,"score_gpt":0.2493797760931485,"score_spread":0.2286623562127531,"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."}}