{"id":"W4404843711","doi":"10.1016/j.cageo.2024.105788","title":"Efficient proxy for time-lapse seismic forward modeling using a U-net encoder–decoder approach","year":2024,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Shell Brasil; Agência Nacional do Petróleo, Gás Natural e Biocombustíveis; Universidade Estadual de Campinas; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Computer Modelling Group; U.S. Department of Energy","keywords":"Proxy (statistics); Computer science; Encoder; Real-time computing; Geology; Algorithm; Operating system; Machine learning","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006439002,0.0002124346,0.0002232492,0.000265512,0.0001490162,0.0003109434,0.0003245261,0.00007558899,0.000004272178],"category_scores_gemma":[0.00002608581,0.0001886463,0.0001227496,0.000507499,0.00004599071,0.0001430899,0.00005476062,0.0001355939,0.00000957833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007780764,"about_ca_system_score_gemma":0.00005269583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001010788,"about_ca_topic_score_gemma":1.472395e-7,"domain_scores_codex":[0.9985914,0.00002726029,0.0002786964,0.0004067846,0.0002637393,0.0004320828],"domain_scores_gemma":[0.9994644,0.0001579349,0.00001619511,0.0002062184,0.00004368086,0.0001115577],"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":[0.00000194427,0.000009962005,0.000004567901,0.0001935822,0.00001753786,0.000001366028,0.0005714548,0.9955177,0.0005039037,0.00008272031,0.0001574714,0.002937736],"study_design_scores_gemma":[0.0001385025,0.00002264572,0.000002061833,0.00009027527,0.00001649365,0.00001089948,0.00004998269,0.9980243,0.00009848815,0.000262214,0.001037855,0.0002462847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2532395,0.0007030686,0.7439951,0.00002256694,0.001034148,0.0002722429,0.000008201097,0.0005423201,0.0001828702],"genre_scores_gemma":[0.565036,0.000005866169,0.4346108,0.00001865871,0.0001648485,0.00002743668,0.000007272477,0.0000312978,0.0000978305],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3117965,"threshold_uncertainty_score":0.7692774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02817685965093534,"score_gpt":0.2794532450514052,"score_spread":0.2512763854004698,"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."}}