{"id":"W2921927751","doi":"10.2118/195046-ms","title":"Enhancing the Near-Surface Image Using Duplex-Wave Reverse Time Migration","year":2019,"lang":"en","type":"article","venue":"SPE Middle East Oil and Gas Show and Conference","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Wavelet; Seismic migration; Multiple; Cross-correlation; SIGNAL (programming language); Duplex (building); Surface (topology); Geology; Surface wave; Computation; Optics; Computer science; Algorithm; Acoustics; Physics; Geometry; Mathematics; Computer vision; Seismology; 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":[0.0002249518,0.0001291474,0.000138259,0.00001525232,0.0002391087,0.000220798,0.00008269706,0.00004753263,0.00114403],"category_scores_gemma":[0.00001739049,0.00008909711,0.00002724225,0.00007897567,0.0001714796,0.0002696852,0.00002177165,0.0001302503,0.0001735114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002732751,"about_ca_system_score_gemma":0.00004972341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002571694,"about_ca_topic_score_gemma":0.0001639786,"domain_scores_codex":[0.999246,0.00005425111,0.0001167135,0.0002430911,0.0001347627,0.0002052017],"domain_scores_gemma":[0.9996152,0.00003947517,0.00007617979,0.0001465628,0.00004920283,0.00007341379],"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":[0.0002158177,0.00004157907,0.1187965,0.0003198051,0.00006983806,0.00003867846,0.008646704,0.0002995744,0.07605172,0.0003330008,0.005933181,0.7892537],"study_design_scores_gemma":[0.0005292523,0.0002140627,0.008239077,0.0003767709,0.00005502001,0.0001751184,0.003961382,0.9382526,0.009667393,0.001389289,0.03654829,0.0005917464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894666,0.0004968393,0.0002051316,0.002159287,0.0001603589,0.0000491302,0.00001907902,0.00004461074,0.007398933],"genre_scores_gemma":[0.9862404,0.000412187,0.003027838,0.0009336313,0.00005825588,2.984472e-7,0.00002255332,0.000003921731,0.009300858],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.937953,"threshold_uncertainty_score":0.999769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02694406228285208,"score_gpt":0.1972887645711059,"score_spread":0.1703447022882539,"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."}}