{"id":"W1981914807","doi":"10.1016/j.cpc.2014.11.002","title":"A computational method for full waveform inversion of crosswell seismic data using automatic differentiation","year":2014,"lang":"en","type":"article","venue":"Computer Physics Communications","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Robustness (evolution); Inversion (geology); Algorithm; Mathematical optimization; Broyden–Fletcher–Goldfarb–Shanno algorithm; Convergence (economics); Applied mathematics; Mathematics; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003965605,0.0001066605,0.0001803732,0.00006972717,0.0003115309,0.00005184573,0.001089532,0.0000387395,0.00002040227],"category_scores_gemma":[0.00001902749,0.0001017816,0.00006125103,0.0001716812,0.0001153324,0.000339869,0.000237705,0.00009932645,0.00001460969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009389933,"about_ca_system_score_gemma":0.00004715532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006103565,"about_ca_topic_score_gemma":0.000009558131,"domain_scores_codex":[0.9990827,0.0001595587,0.0002759565,0.0001990941,0.0001490706,0.00013361],"domain_scores_gemma":[0.9977258,0.000618479,0.0002249098,0.001255484,0.0001315244,0.00004374527],"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":[0.000009382988,0.0001014575,0.001817517,0.0001005302,0.00004816991,3.229999e-8,0.0004269654,0.09209014,0.0001217662,0.002116612,0.00383811,0.8993293],"study_design_scores_gemma":[0.0002450929,0.00005388424,0.001142579,0.0000459951,0.00003346356,0.000001995406,0.00001541117,0.9674647,0.00009120715,0.02880825,0.001986569,0.0001108346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01367047,0.00003133249,0.9850909,0.0004548553,0.0001175887,0.0002077107,0.0001275216,0.00009124099,0.0002084265],"genre_scores_gemma":[0.4119174,0.000006628989,0.5857918,0.0003929973,0.00004810399,9.274299e-7,0.001833937,0.000003655546,0.00000453794],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8992185,"threshold_uncertainty_score":0.4150532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07278111398638974,"score_gpt":0.3122889595925852,"score_spread":0.2395078456061955,"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."}}