{"id":"W2967634416","doi":"10.1190/segam2019-3214777.1","title":"Log-validated waveform inversion of reflection data with wavelet phase and amplitude updating","year":2019,"lang":"en","type":"article","venue":"","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Wavelet; Amplitude; Inversion (geology); Waveform; Offset (computer science); Maxima and minima; Algorithm; Computer science; Wavelet transform; Reflection (computer programming); Geology; Data set; Set (abstract data type); Seismology; Data mining; Mathematics; Artificial intelligence; Telecommunications; Optics; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001397521,0.0004824449,0.0002548764,0.0004502572,0.0002469497,0.0008939919,0.0008145327,0.0005688312,0.002023696],"category_scores_gemma":[0.006633087,0.0003212858,0.0002867111,0.0005159191,0.0004455851,0.001386809,0.0008317941,0.0009366496,0.0009124063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002739812,"about_ca_system_score_gemma":0.0008723369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002263467,"about_ca_topic_score_gemma":0.004567436,"domain_scores_codex":[0.9996538,0.0001116573,0.00002519617,0.00004855781,0.0001315654,0.00002928183],"domain_scores_gemma":[0.998439,0.000501656,0.0001591584,0.0003979904,0.0004696213,0.00003258987],"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":[0.0004517523,0.0005601469,0.01334091,0.0002097373,0.00009142428,0.0002556175,0.0004152363,0.4793054,0.1501173,0.007143249,0.003218577,0.3448907],"study_design_scores_gemma":[0.00002958321,0.00007363348,0.002075497,0.000009854916,0.000006944289,0.00006161991,0.00004136941,0.9660506,0.02935115,0.001255219,0.001023801,0.00002065115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1475382,0.00003988278,0.847418,0.0001305537,0.00004822174,0.00008538883,0.0002917175,0.002191415,0.00225649],"genre_scores_gemma":[0.5730757,0.00006071378,0.4229926,0.00008143771,0.00001468009,0.0001079774,0.001151477,0.0004871776,0.002028201],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002263467,"threshold_uncertainty_score":0.007390857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03241935478074945,"score_gpt":0.2736628941812064,"score_spread":0.2412435394004569,"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."}}